{ "data": { "a": { "slug": "eden-ai", "name": "Eden AI", "vendor": "Eden AI SAS", "vendorUrl": "https://www.edenai.co", "kind": "router", "category": "inference", "summary": "Eden AI is a French AI gateway with one OpenAI-compatible API key for about 1,000 chat and expert models from 80 or more providers, including OCR, speech, translation and image analysis, with an EU-only endpoint and a hosted MCP server.", "url": "https://www.anchorterminal.com/tools/eden-ai", "markdownUrl": "https://www.anchorterminal.com/tools/eden-ai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/eden-ai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/eden-ai.json", "dns": "eden-ai.grade.anchorterminal.com", "transports": [ "http", "streamable-http" ], "remoteUrl": "https://mcp.edenai.run/mcp", "packages": [], "auth": "api-key", "authNotes": "Bearer key created in the dashboard at app.edenai.run after a browser signup. Keys carry budgets, expiry and attribution, can be managed through a key API, and can be limited to chosen models by deny-by-default guardrails. The MCP server takes the same key in the Authorization header.", "pricing": "usage", "pricingNotes": "Provider list prices with no markup plus a 5.5% platform fee applied when buying credits. Prepaid credits through Stripe by card, PayPal or bank transfer, with optional auto-refill. Some models are billed $0 for tests. The Advanced plan is by quote.", "priceSummary": "5.5% fee", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in llms.txt, the OpenAPI spec, the pricing pages or the agent catalogue (checked 2026-11-06). Credits are bought through Stripe.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-06" }, "docsUrl": "https://www.edenai.co/docs", "llmsTxt": "https://www.edenai.co/llms.txt", "openapi": "https://api.edenai.run/v3/docs/openapi.json", "capabilities": [ "inference.router", "inference.llm", "embed.text", "translate.text", "translate.documents", "docs.ocr", "speech.stt", "speech.tts", "image.generate", "guard.moderation" ], "tags": [ "hosted", "router", "openai-compatible", "usage-priced", "prepaid", "mcp", "remote-mcp", "api-key", "llms-txt", "openapi", "eu", "eu-region", "soc2", "iso27001", "batch", "closed-source" ], "lastRelease": "2026-11-05", "graded": true, "anchor": { "graded": true, "score": 59.9, "grade": "C", "agentReady": false, "rank": 672, "ranked": true, "rankOf": 1486, "categoryRank": 22, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 58, "payments": 30, "reliability": 33, "schema": 81, "security": 82, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-06" }, "negative": 0, "verdict": "Eden AI gives one OpenAI-compatible key to about 1,000 models and expert models such as OCR, speech and translation, with an EU-only endpoint, per-key guardrails and no stored content by default. No status page, SLA or changelog was found, and its own pages disagree on the default rate limit.", "bestFor": "Teams that want one key and bill for many LLMs plus OCR, speech, translation and web tools, with EU-only processing.", "strengths": [ "OpenAI-compatible v3 API with an OpenAPI 3.1 spec and a keyless model catalogue with prices and regions", "EU endpoint that refuses models not cleared for European processing with HTTP 451", "Content isn't stored or used for training by default, with opt-in logs and enforceable Zero Data Retention", "Per-key budgets, expiry and deny-by-default guardrails, plus a key management API", "Hosted MCP server exposing OCR, speech, translation, web search and other expert models as tools" ], "weaknesses": [ "No public status page, incident history or SLA found", "The rate-limits page says 10 requests a second and the plans page says 7, raisable to 15", "No public changelog or deprecation policy", "The OpenAPI spec documents few error responses", "Credits are prepaid through Stripe with a 5.5% platform fee on every purchase" ], "agentNotes": [ "Read GET https://api.edenai.run/v3/models at run time for model ids, prices and regions instead of hard-coding them", "Use https://api.eu.edenai.run/v3 when data must stay in the EU, and pick another model on a 451 region_not_allowed", "Set max_tokens to 500 or more for reasoning models, or the answer can come back empty", "Pass a fallbacks list so a provider 429 or error retries inside one request", "Poll check_job for MCP tools marked long-running instead of calling them again" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59.9 } ], "editorialScores": { "ergonomics": 75, "maintenance": 58, "payments": 30, "reliability": 33, "schema": 81, "security": 82, "transparency": 65 }, "provenanceScore": 44, "checked": { "categories": { "ergonomics": "2026-11-06", "maintenance": "2026-11-06", "payments": "2026-11-06", "reliability": "2026-11-06", "schema": "2026-11-06", "security": "2026-11-06", "transparency": "2026-11-06" }, "last": "2026-11-06", "oldest": "2026-11-06", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl https://api.edenai.run/v3/models", "claudeCode": "claude mcp add edenai --transport http https://mcp.edenai.run/mcp --header \"Authorization: Bearer $EDENAI_API_KEY\"", "config": { "mcpServers": { "edenai": { "headers": { "Authorization": "Bearer YOUR_EDENAI_API_KEY" }, "url": "https://mcp.edenai.run/mcp" } } } }, "letme": { "capability": "https://letme.dev/inference.router", "tool": "https://letme.dev/eden-ai" }, "area": "models", "unitPrices": [ { "item": "Platform fee on credit purchases", "unit": "pct", "usd": 5.5, "note": "On top of provider list prices, which carry no markup" } ], "provenance": { "legalEntity": "Eden AI SAS", "domain": "edenai.co", "domainRegistered": "", "endpointOnVendorDomain": false, "terms": "https://www.edenai.co/terms", "privacy": "https://www.edenai.co/privacy", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-11-06", "notes": [ "The terms, last updated 22 October 2026, name Eden AI SAS, share capital €4,992, 19 rue Louis Guérin, 69100 Villeurbanne, France, under French law with courts in Lyon.", "The API and MCP server run on edenai.run, a second domain of the same company, while the site and docs are on edenai.co.", "robots.txt on www.edenai.co allows all agents and carries `Content-Signal: search=yes, ai-input=yes, ai-train=yes`.", "/.well-known/security.txt returns 404. No status page is linked, and status.edenai.co didn't resolve on 6 November 2026.", "RDAP for edenai.co wasn't read: rdap.org rate-limited us and the .co registry server wasn't tried." ], "score": 44 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/eden-ai.json", "live": { "slug": "eden-ai", "probe": { "target": "https://mcp.edenai.run/mcp", "method": "get", "lastAt": "2026-11-07T18:20:22.496540152Z", "lastOk": true, "lastStatus": 405, "lastMs": 123, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 50, "p95ms24h": 129, "samples24h": 200, "samples30d": 200, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 187, "ok": 187 } ] }, "securityTxt": { "url": "https://edenai.co/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T15:59:41.694549385Z" }, "llmsTxt": { "url": "https://www.edenai.co/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:20.299431316Z" }, "pages": [ { "url": "https://www.edenai.co/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-07T14:28:13.706474894Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "21a1da47a2ce" }, { "url": "https://www.edenai.co/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T14:28:15.881335606Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "9cd0db8f0f61" } ], "mcpTools": { "url": "https://mcp.edenai.run/mcp", "checkedAt": "2026-11-07T01:44:01.27972989Z", "status": "ok", "protocol": "2025-11-25", "tools": [ { "name": "list_models", "description": "List available providers/models and their pricing.", "inputSchema": { "additionalProperties": false, "properties": { "tool": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Optional tool name (as returned by tools/list) to filter on." } }, "type": "object" }, "outputSchema": { "additionalProperties": true, "type": "object" } }, { "name": "list_voices", "description": "List the voices a text-to-speech model accepts, for audio_tts's 'voice'.\n\nThe model's default voice comes first.", "inputSchema": { "additionalProperties": false, "properties": { "gender": { "anyOf": [ { "enum": [ "female", "male", "unknown" ], "type": "string" }, { "type": "null" } ], "default": null, "description": "Keep voices of this gender." }, "language": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Keep voices speaking this language: 'fr' matches every region,\n'fr-CA' only that locale. Multilingual voices are always kept." }, "limit": { "default": 50, "description": "Most voices to return (default 50, max 200); 'total' counts every match.", "type": "integer" }, "model": { "description": "The audio_tts model, as 'provider' or 'provider/model' (e.g. 'amazon/neural').", "type": "string" } }, "required": [ "model" ], "type": "object" }, "outputSchema": { "additionalProperties": true, "type": "object" } }, { "name": "upload_file", "description": "Upload a file to Eden AI and get a file ID to use in other tools.\n\nOnly needed for local file content. If you already have a public URL\nfor the file, pass that URL directly to the tool's file parameter\ninstead — no upload needed.", "inputSchema": { "additionalProperties": false, "properties": { "content_base64": { "description": "The file content, base64-encoded.", "type": "string" }, "expires_in_days": { "default": 30, "description": "How long Eden AI keeps the file (1-30, default 30).", "type": "integer" }, "filename": { "description": "Name with extension (used to detect the file type).", "type": "string" } }, "required": [ "content_base64", "filename" ], "type": "object" }, "outputSchema": { "additionalProperties": true, "type": "object" } }, { "name": "check_job", "description": "Get the status/result of an async job started by a long-running tool.", "inputSchema": { "additionalProperties": false, "properties": { "job_id": { "description": "The job id returned when the job was submitted.", "type": "string" } }, "required": [ "job_id" ], "type": "object" }, "outputSchema": { "additionalProperties": true, "type": "object" } }, { "name": "manage_collections", "description": "Create and manage collections: stored sets of items (e.g. faces) that a tool searches against. Tools that take a collection_id: image_face_recognition. Flow: create, then add_item once per file (returns the item ids it indexed), then call the tool with the collection_id; delete when done. Arguments per action: create(model, tool?, name?); list(tool?, model?, page?); get, delete, list_items(collection_id); add_item(collection_id, file); delete_item(collection_id, item_id).", "inputSchema": { "additionalProperties": false, "properties": { "action": { "enum": [ "create", "list", "get", "delete", "add_item", "list_items", "delete_item" ], "type": "string" }, "collection_id": { "description": "From create or list.", "type": "string" }, "file": { "description": "File to add (add_item). Pass a file ID (UUID) from upload_file, or a direct file URL.", "type": "string" }, "item_id": { "description": "Item to delete (delete_item), from add_item or list_items.", "type": "string" }, "model": { "description": "Provider the collection binds to (create; list filter). image_face_recognition: amazon.", "type": "string" }, "name": { "description": "Optional label (create).", "type": "string" }, "page": { "description": "Result page (list).", "minimum": 1, "type": "integer" }, "tool": { "description": "Tool the collection serves (create; list filter). Defaults to image_face_recognition.", "enum": [ "image_face_recognition" ], "type": "string" } }, "required": [ "action" ], "type": "object" } }, { "name": "audio_speech_to_text", "description": "Speech Recognition (or speech to text or voice to text) can recognize and transcribe spoken words (voice), that will be converted to text with or without diarization. It is also possible to filter profanity. The type of files allowed are: mp3, wav, m4a. If convert_to_wav option is set to true, then you can transcribe any type of audio or video files. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Audio file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: audio/*.", "type": "string" }, "language": { "description": "Language code in ISO format (e.g., 'en', 'fr', 'es')", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, assembly, deepgram, deepgram/base, deepgram/enhanced, deepgram/nova-3, gladia, google, gradium, gradium/stt-translate, greenpt, greenpt/green-s, greenpt/green-s-pro, microsoft, mistral, mistral/voxtral-mini-2602, mistral/voxtral-mini-latest, mistral/voxtral-mini-transcribe-3, mistral/voxtral-small-transcribe-3, openai. Call list_models for pricing.", "type": "string" }, "profanity_filter": { "default": false, "description": "Whether to filter profanity and replace inappropriate words with a series of asterisks", "type": "boolean" }, "speakers": { "default": 2, "description": "Number of speakers present in the audio", "minimum": 1, "type": "integer" }, "vocabulary": { "description": "List of words or composed words to be detected by the speech to text engine", "items": { "type": "string" }, "type": "array" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "audio_tts", "description": "Text-to-speech (TTS) is a technology that converts written text into spoken audio, enabling computers and digital devices to \"read\" text aloud. It is also known as speech synthesis, read aloud technology, or computer-generated speech.", "inputSchema": { "additionalProperties": false, "properties": { "audio_format": { "description": "Desired audio format (e.g., 'mp3', 'wav')", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon/neural, amazon/standard, deepgram/aura, deepgram/aura-2, elevenlabs, elevenlabs/eleven_flash_v2, elevenlabs/eleven_flash_v2_5, elevenlabs/eleven_multilingual_v2, elevenlabs/eleven_turbo_v2, elevenlabs/eleven_turbo_v2_5, elevenlabs/eleven_v3, google/casual, google/chirp-hd, google/chirp3-hd, google/gemini-2.5-flash-tts, google/gemini-2.5-pro-tts, google/gemini-3.1-flash-tts-preview, google/neural2, google/news, google/polyglot, google/standard, google/studio, google/wavenet, gradium, microsoft/neural, microsoft/neural-hd, mistral/voxtral-mini-tts-3, openai/gpt-4o-mini-tts, openai/tts-1, openai/tts-1-hd, ovhcloud/nvr-tts-de-de, ovhcloud/nvr-tts-en-us, ovhcloud/nvr-tts-es-es, ovhcloud/nvr-tts-it-it. Call list_models for pricing.", "type": "string" }, "speaking_pitch": { "description": "Increase or decrease the speaking pitch by a percentage from -100% to 100%, 0 is normal.", "maximum": 100, "minimum": -100, "type": "integer" }, "speaking_volume": { "description": "Increase or decrease the audio volume by a percentage from -100% to 100%, 0 is normal.", "maximum": 100, "minimum": -100, "type": "integer" }, "speed": { "description": "Speed multiplier for speech synthesis", "maximum": 4, "minimum": 0.25, "type": "number" }, "text": { "description": "Text to convert to speech", "minLength": 1, "type": "string" }, "voice": { "description": "Voice to synthesize with: an id or name from list_voices(model=...), matched case-insensitively. Omit it to use the model's default voice. An id the list doesn't know (e.g. your own cloned voice) is sent to the provider unchanged.", "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "image_ai_detection", "description": "Determine whether an image was generated by AI or is a real photograph or artwork. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: resemble, winstonai. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_anonymization", "description": "Image Anonymization API, also known as image de-identification or image de-personalization, refers to the process of automatically removing personal or sensitive information from images. The main objective is to protect the privacy and security of individuals who may be depicted in images shared online or used for research purposes. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: api4ai. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_background_removal", "description": "Background removal is a digital image processing technique designed to seamlessly eliminate the backdrop of a photo, leaving behind only the main subject. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: api4ai, clipdrop, photoroom, picsart, sentisight, stabilityai. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_deepfake_detection", "description": "Detect whether a face in a real photo has been swapped or manipulated (face-swap deepfakes). For fully AI-generated images, use AI Image Detection instead. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: resemble, sightengine. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_explicit_content", "description": "Explicit Content Detection detects adult only content in images, that is generally inappropriate for people under the age of 18 and includes nudity, sexual activity, pornography, violence, gore content, etc. (type of files allowed: jpg, jpeg, png, tiff) File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, google, microsoft, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_face_compare", "description": "Compare two faces and decide whether they are from the same person. The API expects 2 images, reference and query, where the former is the ground truth (e.g. user's official ID) and the latter is the validation image (e.g. a selfie). File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file1": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "file2": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, base64, facepp. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file1", "file2" ], "type": "object" } }, { "name": "image_face_detection", "description": "Face Detection is a computer technology being used in a variety of applications that identifies human faces in digital images with different attributes like landmarks, sentiments and physical attire (type of file allowed: jpg, jpeg, png, tiff) File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, api4ai, google. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_face_recognition", "description": "Recognizing a face in an image is a more advanced feature than just detecting the face in the image. It allows the user to search for the detected face from a database of existing images and extract the faces that are most similar to the face displayed in the query image. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "collection_id": { "description": "Id of one of your collections. Make one with manage_collections (action='create', then add_item for each file to search against); action='list' shows the ones you have. 'model' must be the collection's provider.", "type": "string" }, "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "collection_id", "file" ], "type": "object" } }, { "name": "image_generation", "description": "Image Generation is an advanced feature that generates compelling images based on a given text prompt. It can easily produce high-quality and original images in a matter of seconds, without the need for specialized design skills or software File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "mask": { "description": "Optional inpainting mask. Must be paired with at least one reference image (otherwise the request returns 422). Support varies by model; models that do not accept a mask surface the error as status=\"fail\" with a provider message in the response body. Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: bytedance, bytedance/seedream-4-0-250828, bytedance/seedream-4-5-251128, bytedance/seedream-5-0-260128, google, google/gemini-2.5-flash-image, google/gemini-3-pro-image-preview, infomaniak/flux, ionos/black-forest-labs/FLUX.1-schnell, ionos/black-forest-labs/FLUX.2-klein-4B, leonardo, leonardo/AlbedoBase XL, leonardo/Leonardo Anime XL, leonardo/Leonardo Diffusion XL, leonardo/Leonardo Kino XL, leonardo/Leonardo Lightning XL, leonardo/Leonardo Phoenix, leonardo/Leonardo Vision XL, leonardo/SDXL 0.9, minimax, minimax/image-01, openai, openai/gpt-image-1, openai/gpt-image-1-mini, openai/gpt-image-1.5, openai/gpt-image-2, ovhcloud/stable-diffusion-xl-base-v10, pruna/p-image, pruna/p-image-edit, pruna/p-image-ideogram, pruna/p-image-ideogram-low, pruna/p-image-ideogram-medium, pruna/p-image-ideogram-very-high, pruna/p-image-ideogram-very-low, replicate, replicate/anime-style, replicate/classic, replicate/vintedois-diffusion, stabilityai, stabilityai/stable-diffusion-xl-1024-v1-0. Call list_models for pricing.", "type": "string" }, "num_images": { "default": 1, "description": "Number of images to generate", "maximum": 10, "minimum": 1, "type": "integer" }, "reference_images": { "description": "Optional reference images. Plain file IDs/URLs are treated as ReferenceImage with kind=None. Support and per-model limits vary by model; models that do not accept reference images surface the error as status=\"fail\" with a provider message in the response body.", "items": {}, "maxItems": 64, "type": "array" }, "resolution": { "description": "Image resolution as a named size (e.g. '1024x1024'). Optional: providers may also accept native sizing knobs (width/height, aspect_ratio, image_size tier, etc.) in the top-level `provider_params` request field, next to `input` rather than inside it, in which case `provider_params` takes precedence.", "pattern": "^\\d+x\\d+$", "type": "string" }, "text": { "description": "Text prompt for image generation", "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "image_logo_detection", "description": "Logo Detection is a powerful technology that enables the automatic identification and recognition of popular logos within an image. It provides accurate and efficient detection of known (and less known) brand logos. It's a valuable tool for a variety of industries, including advertising, retail, and e-commerce. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: api4ai, api4ai/v1, api4ai/v2, google, microsoft, openai, openai/gpt-4-turbo, openai/gpt-4o. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "image_object_detection", "description": "Object Detection is a computer vision technique that allows users to identify and locate (with bounding boxes) objects in an image. The detected objects can be animals, people, electronics, vehicles etc. (type of file allowed: jpg, jpeg, png, tiff) File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, api4ai, google, microsoft, sentisight. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr", "description": "Optical Character Recognition or Reader (OCR) is the electronic or mechanical conversion of images of typed, handwritten or printed text into machine-encoded text. The image can be a scanned document or a photo of a license plate, a road sign or anything else that contains text (type of files allowed: jpg, jpeg, png, tiff) . File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "PDF or image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, image/*.", "type": "string" }, "language": { "description": "Document language code. Most providers detect the language themselves and ignore this field; providers that cannot detect it reject requests that omit it. Check the provider's supported languages before relying on it.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, api4ai, google, ionos, ionos/lightonai/LightOnOCR-2-1B, microsoft, mistral, mistral/mistral-ocr-2512, mistral/mistral-ocr-3, mistral/mistral-ocr-3-0, mistral/mistral-ocr-4, mistral/mistral-ocr-4-0, mistral/mistral-ocr-4-1, mistral/mistral-ocr-latest, sentisight. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_async", "description": "OCR or Optical Character Recognition is also referred to as text recognition or text extraction. It allows users to extract text data from PDFs with multiple pages. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "PDF or image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, microsoft, mistral, mistral/mistral-ocr-2512, mistral/mistral-ocr-3, mistral/mistral-ocr-3-0, mistral/mistral-ocr-4, mistral/mistral-ocr-4-0, mistral/mistral-ocr-4-1, mistral/mistral-ocr-latest. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_financial_parser", "description": "Financial Parser API is a powerful and versatile tool designed to streamline your financial data extraction process. This cutting-edge API combines Optical Character Recognition (OCR) technology with advanced parsing capabilities to seamlessly extract and organize relevant information from invoices and receipts. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "document_type": { "default": "invoice", "description": "Specify the type of your document. Can be set to 'auto-detect' for automatic detection if the provider supports it.", "enum": [ "auto-detect", "invoice", "receipt" ], "type": "string" }, "file": { "description": "File ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*, application/*, text/*.", "type": "string" }, "language": { "description": "Document language code. Most providers detect the language themselves and ignore this field; providers that cannot detect it reject requests that omit it. Check the provider's supported languages before relying on it.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: affinda, amazon, base64, eagledoc, extracta, google, klippa, microsoft, mindee, openai, openai/gpt-4o, tabscanner, veryfi. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_identity_parser", "description": "ID Document parsing technology allows users to automatically extract information from an ID Document such as passport, ID card, driving license and more. This API is ideal for developers looking to create applications that require identity verification or access control. It extracts information like name, date of birth, nationality, age and more. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "PDF or image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: affinda, amazon, base64, klippa, microsoft, mindee, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_resume_parser", "description": "Resume Parser enables users to extract various information from resumes (curriculum vitae, CV) that could be in a variety of formats and returns structured data (name, job list, education, skills) to automate their processing. (type of files allowed: pdf, jpg, jpeg, png) File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "File ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/*, application/*, text/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: affinda, extracta, klippa, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "ocr_tables", "description": "OCR Table (Optical Character Recognition for tabular documents) allows users to analyze documents containing tables and returns a structured representation of the detected tables in the form of a Json object that can be converted to a CSV. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "PDF or image file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, image/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, google, microsoft. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "text_ai_detection", "description": "The AI Detection API is a tool designed to determine whether a given text was generated by an artificial intelligence chatbot, or a Language Model (LLM).", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: sapling, winstonai. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to analyze for AI detection, at least 300 characters long. Shorter texts carry too little signal to be scored reliably.", "minLength": 300, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_anonymization", "description": "The personally identifiable information (PII) detection feature can identify and redact sensitive information in unstructured text. For example: phone numbers, email addresses, and forms of identification. Removing these personally identifiable information helps with the Anonymization of a text and privacy protection.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, limina, microsoft. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to detect personal information in", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_moderation", "description": "Text moderation scans text for offensive, sexually explicit or suggestive content, it also checks if there is any content of self-harm, violence, racist or hate speech.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: google, microsoft, openai, openai/text-moderation-007, openai/text-moderation-latest, openai/text-moderation-stable, ovhcloud/Qwen3Guard-Gen-0.6B, ovhcloud/Qwen3Guard-Gen-8B. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to moderate", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_named_entity_recognition", "description": "Named Entity Recognition (also called entity identification or extraction) is an information extraction technique that automatically identifies named entities in a text and classifies them into predefined categories such as dates, persons names, company names, dates, products etc.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: amazon, microsoft, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to analyze for named entities", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_plagia_detection", "description": "The Plagiarism Detection API is a tool designed to scan a content string for plagiarism using a plagiarism detection module", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: winstonai. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text content on which plagiarism detection will be run", "minLength": 1, "type": "string" }, "title": { "description": "Content title", "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_spell_check", "description": "Spell Check analyzes the inputted text by comparing it to a dictionary of correctly spelled words to identify and highlight any spelling errors or grammatical mistakes.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: prowritingaid, sapling. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to be spell-checked", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "text_topic_extraction", "description": "Topic analysis is a Natural Language Processing (NLP) technique that allows us to automatically extract meaning from text by identifying recurrent themes or topics.", "inputSchema": { "additionalProperties": false, "properties": { "language": { "description": "ISO 639-1 language code", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: google, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" }, "text": { "description": "Text to analyze for topics", "minLength": 1, "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "translation_automatic_translation", "description": "Automatic translation or machine translation (MT) is the process of automatically translating a text from one language to another language. The aim of it is to enable people to communicate and access information in different languages, without the need for a human translator.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: amazon, deepl, google, microsoft, modernmt, openai, openai/gpt-4o. Call list_models for pricing.", "type": "string" }, "source_language": { "description": "Source language code", "minLength": 1, "type": "string" }, "target_language": { "description": "Target language code", "minLength": 1, "type": "string" }, "text": { "description": "Text to translate", "minLength": 1, "type": "string" } }, "required": [ "model", "text", "target_language" ], "type": "object" } }, { "name": "translation_document_translation", "description": "Document Translation API translates whole documents in supported languages and various file formats (like pdf or doc) while keeping their structure intact. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Document file ID from /v3/upload or direct file URL (PDF, DOCX, PPTX) Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: application/pdf, application/vnd.openxmlformats-officedocument.wordprocessingml.document, application/vnd.openxmlformats-officedocument.presentationml.presentation.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: deepl, google. Call list_models for pricing.", "type": "string" }, "source_language": { "description": "Source language code", "minLength": 1, "type": "string" }, "target_language": { "description": "Target language code", "minLength": 1, "type": "string" } }, "required": [ "model", "file", "target_language" ], "type": "object" } }, { "name": "video_deepfake_detection", "description": "Detect deepfake and synthetically manipulated video. Returns an overall deepfake score and prediction, along with per-frame scores when the provider supplies them. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "file": { "description": "Video file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: video/*.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: resemble. Call list_models for pricing.", "type": "string" } }, "required": [ "model", "file" ], "type": "object" } }, { "name": "video_generation", "description": "The Video Generation API enables easy creation of high-quality videos from text,and images, perfect for marketing, education, and more—all through one integration. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'. File parameters accept a file ID from upload_file or a direct file URL — if you already have a URL, pass it as-is (no upload needed).", "inputSchema": { "additionalProperties": false, "properties": { "dimension": { "default": "1280x720", "description": "Video resolution in 'widthxheight' format (e.g., '1280x720')", "pattern": "^\\d+x\\d+$", "type": "string" }, "duration": { "description": "Length of the generated video in seconds. Omit to use the model's default duration.", "maximum": 120, "minimum": 1, "type": "integer" }, "file": { "description": "Initial keyframe image (JPEG or PNG) file ID from /v3/upload or direct file URL Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: image/jpeg, image/png.", "type": "string" }, "model": { "description": "Which provider/model to run, exactly one of: bytedance, bytedance/dreamina-seedance-2-0-260128, bytedance/dreamina-seedance-2-0-fast-260128, bytedance/dreamina-seedance-2-0-mini-260615, bytedance/dreamina-seedance-2-5-260628, bytedance/seedance-1-0-pro-250528, bytedance/seedance-1-0-pro-fast-251015, bytedance/seedance-1-5-pro-251215, minimax, minimax/MiniMax-Hailuo-02, minimax/MiniMax-Hailuo-2.3, minimax/S2V-01, minimax/T2V/I2V-01-Director, openai, openai/sora-2, openai/sora-2-pro, pixverse, pixverse/c1, pixverse/v5, pixverse/v5.5, pixverse/v6, pruna, pruna/p-video, pruna/p-video-2, pruna/p-video-2-pro, pruna/p-video-2-pro-cost, pruna/p-video-2-pro-quality, pruna/p-video-animate, pruna/p-video-avatar, pruna/p-video-edit, xai/grok-imagine-video-1.5, xai/grok-imagine-video-1.5-lite. Call list_models for pricing.", "type": "string" }, "seed": { "description": "Random seed for generation reproducibility (0 to 2,147,483,646). Omit for varied output.", "maximum": 2147483646, "minimum": 0, "type": "integer" }, "text": { "description": "Text prompt describing the content and style of the video to generate", "minLength": 1, "type": "string" }, "video": { "description": "Source video to edit, for models that edit a video (their `requires_video` is true in /v3/info). The output keeps the source's length, which is what is billed, so `duration` and `dimension` do not apply to them. Other models refuse it. Pass a file ID (UUID) from upload_file, or a direct file URL. Accepted types: video/*.", "type": "string" } }, "required": [ "model", "text" ], "type": "object" } }, { "name": "web_batch_scrape", "description": "Scrape many URLs in one request and return each page's content. Runs as an async job. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl. Call list_models for pricing.", "type": "string" }, "urls": { "description": "Explicit list of URLs to scrape (async job)", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "model", "urls" ], "type": "object" } }, { "name": "web_crawl", "description": "Crawl a site from a starting URL and return the content of its pages. Runs as an async job. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl, tavily. Call list_models for pricing.", "type": "string" }, "url": { "description": "Root URL to crawl (async job)", "minLength": 1, "type": "string" } }, "required": [ "model", "url" ], "type": "object" } }, { "name": "web_map", "description": "Discover and return the list of URLs on a site (a sitemap-style link map).", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl, tavily. Call list_models for pricing.", "type": "string" }, "url": { "description": "Root URL to map (discover its links)", "minLength": 1, "type": "string" } }, "required": [ "model", "url" ], "type": "object" } }, { "name": "web_research", "description": "Run deep, multi-step web research on a query and return a synthesized answer with sources. Runs as an async job. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl, linkup, tavily. Call list_models for pricing.", "type": "string" }, "prompt": { "description": "Natural-language research task for the agent", "minLength": 1, "type": "string" }, "reasoning_depth": { "default": "L", "description": "Research reasoning-effort tier (billed per tier; priced pre-call)", "enum": [ "S", "M", "L", "XL" ], "type": "string" }, "schema": { "description": "JSON schema describing the structured output", "type": "object" } }, "required": [ "model", "prompt" ], "type": "object" } }, { "name": "web_scraping", "description": "Fetch a single URL and return its clean content as markdown, text, or HTML, with optional JavaScript rendering.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: deepseek, firecrawl, linkup, tavily. Call list_models for pricing.", "type": "string" }, "url": { "description": "URL of the web page to scrape", "minLength": 1, "type": "string" } }, "required": [ "model", "url" ], "type": "object" } }, { "name": "web_search", "description": "Search the web for a query and return ranked results (title, URL, and a snippet or the page content).", "inputSchema": { "additionalProperties": false, "properties": { "depth": { "default": "standard", "description": "Search depth tier. `deep` is refused with a 400 on deepseek, perplexityai and zai, which run one tier only.", "enum": [ "standard", "deep" ], "type": "string" }, "max_results": { "description": "Max number of search results to return", "minimum": 1, "type": "integer" }, "model": { "description": "Which provider/model to run, exactly one of: deepseek, firecrawl, linkup, perplexityai, tavily, zai. Call list_models for pricing.", "type": "string" }, "query": { "description": "The web search query", "minLength": 1, "type": "string" } }, "required": [ "model", "query" ], "type": "object" } }, { "name": "web_structured_extraction", "description": "Extract structured data from one or more web pages against a schema or prompt. Runs as an async job. Long-running: returns a job immediately; poll it with check_job(job_id) every few seconds until status is 'success' or 'fail'.", "inputSchema": { "additionalProperties": false, "properties": { "model": { "description": "Which provider/model to run, exactly one of: firecrawl. Call list_models for pricing.", "type": "string" }, "prompt": { "description": "Natural-language description of what to extract", "type": "string" }, "schema": { "description": "JSON schema describing the structured output", "type": "object" }, "urls": { "description": "URLs to extract structured data from (async job)", "items": { "type": "string" }, "minItems": 1, "type": "array" } }, "required": [ "model", "urls" ], "type": "object" } } ], "schemaTokens": 10725, "changedAt": "2026-11-07T01:44:01.27972989Z", "check": { "checker": "anchor-check/1.0", "totalTokens": 10725, "counts": { "error": 0, "note": 1, "warn": 49 }, "findings": [ { "rule": "TC11", "severity": "warn", "tool": "manage_collections", "message": "1 parameter without a description: action", "fix": "Describe each one: format, units, an example, and what happens when it's left out." }, { "rule": "TC12", "severity": "warn", "tool": "image_generation", "message": "no type for reference_images[]", "fix": "Give every parameter a type (or an enum)." }, { "rule": "TC13", "severity": "warn", "tool": "web_research", "message": "schema (object with no properties)", "fix": "Declare the properties (or additionalProperties with a schema) and the array's items." }, { "rule": "TC13", "severity": "warn", "tool": "web_structured_extraction", "message": "schema (object with no properties)", "fix": "Declare the properties (or additionalProperties with a schema) and the array's items." }, { "rule": "TC14", "severity": "warn", "tool": "image_face_recognition", "message": "allowed values are in the description, not an enum: collection_id", "fix": "Move them into enum." }, { "rule": "TC14", "severity": "warn", "tool": "image_generation", "message": "allowed values are in the description, not an enum: resolution", "fix": "Move them into enum." }, { "rule": "TC16", "severity": "warn", "tool": "audio_speech_to_text", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "audio_tts", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "check_job", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"check\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "image_ai_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_anonymization", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_background_removal", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_deepfake_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_explicit_content", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_face_compare", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_face_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_face_recognition", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_generation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_logo_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "image_object_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "list_models", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"list\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "list_voices", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"list\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "manage_collections", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_async", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_financial_parser", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_identity_parser", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_resume_parser", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "ocr_tables", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_ai_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_anonymization", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_moderation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_named_entity_recognition", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_plagia_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "text_spell_check", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"check\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "text_topic_extraction", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "translation_automatic_translation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "translation_document_translation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "upload_file", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "video_deepfake_detection", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "video_generation", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_batch_scrape", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_crawl", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_map", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_research", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_scraping", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC16", "severity": "warn", "tool": "web_search", "message": "no readOnlyHint or destructiveHint", "fix": "Its name starts with \"search\"; if it only reads, set readOnlyHint: true so harnesses can run it without asking." }, { "rule": "TC16", "severity": "warn", "tool": "web_structured_extraction", "message": "no readOnlyHint or destructiveHint", "fix": "Set readOnlyHint: true if it only reads; otherwise set destructiveHint and idempotentHint." }, { "rule": "TC23", "severity": "warn", "message": "42 tools", "fix": "Offer a smaller default toolset and let clients opt into the rest." }, { "rule": "TC24", "severity": "note", "message": "38 of 42 tools have no outputSchema", "fix": "Declare outputSchema for tools that return structured data, and return structuredContent that matches it." } ] } }, "updatedAt": "2026-11-07T18:20:22.496540152Z" } }, "answer": "Gemini Developer API scores 62.2 (B) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on security \u0026 auth.", "b": { "slug": "gemini-api", "name": "Gemini Developer API", "vendor": "Google", "vendorUrl": "https://ai.google.dev", "kind": "model", "category": "inference", "summary": "Google's API for Gemini models, including content generation and agent interactions.", "url": "https://www.anchorterminal.com/tools/gemini-api", "markdownUrl": "https://www.anchorterminal.com/tools/gemini-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-api.json", "dns": "gemini-api.grade.anchorterminal.com", "repo": "https://github.com/googleapis/python-genai", "license": "Apache-2.0 (SDKs)", "transports": [ "http" ], "remoteUrl": "https://generativelanguage.googleapis.com/v1beta", "packages": [ { "registry": "pypi", "name": "google-genai", "main": true }, { "registry": "npm", "name": "@google/genai", "main": true } ], "auth": "api-key", "authNotes": "`x-goog-api-key` header with a key from AI Studio. A Google account is needed to make one.", "pricing": "freemium", "pricingNotes": "Free tier on 3.8 Flash, 3.6 Flash and the Flash-Lite models, not on Gemini 4 Argon or 3.1 Pro preview, and free-tier content is used to improve Google products. Gemini 4 Argon (`gemini-4-argon`) costs $2/$10 per million tokens through 2026-11-30, then $4/$20, with implicit caching only. 3.1 Pro preview costs $2/$12 per million tokens, $4/$18 over 200K. 3.8 Flash is $0.75/$3.75 until 2026-12-31, then $1.50/$7.50. Cached input 0.1x. Nano Banana 2.1 (`gemini-nano-banana-2.1`, images) is paid tier only at $1.50 input and $30 per million image output tokens, about $0.034 a 1K image, $0.050 at 2K and $0.113 at 4K, half that in batch. Search grounding 5,000 free a month, then $14 per 1,000. Batch half price (https://ai.google.dev/gemini-api/docs/pricing).", "priceSummary": "from $0.25 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No machine payment. The free tier needs a Google account and paid use needs billing on a Cloud project.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 3992, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-09-26" }, "docsUrl": "https://ai.google.dev/gemini-api/docs", "rateLimitsUrl": "https://ai.google.dev/gemini-api/docs/rate-limits", "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "free-tier", "no-card", "llms-txt" ], "lastRelease": "2026-10-30", "graded": true, "anchor": { "graded": true, "score": 62.2, "grade": "B", "agentReady": false, "rank": 566, "ranked": true, "rankOf": 1486, "categoryRank": 21, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 93, "maintenance": 83, "payments": 40, "reliability": 45, "schema": 85, "security": 50, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-01" }, "negative": -3, "negativeNotes": [ "2026-10-08. Google deprecated the generally available `gemini-3.7-flash` and `gemini-3.5-flash` and routed API requests for them to `gemini-3.8-flash` and `gemini-3.6-flash` the same day, announced in that day's changelog. Callers pinned to those names got a different model without notice, the reading DeepSeek API takes for its 10 September routing, so 3 points (https://ai.google.dev/gemini-api/docs/changelog, https://ai.google.dev/gemini-api/docs/deprecations)." ], "verdict": "Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string.", "bestFor": "Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.", "strengths": [ "Free tier on 3.8 Flash and Flash-Lite with no billing account", "Gemini 4 Argon went GA on 13 October 2026 with a 1,048,576-token shared window and long decode up to 1M tokens", "Backoff guidance with jitter, and the Python SDK retries transient errors four times", "New keys are bound to a service account and restricted to the Gemini API, and unrestricted standard keys are rejected", "A keyless Discovery document types the v1beta GenerateContent surface, and function calling can be forced to named tools" ], "weaknesses": [ "Free-tier prompts and responses improve Google products and may be read by human reviewers", "The API reads an OAuth access token from the `access_token` query parameter", "No SLA and no zero-retention route found for the Developer API", "Gemini 4 Argon isn't on the free tier and doesn't support explicit context caching", "Shutdown dates are 'earliest possible' with no stated minimum notice, and one preview agent got 15 days" ], "agentNotes": [ "Never send customer data through a free-tier key. Link billing first", "Send keys in the `x-goog-api-key` header, never as `key` or `access_token` in the URL", "Back off exponentially on 429 RESOURCE_EXHAUSTED and 503, and don't retry 400, 402 or 403", "Remove temperature, top_p and top_k. Gemini 4 and 3.6+ ignore them and a future release returns 400", "On `gemini-4-argon`, set max_output_tokens to at least 32,768 and client timeouts to 15 minutes, or use background execution" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.2 } ], "editorialScores": { "ergonomics": 93, "maintenance": 83, "payments": 40, "reliability": 45, "schema": 85, "security": 50, "transparency": 55 }, "provenanceScore": 89, "checked": { "categories": { "ergonomics": "2026-11-01", "maintenance": "2026-11-01", "payments": "2026-11-01", "reliability": "2026-11-01", "schema": "2026-11-01", "security": "2026-11-01", "transparency": "2026-11-01" }, "last": "2026-11-01", "lastMoved": "2026-11-01", "oldest": "2026-11-01", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install google-genai # or: npm i @google/genai", "http": "curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-3.8-flash:generateContent\" \\\n -H \"x-goog-api-key: $GEMINI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"contents\":[{\"parts\":[{\"text\":\"hello\"}]}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/gemini-api" }, "sameCompany": [ "vertex-ai", "gemini-embedding", "vertex-ai-tuning", "cloud-run-gpus", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "google-cloud-tts", "gemini-live", "google-adk", "google-memory-bank", "google-secret-manager", "google-cloud-document-ai", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-cloud-storage", "google-drive-api", "gemini-enterprise", "gemini-cli", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api", "google-people-api" ], "sameModel": [ "gemma-4-26b-a4b-it", "gemma-4-31b-it" ], "area": "models", "unitPrices": [ { "item": "Search grounding over 5,000 a month", "unit": "1k-requests", "usd": 14 }, { "item": "Nano Banana 2.1 image, 1K", "unit": "image", "usd": 0.0336, "note": "1,120 output tokens at $30 per million. Paid tier only, half price in batch" }, { "item": "Nano Banana 2.1 image, 2K", "unit": "image", "usd": 0.0504, "note": "1,680 output tokens" }, { "item": "Nano Banana 2.1 image, 4K", "unit": "image", "usd": 0.113, "note": "3,780 output tokens" } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://ai.google.dev/gemini-api/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://aistudio.google.com/status", "changelog": "https://ai.google.dev/gemini-api/docs/changelog", "securityTxt": "valid", "checked": "2026-11-01", "score": 89 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-api.json", "live": { "slug": "gemini-api", "probe": { "target": "https://generativelanguage.googleapis.com/v1beta", "method": "get", "lastAt": "2026-11-07T18:20:26.848283661Z", "lastOk": true, "lastStatus": 404, "lastMs": 120, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 37, "p95ms24h": 120, "samples24h": 239, "samples30d": 7910, "days": [ { "date": "2026-10-07", "probes": 272, "ok": 272 }, { "date": "2026-10-08", "probes": 268, "ok": 268 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 249 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 187, "ok": 187 } ] }, "vendorStatus": { "page": "https://aistudio.google.com/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-07T04:47:49.73911967Z" }, "versions": [ { "registry": "github", "name": "googleapis/python-genai", "version": "v3.0.0", "released": "2026-11-06", "seenAt": "2026-11-06T23:27:37.766484844Z" }, { "registry": "npm", "name": "@google/genai", "version": "3.0.0", "seenAt": "2026-11-06T23:27:37.49484945Z" }, { "registry": "pypi", "name": "google-genai", "version": "3.0.0", "released": "2026-11-06", "seenAt": "2026-11-06T23:27:37.329000252Z" } ], "githubStars": 4057, "npmWeekly": 31989360, "pypiWeekly": 38431674, "securityTxt": { "url": "https://google.com/.well-known/security.txt", "state": "valid", "expires": "2030-04-01T00:00:00z", "checkedAt": "2026-11-07T16:01:22.068349931Z" }, "llmsTxt": { "url": "https://ai.google.dev/gemini-api/docs/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-07T15:32:33.34914217Z" }, "domain": { "domain": "google.com", "registered": "1997-09-15", "source": "https://rdap.verisign.com/com/v1/domain/google.com", "checkedAt": "2026-11-01T14:17:18.719851913Z" }, "pages": [ { "url": "https://ai.google.dev/gemini-api/docs/changelog", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T13:58:55.08107274Z", "changedAt": "2026-11-07T13:58:55.08107274Z", "fingerprint": "5bad8c4d68e3" }, { "url": "https://ai.google.dev/gemini-api/docs/deprecations", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T13:58:57.400152684Z", "changedAt": "2026-11-07T13:58:57.400152684Z", "fingerprint": "f0958caa84a5" }, { "url": "https://ai.google.dev/gemini-api/docs/pricing", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-07T13:58:59.844674867Z", "changedAt": "2026-11-06T13:21:47.729671724Z", "fingerprint": "d3b5522104e1" }, { "url": "https://ai.google.dev/gemini-api/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-07T13:59:01.357633499Z", "changedAt": "2026-11-03T07:41:20.562939041Z", "fingerprint": "07b98470ed02" } ], "models": { "gemini-3.1-flash-lite": { "openrouterId": "google/gemini-3.1-flash-lite", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gemini-3.1-pro-preview": { "openrouterId": "google/gemini-3.1-pro-preview", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gemini-3.5-flash-lite": { "openrouterId": "google/gemini-3.5-flash-lite", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gemini-3.8-flash": { "openrouterId": "google/gemini-3.8-flash", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 65536, "checkedAt": "2026-11-06T23:49:25.225778715Z" }, "gemini-4-argon": { "openrouterId": "google/gemini-4-argon", "tools": true, "structuredOutputs": true, "jsonMode": true, "reasoning": true, "input": [ "audio", "file", "image", "text", "video" ], "promptCaching": true, "contextTokens": 1048576, "maxOutput": 943718, "checkedAt": "2026-11-06T23:49:25.225778715Z" } }, "github": { "repo": "googleapis/python-genai", "checkedAt": "2026-11-02T08:52:22.518653754Z", "stars": 4054, "archived": false, "issuesEnabled": true, "pushedAt": "2026-10-31T00:34:25Z", "openIssues": 227, "openPRs": 143, "issuesOpened90d": 78, "issuesClosed90d": 38, "prsMerged90d": 216, "ci": { "branch": "main", "runs": 50, "success": 50, "failure": 0, "other": 0, "latest": "success", "latestAt": "2026-10-30T23:58:40Z" }, "replies": { "sampled": 25, "unanswered": 16, "medianDays": 7.5 } }, "updatedAt": "2026-11-07T18:20:26.848283661Z" } }, "edges": [ { "by": 32, "category": "security", "leads": "eden-ai", "name": "Security \u0026 auth", "reasons": { "a": "Router, read on the model lines. Bearer keys with per-key budgets, expiry and attribution, a key management API, and deny-by-default guardrails that limit models per member, role or key (25). The data retention page says request and response content isn't stored and data isn't used to train models by default (18). Content logging is opt-in, async results are deleted after 7 days, and an organisation can enforce Zero Data Retention and region rules; the EU endpoint refuses non-EU models with 451 (13). Cost and provider per response, and a monitoring dashboard by key (12). The security page says Eden AI is SOC 2 and ISO 27001 certified and links a trust centre; /.well-known/security.txt returns 404 and no disclosure policy was found (14). 25+18+13+12+14 = 82.", "b": "Model reading. Keys live in a Cloud project. Since 28 May 2026 new keys are authorisation keys bound to a service account and restricted to the Gemini API, keys can be limited by IP or origin, unrestricted standard keys are rejected, and the docs give a leak checklist, per the API key page re-read on 1 November. No permission scopes, so 25 of 30, less 10 because the API reads a token from the URL. The Discovery document (revision 20261101) lists `access_token`, `oauth_token` and `key` as query parameters, and on 1 November a generateContent request with no credential answered 403 'Method doesn't allow unregistered callers' while one with a made-up `?access_token=` answered 401 'Request had invalid authentication credentials' (ACCESS_TOKEN_TYPE_UNSUPPORTED), the deduction Google APIs took on 17 October and 1 November and Gemini Embedding and Gemini Live carry (15 of 30). Terms (effective 23 March 2026, re-read 1 November) say free-tier prompts and responses are used to improve Google products and human reviewers may read them, paid tier isn't used (10 of 20). Abuse monitoring on paid services is kept 'a limited period' with no number, grounding data 30 days, and since 26 October new paid projects store Generate Content requests by default unless `store=false`. No zero-retention route on this API was found (5 of 15). Request logs in AI Studio for billed projects (15). security.txt on google.com valid to 2030-04-01 with a vulnerability reward programme, re-read on 1 November. No certification naming the Developer API found, the reading Gemini Embedding takes (5 of 20)." }, "scores": { "a": 82, "b": 50 } }, { "by": 25, "category": "maintenance", "leads": "gemini-api", "name": "Maintenance \u0026 community", "reasons": { "a": "Closed service, read on the model lines. The docs repository changed on 5 and 6 November 2026, including retiring Nova Reel and Veo from video generation, and the terms were updated 22 October (30). No public changelog, so dated API changes in the last 90 days come only from docs commits (12 of 20). Chat support within 48 working hours, no public changelog (8). No SDK of its own, and the MCP server isn't in the official MCP registry (5). Nothing to judge package health by beyond the docs repository (3). 30+12+8+5+3 = 58.", "b": "Model reading. `gemini-4-argon` went GA on 13 October and the newest changelog entry, on 30 October, takes the Computer Use tool to GA (30). The deprecations page lists earliest possible shutdown dates and promises advance notice, with no stated minimum, and `deep-research-pro-preview-12-2025` was announced on 8 October for shutdown on 23 October (4 of 12). Shutdown dates in the 90 days from 3 August include Imagen 4 on 17 August, Robotics ER 1.6 on 31 August, `antigravity-preview-05-2026` on 5 October, the Veo 3.1 previews and `gemini-omni-flash-preview` on 22 October and `deep-research-pro-preview-12-2025` on 23 October, each in the table with a replacement, and 3.7 and 3.5 Flash were deprecated on 8 October with requests routed to newer models (4 of 8). 16 dated changelog entries from 13 August to 30 October (15 of 15). SDK repository replies not sampled (5 of 10). Current official SDKs, `google-genai` 2.34.0 and `@google/genai` 2.32.0, both tagged on 29 October, with releases about weekly through October (15). Supported runtimes stated, Python 3.11 and later since 2.34.0 dropped 3.10, Node 20 and later (10)." }, "scores": { "a": 58, "b": 83 } }, { "by": 18, "category": "ergonomics", "leads": "gemini-api", "name": "Agent ergonomics", "reasons": { "a": "Router, read on the model lines. Chat completions, Responses and an Anthropic Messages route in the OpenAI shape, with tool calling depending on the routed model (14 of 20) and structured output likewise (10 of 15). Caching is named in the terms as part of the service, without a per-model price table we read (8 of 15). Context depends on the model; the keyless /v3/models catalogue lists capabilities, prices and regions per model so an agent can choose (12 of 15). A Batches API (10 of 10). No SDK of its own; the docs use the OpenAI Python and TypeScript SDKs, and a hosted MCP server exposes about 40 expert-model tools plus upload_file, check_job and list_models (21 of 25). 14+10+8+12+10+21 = 75.", "b": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling documented, and `FunctionCallingConfig` mode `ANY` with `allowedFunctionNames` forces a call to a named tool, with a `VALIDATED` mode that checks calls by constrained decoding (20 of 20). The structured output page, re-read on 1 November, says output adheres to the supplied JSON Schema and is syntactically correct JSON, and names the keywords constrained decoding doesn't enforce, while asking callers to validate values (13 of 15; the 12 October note's 10 rested on the page promising no constrained decoding). Context caching at 0.1x input, $0.075 per million on 3.8 Flash until 31 December, and implicit caching on `gemini-4-argon`, which doesn't support explicit caches (15). 1,048,576-token context on the default and flagship models (15). Batch half price (10). Official SDKs for Python and JavaScript (10). Documented error codes with backoff guidance (10 of 15, because 400, 402 and 403 are flagged as not retryable but recovery advice per code sits on a separate page we only skimmed)." }, "scores": { "a": 75, "b": 93 } }, { "by": 17, "category": "transparency", "leads": "gemini-api", "name": "Transparency \u0026 trust", "reasons": { "a": "Editorial half only. Closed service under terms last updated 22 October 2026, naming Eden AI SAS and French law with courts in Lyon (15). The data retention page, terms and DPA agree that content isn't stored by default, and the docs carry a per-provider table of training, retention and ZDR (27). No deprecation policy found; models are dropped in docs updates without dated notice that we found (5). The DPA's Annex 2 names operational subprocessors (AWS EMEA, ClickHouse, Sentry, Metabase, Stripe, Brevo and others) with their roles, and the EU endpoint keeps routing in the EU (18). 15+27+5+18 = 65.", "b": "Closed service with clear terms, SDKs Apache-2.0 (15). Terms, pricing page and logs policy agree that free-tier data improves Google products and paid data doesn't, but abuse retention on paid services has no number, and the 26 October changelog entry turns on request storage by default for new paid projects while existing projects keep their setting (20 of 30). Deprecations table with earliest shutdown dates, exact dates promised later (15 of 20). Terms say data may be cached in any country where Google has facilities. No subprocessor list checked (5 of 20)." }, "scores": { "a": 55, "b": 72 } }, { "by": 12, "category": "reliability", "leads": "gemini-api", "name": "Reliability", "reasons": { "a": "Router, read on the hosted lines. No status page is linked from the site or docs, and status.edenai.co didn't resolve for us on 6 November 2026 (0). No incident history to read (5). The rate-limits page gives 10 requests a second per account, shared across keys, while the plans page gives 7 a second, raisable to 15, so the figure is documented but inconsistent (10). Fallback retries the next model or provider on a 429 or error inside one request, but no client-facing 429 or Retry-After guidance was found (8). No SLA published; 'Dedicated support \u0026 SLA' is listed only for the custom Advanced plan, and the pricing page's '99.99%' uptime is a claim without a status page behind it (0). The v3 API is GA (10). 0+5+10+8+0+10 = 33.", "b": "Status page at aistudio.google.com/status (robots.txt allows it), but on 1 November it still returned a script shell titled Google AI Studio with no components or history, so 10 of 20, and 5 of 30 for the incident line with no readable record, as the Gemini Live and Gemini Embedding listings read the same page. Google Cloud's status dashboard covers Vertex AI products, not this endpoint. Per-model RPM and TPM live in AI Studio. The public rate-limits page, re-read on 1 November, publishes tier rules, spend-based limits of $10, $50 and $200 per 10 minutes for Tiers 1 to 3, and batch queue limits (5 of 15). The troubleshooting page, re-read on 1 November, gives exponential backoff with jitter for 429 RESOURCE_EXHAUSTED and 503 UNAVAILABLE, says to retry 429, 408 and 5xx and not 400, 402 or 403, and says the Python SDK retries transient errors up to four times (15). No SLA found for the Developer API (0). The flagship `gemini-4-argon` went GA on 13 October 2026 and the default 3.8 Flash is GA, and the Interactions API the guides lead with has been Generally Available since June 2026. The listing's example and the SDKs default to the `v1beta` path, which Google labels beta, but the API versions page, read on 1 November, says the stable `v1` path carries the Interactions API, generateContent, function calling and structured output and supports every model, so the surface is available as GA, the reading Gemini Embedding takes on the same endpoint. Gemini Live keeps a deduction because the Live API runs on `v1beta` only (10). Before 13 October the only Pro model was `gemini-3.1-pro-preview` (5 of 10)." }, "scores": { "a": 33, "b": 45 } }, { "by": 10, "category": "payments", "leads": "gemini-api", "name": "Payments \u0026 pricing", "reasons": { "a": "No x402, MPP or L402 (0). Provider prices with no markup plus a 5.5% platform fee, published per model in the keyless catalogue (20). llms.txt says some models are billed $0 for tests, but whether a new account can call them without buying credits isn't stated (10). A person signs up in a browser and buys credits through Stripe (0). 0+20+10+0 = 30.", "b": "No machine payment protocol (0). Per-token prices published without a login, re-read on 1 November (20). Free tier on 3.8 Flash, 3.6 Flash, 3.5 Flash-Lite and 3.1 Flash-Lite, not on `gemini-4-argon` or 3.1 Pro preview, and the rate-limits page needs only an active project for the free tier and a linked billing account from Tier 1, so the free tier needs no card (20). A Google account and AI Studio are needed for a key (0)." }, "scores": { "a": 30, "b": 40 } } ], "facts": [ { "a": "Model router", "b": "Model API", "name": "Kind" }, { "a": "Eden AI SAS", "b": "Google", "name": "Vendor" }, { "a": "https://mcp.edenai.run/mcp", "b": "https://generativelanguage.googleapis.com/v1beta", "name": "Hosted endpoint" }, { "a": "HTTP, Streamable HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Freemium", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "none", "b": "Apache-2.0 (SDKs)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-11-05", "b": "2026-10-30", "name": "Last release" }, { "a": "2026-10-22", "b": "2026-04-28", "name": "Terms last updated" }, { "a": "2026-10-22", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "yes", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "yes", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "4k stars", "name": "Popularity" }, { "a": "none", "b": "3.5/5 (2)", "name": "Agent reviews" } ], "faq": [ { "answer": "Gemini Developer API scores 62.2 (B) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on security \u0026 auth.", "question": "Which is better for AI agents, Eden AI or Gemini Developer API?" }, { "answer": "Both need an API key.", "question": "Do Eden AI and Gemini Developer API need an API key?" }, { "answer": "Yes. Eden AI has a hosted endpoint at https://mcp.edenai.run/mcp and Gemini Developer API at https://generativelanguage.googleapis.com/v1beta.", "question": "Can an agent call Eden AI and Gemini Developer API without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Security \u0026 auth, 82 against 50" ], "also": [ "No incidents deducted, where Gemini Developer API loses 3 points for them" ], "goodFor": "Teams that want one key and bill for many LLMs plus OCR, speech, translation and web tools, with EU-only processing.", "slug": "eden-ai", "watchFor": "No public status page, incident history or SLA found" }, { "aheadOn": [ "Reliability, 45 against 33", "Agent ergonomics, 93 against 75", "Payments \u0026 pricing, 40 against 30", "Maintenance \u0026 community, 83 against 58", "Transparency \u0026 trust, 72 against 55" ], "also": [ "Free to start without a card" ], "goodFor": "Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.", "slug": "gemini-api", "watchFor": "Free-tier prompts and responses improve Google products and may be read by human reviewers" } ], "history": { "eden-ai": null, "gemini-api": [ { "causes": [ "product", "evidence", "correction" ], "date": "2026-11-01", "delta": -3.3, "from": 65.5, "fromGrade": "B", "to": 62.2, "toGrade": "B" }, { "causes": [ "correction" ], "date": "2026-10-28", "delta": -0.1, "from": 65.6, "fromGrade": "B", "to": 65.5, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-28", "delta": 0.1, "from": 65.5, "fromGrade": "B", "to": 65.6, "toGrade": "B" } ] }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-eden-ai.json", "title": "Amazon Bedrock vs Eden AI", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-api.json", "title": "Amazon Bedrock vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-eden-ai.json", "title": "Claude API vs Eden AI", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-api.json", "title": "Claude API vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-gemini-api.json", "title": "Antseed vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/antseed-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.json", "title": "Azure OpenAI in Foundry Models vs Eden AI", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.json", "title": "Azure OpenAI in Foundry Models vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-eden-ai.json", "title": "BlockRun.AI vs Eden AI", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api.json", "title": "BlockRun.AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-eden-ai.json", "title": "Cerebras Inference vs Eden AI", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-api.json", "title": "Cerebras Inference vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-eden-ai.json", "title": "Cloudflare AI Gateway vs Eden AI", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-api.json", "title": "Cloudflare AI Gateway vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-eden-ai.json", "title": "Cloudflare Workers AI vs Eden AI", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-api.json", "title": "Cloudflare Workers AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-eden-ai.json", "title": "Cohere Chat API (Command models) vs Eden AI", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-api.json", "title": "Cohere Chat API (Command models) vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai.json", "title": "DeepInfra vs Eden AI", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api.json", "title": "DeepInfra vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-eden-ai.json", "title": "DeepSeek API vs Eden AI", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-api.json", "title": "DeepSeek API vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-elorian-glimpse.json", "title": "Eden AI vs Glimpse", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-elorian-glimpse" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-fireworks-inference.json", "title": "Eden AI vs Fireworks AI Inference", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-fireworks-inference" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-groq.json", "title": "Eden AI vs GroqCloud", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-inkling-2-small.json", "title": "Eden AI vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.json", "title": "Eden AI vs Jais 3", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-meta-model-api.json", "title": "Eden AI vs Meta Model API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-minimax-api.json", "title": "Eden AI vs MiniMax API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-mistral-api.json", "title": "Eden AI vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open.json", "title": "Eden AI vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-nemotron-3-5-super-vl.json", "title": "Eden AI vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-novita-ai.json", "title": "Eden AI vs Novita AI", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-openai-api.json", "title": "Eden AI vs OpenAI API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-prism-inference.json", "title": "Eden AI vs Prism Inference", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam.json", "title": "Eden AI vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-sambanova.json", "title": "Eden AI vs SambaCloud", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow.json", "title": "Eden AI vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-simplismart.json", "title": "Eden AI vs Simplismart", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-stepfun-api.json", "title": "Eden AI vs StepFun API", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-swe-2-flash.json", "title": "Eden AI vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-together-inference.json", "title": "Eden AI vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai.json", "title": "Eden AI vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-xai-api.json", "title": "Eden AI vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-api.json", "title": "Glimpse vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-api.json", "title": "Fireworks AI Inference vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-groq.json", "title": "Gemini Developer API vs GroqCloud", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-hugging-face-inference-providers.json", "title": "Gemini Developer API vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-inkling-2-small.json", "title": "Gemini Developer API vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-jais-3.json", "title": "Gemini Developer API vs Jais 3", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.json", "title": "Gemini Developer API vs Meta Model API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-minimax-api.json", "title": "Gemini Developer API vs MiniMax API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-mistral-api.json", "title": "Gemini Developer API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open.json", "title": "Gemini Developer API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-nemotron-3-5-super-vl.json", "title": "Gemini Developer API vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-novita-ai.json", "title": "Gemini Developer API vs Novita AI", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-openai-api.json", "title": "Gemini Developer API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-openrouter.json", "title": "Gemini Developer API vs OpenRouter", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference.json", "title": "Gemini Developer API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-ramp-router.json", "title": "Gemini Developer API vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-reflection-beam.json", "title": "Gemini Developer API vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-routerplus.json", "title": "Gemini Developer API vs RouterPlus", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-sambanova.json", "title": "Gemini Developer API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow.json", "title": "Gemini Developer API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-simplismart.json", "title": "Gemini Developer API vs Simplismart", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api.json", "title": "Gemini Developer API vs StepFun API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-swe-2-flash.json", "title": "Gemini Developer API vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-tencent-tokenhub.json", "title": "Gemini Developer API vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-together-inference.json", "title": "Gemini Developer API vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-vercel-ai-gateway.json", "title": "Gemini Developer API vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai.json", "title": "Gemini Developer API vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-xai-api.json", "title": "Gemini Developer API vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-eden-ai.json", "title": "Antseed vs Eden AI", "url": "https://www.anchorterminal.com/compare/antseed-vs-eden-ai" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-hugging-face-inference-providers.json", "title": "Eden AI vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-litellm.json", "title": "Eden AI vs LiteLLM", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-litellm" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-openrouter.json", "title": "Eden AI vs OpenRouter", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-ramp-router.json", "title": "Eden AI vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-routerplus.json", "title": "Eden AI vs RouterPlus", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-tencent-tokenhub.json", "title": "Eden AI vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-vercel-ai-gateway.json", "title": "Eden AI vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-vercel-ai-gateway" } ], "panel": null, "scores": [ { "by": 12, "eden-ai": 33, "edge": "gemini-api", "gemini-api": 45, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 4, "eden-ai": 81, "edge": "gemini-api", "gemini-api": 85, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 18, "eden-ai": 75, "edge": "gemini-api", "gemini-api": 93, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 32, "eden-ai": 82, "edge": "eden-ai", "gemini-api": 50, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 10, "eden-ai": 30, "edge": "gemini-api", "gemini-api": 40, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 25, "eden-ai": 58, "edge": "gemini-api", "gemini-api": 83, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 17, "eden-ai": 55, "edge": "gemini-api", "gemini-api": 72, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Gemini Developer API scores 62.2 (B) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on security \u0026 auth. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "eden-ai": "Eden AI gives one OpenAI-compatible key to about 1,000 models and expert models such as OCR, speech and translation, with an EU-only endpoint, per-key guardrails and no stored content by default. No status page, SLA or changelog was found, and its own pages disagree on the default rate limit.", "gemini-api": "Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api", "json": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.min.md" }, "markdown": "Gemini Developer API scores 62.2 (B) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on security \u0026 auth. Both do llm inference.\n\n- Eden AI: grade C, 59.9/100, rank #672 of 1486. Markdown https://www.anchorterminal.com/tools/eden-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/eden-ai.json\n- Gemini Developer API: grade B, 62.2/100, rank #566 of 1486. Markdown https://www.anchorterminal.com/tools/gemini-api.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-api.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 830 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Eden AI (C)\n\nGood for: Teams that want one key and bill for many LLMs plus OCR, speech, translation and web tools, with EU-only processing.\n\nAhead on:\n- Security \u0026 auth, 82 against 50\n\nAlso in its favour:\n- No incidents deducted, where Gemini Developer API loses 3 points for them\n\nWatch for: No public status page, incident history or SLA found\n\n### Gemini Developer API (B)\n\nGood for: Cheap, long-context Flash calls for prototypes, public data and agents that need to start without a card, and Gemini 4 Argon for long reasoning on the paid tier.\n\nAhead on:\n- Reliability, 45 against 33\n- Agent ergonomics, 93 against 75\n- Payments \u0026 pricing, 40 against 30\n- Maintenance \u0026 community, 83 against 58\n- Transparency \u0026 trust, 72 against 55\n\nAlso in its favour:\n- Free to start without a card\n\nWatch for: Free-tier prompts and responses improve Google products and may be read by human reviewers\n\n\n## Why the edge\n\n### Security \u0026 auth: Eden AI +32\n\n- Eden AI (82): Router, read on the model lines. Bearer keys with per-key budgets, expiry and attribution, a key management API, and deny-by-default guardrails that limit models per member, role or key (25). The data retention page says request and response content isn't stored and data isn't used to train models by default (18). Content logging is opt-in, async results are deleted after 7 days, and an organisation can enforce Zero Data Retention and region rules; the EU endpoint refuses non-EU models with 451 (13). Cost and provider per response, and a monitoring dashboard by key (12). The security page says Eden AI is SOC 2 and ISO 27001 certified and links a trust centre; /.well-known/security.txt returns 404 and no disclosure policy was found (14). 25+18+13+12+14 = 82.\n- Gemini Developer API (50): Model reading. Keys live in a Cloud project. Since 28 May 2026 new keys are authorisation keys bound to a service account and restricted to the Gemini API, keys can be limited by IP or origin, unrestricted standard keys are rejected, and the docs give a leak checklist, per the API key page re-read on 1 November. No permission scopes, so 25 of 30, less 10 because the API reads a token from the URL. The Discovery document (revision 20261101) lists `access_token`, `oauth_token` and `key` as query parameters, and on 1 November a generateContent request with no credential answered 403 'Method doesn't allow unregistered callers' while one with a made-up `?access_token=` answered 401 'Request had invalid authentication credentials' (ACCESS_TOKEN_TYPE_UNSUPPORTED), the deduction Google APIs took on 17 October and 1 November and Gemini Embedding and Gemini Live carry (15 of 30). Terms (effective 23 March 2026, re-read 1 November) say free-tier prompts and responses are used to improve Google products and human reviewers may read them, paid tier isn't used (10 of 20). Abuse monitoring on paid services is kept 'a limited period' with no number, grounding data 30 days, and since 26 October new paid projects store Generate Content requests by default unless `store=false`. No zero-retention route on this API was found (5 of 15). Request logs in AI Studio for billed projects (15). security.txt on google.com valid to 2030-04-01 with a vulnerability reward programme, re-read on 1 November. No certification naming the Developer API found, the reading Gemini Embedding takes (5 of 20).\n\n### Maintenance \u0026 community: Gemini Developer API +25\n\n- Eden AI (58): Closed service, read on the model lines. The docs repository changed on 5 and 6 November 2026, including retiring Nova Reel and Veo from video generation, and the terms were updated 22 October (30). No public changelog, so dated API changes in the last 90 days come only from docs commits (12 of 20). Chat support within 48 working hours, no public changelog (8). No SDK of its own, and the MCP server isn't in the official MCP registry (5). Nothing to judge package health by beyond the docs repository (3). 30+12+8+5+3 = 58.\n- Gemini Developer API (83): Model reading. `gemini-4-argon` went GA on 13 October and the newest changelog entry, on 30 October, takes the Computer Use tool to GA (30). The deprecations page lists earliest possible shutdown dates and promises advance notice, with no stated minimum, and `deep-research-pro-preview-12-2025` was announced on 8 October for shutdown on 23 October (4 of 12). Shutdown dates in the 90 days from 3 August include Imagen 4 on 17 August, Robotics ER 1.6 on 31 August, `antigravity-preview-05-2026` on 5 October, the Veo 3.1 previews and `gemini-omni-flash-preview` on 22 October and `deep-research-pro-preview-12-2025` on 23 October, each in the table with a replacement, and 3.7 and 3.5 Flash were deprecated on 8 October with requests routed to newer models (4 of 8). 16 dated changelog entries from 13 August to 30 October (15 of 15). SDK repository replies not sampled (5 of 10). Current official SDKs, `google-genai` 2.34.0 and `@google/genai` 2.32.0, both tagged on 29 October, with releases about weekly through October (15). Supported runtimes stated, Python 3.11 and later since 2.34.0 dropped 3.10, Node 20 and later (10).\n\n### Agent ergonomics: Gemini Developer API +18\n\n- Eden AI (75): Router, read on the model lines. Chat completions, Responses and an Anthropic Messages route in the OpenAI shape, with tool calling depending on the routed model (14 of 20) and structured output likewise (10 of 15). Caching is named in the terms as part of the service, without a per-model price table we read (8 of 15). Context depends on the model; the keyless /v3/models catalogue lists capabilities, prices and regions per model so an agent can choose (12 of 15). A Batches API (10 of 10). No SDK of its own; the docs use the OpenAI Python and TypeScript SDKs, and a hosted MCP server exposes about 40 expert-model tools plus upload_file, check_job and list_models (21 of 25). 14+10+8+12+10+21 = 75.\n- Gemini Developer API (93): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling documented, and `FunctionCallingConfig` mode `ANY` with `allowedFunctionNames` forces a call to a named tool, with a `VALIDATED` mode that checks calls by constrained decoding (20 of 20). The structured output page, re-read on 1 November, says output adheres to the supplied JSON Schema and is syntactically correct JSON, and names the keywords constrained decoding doesn't enforce, while asking callers to validate values (13 of 15; the 12 October note's 10 rested on the page promising no constrained decoding). Context caching at 0.1x input, $0.075 per million on 3.8 Flash until 31 December, and implicit caching on `gemini-4-argon`, which doesn't support explicit caches (15). 1,048,576-token context on the default and flagship models (15). Batch half price (10). Official SDKs for Python and JavaScript (10). Documented error codes with backoff guidance (10 of 15, because 400, 402 and 403 are flagged as not retryable but recovery advice per code sits on a separate page we only skimmed).\n\n### Transparency \u0026 trust: Gemini Developer API +17\n\n- Eden AI (55): Editorial half only. Closed service under terms last updated 22 October 2026, naming Eden AI SAS and French law with courts in Lyon (15). The data retention page, terms and DPA agree that content isn't stored by default, and the docs carry a per-provider table of training, retention and ZDR (27). No deprecation policy found; models are dropped in docs updates without dated notice that we found (5). The DPA's Annex 2 names operational subprocessors (AWS EMEA, ClickHouse, Sentry, Metabase, Stripe, Brevo and others) with their roles, and the EU endpoint keeps routing in the EU (18). 15+27+5+18 = 65.\n- Gemini Developer API (72): Closed service with clear terms, SDKs Apache-2.0 (15). Terms, pricing page and logs policy agree that free-tier data improves Google products and paid data doesn't, but abuse retention on paid services has no number, and the 26 October changelog entry turns on request storage by default for new paid projects while existing projects keep their setting (20 of 30). Deprecations table with earliest shutdown dates, exact dates promised later (15 of 20). Terms say data may be cached in any country where Google has facilities. No subprocessor list checked (5 of 20).\n\n### Reliability: Gemini Developer API +12\n\n- Eden AI (33): Router, read on the hosted lines. No status page is linked from the site or docs, and status.edenai.co didn't resolve for us on 6 November 2026 (0). No incident history to read (5). The rate-limits page gives 10 requests a second per account, shared across keys, while the plans page gives 7 a second, raisable to 15, so the figure is documented but inconsistent (10). Fallback retries the next model or provider on a 429 or error inside one request, but no client-facing 429 or Retry-After guidance was found (8). No SLA published; 'Dedicated support \u0026 SLA' is listed only for the custom Advanced plan, and the pricing page's '99.99%' uptime is a claim without a status page behind it (0). The v3 API is GA (10). 0+5+10+8+0+10 = 33.\n- Gemini Developer API (45): Status page at aistudio.google.com/status (robots.txt allows it), but on 1 November it still returned a script shell titled Google AI Studio with no components or history, so 10 of 20, and 5 of 30 for the incident line with no readable record, as the Gemini Live and Gemini Embedding listings read the same page. Google Cloud's status dashboard covers Vertex AI products, not this endpoint. Per-model RPM and TPM live in AI Studio. The public rate-limits page, re-read on 1 November, publishes tier rules, spend-based limits of $10, $50 and $200 per 10 minutes for Tiers 1 to 3, and batch queue limits (5 of 15). The troubleshooting page, re-read on 1 November, gives exponential backoff with jitter for 429 RESOURCE_EXHAUSTED and 503 UNAVAILABLE, says to retry 429, 408 and 5xx and not 400, 402 or 403, and says the Python SDK retries transient errors up to four times (15). No SLA found for the Developer API (0). The flagship `gemini-4-argon` went GA on 13 October 2026 and the default 3.8 Flash is GA, and the Interactions API the guides lead with has been Generally Available since June 2026. The listing's example and the SDKs default to the `v1beta` path, which Google labels beta, but the API versions page, read on 1 November, says the stable `v1` path carries the Interactions API, generateContent, function calling and structured output and supports every model, so the surface is available as GA, the reading Gemini Embedding takes on the same endpoint. Gemini Live keeps a deduction because the Live API runs on `v1beta` only (10). Before 13 October the only Pro model was `gemini-3.1-pro-preview` (5 of 10).\n\n### Payments \u0026 pricing: Gemini Developer API +10\n\n- Eden AI (30): No x402, MPP or L402 (0). Provider prices with no markup plus a 5.5% platform fee, published per model in the keyless catalogue (20). llms.txt says some models are billed $0 for tests, but whether a new account can call them without buying credits isn't stated (10). A person signs up in a browser and buys credits through Stripe (0). 0+20+10+0 = 30.\n- Gemini Developer API (40): No machine payment protocol (0). Per-token prices published without a login, re-read on 1 November (20). Free tier on 3.8 Flash, 3.6 Flash, 3.5 Flash-Lite and 3.1 Flash-Lite, not on `gemini-4-argon` or 3.1 Pro preview, and the rate-limits page needs only an active project for the free tier and a linked billing account from Tier 1, so the free tier needs no card (20). A Google account and AI Studio are needed for a key (0).\n\n## Score by category\n\n| Category | Weight | Eden AI | Gemini Developer API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 33 | 45 | Gemini Developer API +12 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 81 | 85 | Gemini Developer API +4 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 93 | Gemini Developer API +18 |\n| Security \u0026 auth | 14% (17.5 this run) | 82 | 50 | Eden AI +32 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 40 | Gemini Developer API +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 58 | 83 | Gemini Developer API +25 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 72 | Gemini Developer API +17 |\n| Negative events | ≤15 | 0 | -3 | |\n| **Total** | | **59.9 · C** | **62.2 · B** | |\n\n## Facts side by side\n\n| Fact | Eden AI | Gemini Developer API |\n| --- | --- | --- |\n| Kind | Model router | Model API |\n| Vendor | Eden AI SAS | Google |\n| Hosted endpoint | `https://mcp.edenai.run/mcp` | `https://generativelanguage.googleapis.com/v1beta` |\n| Transports | HTTP, Streamable HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Freemium |\n| x402 | no | no |\n| Licence | none | Apache-2.0 (SDKs) |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-11-05 | 2026-10-30 |\n| Terms last updated | 2026-10-22 | 2026-04-28 |\n| Privacy policy last updated | 2026-10-22 | 2026-10-01 |\n| Customer content may train models | not found in the text | yes |\n| Terms restrict automated access | not found in the text | yes |\n| Terms restrict benchmarking | not found in the text | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | not found in the text | not found in the text |\n| Popularity | none | 4k stars |\n| Agent reviews | none | 3.5/5 (2) |\n\n## How the scores have moved\n\n### Eden AI\n\nNo score change since it was listed.\n\n### Gemini Developer API\n\n- -3.3 on 1 November 2026: product, new evidence, correction\n- -0.1 on 28 October 2026: correction\n- +0.1 on 28 October 2026: checklist\n\n## Verdicts\n\n**Eden AI.** Eden AI gives one OpenAI-compatible key to about 1,000 models and expert models such as OCR, speech and translation, with an EU-only endpoint, per-key guardrails and no stored content by default. No status page, SLA or changelog was found, and its own pages disagree on the default rate limit.\n\n**Gemini Developer API.** Free tier on 3.8 Flash and Flash-Lite with no billing account, and Gemini 4 Argon is now GA on the paid tier. Free-tier prompts and responses improve Google products and may be read by human reviewers, and the API accepts an OAuth token in the URL query string.\n\n## Before you call either\n\n### Eden AI\n\n1. Read GET https://api.edenai.run/v3/models at run time for model ids, prices and regions instead of hard-coding them\n2. Use https://api.eu.edenai.run/v3 when data must stay in the EU, and pick another model on a 451 region_not_allowed\n3. Set max_tokens to 500 or more for reasoning models, or the answer can come back empty\n4. Pass a fallbacks list so a provider 429 or error retries inside one request\n5. Poll check_job for MCP tools marked long-running instead of calling them again\n\n### Gemini Developer API\n\n1. Never send customer data through a free-tier key. Link billing first\n2. Send keys in the `x-goog-api-key` header, never as `key` or `access_token` in the URL\n3. Back off exponentially on 429 RESOURCE_EXHAUSTED and 503, and don't retry 400, 402 or 403\n4. Remove temperature, top_p and top_k. Gemini 4 and 3.6+ ignore them and a future release returns 400\n5. On `gemini-4-argon`, set max_output_tokens to at least 32,768 and client timeouts to 15 minutes, or use background execution\n\n## Questions\n\n### Which is better for AI agents, Eden AI or Gemini Developer API?\n\nGemini Developer API scores 62.2 (B) on agent readiness against Eden AI's 59.9 (C), and leads in 6 of 7 scored categories. Eden AI leads on security \u0026 auth.\n\n### Do Eden AI and Gemini Developer API need an API key?\n\nBoth need an API key.\n\n### Can an agent call Eden AI and Gemini Developer API without installing anything?\n\nYes. Eden AI has a hosted endpoint at https://mcp.edenai.run/mcp and Gemini Developer API at https://generativelanguage.googleapis.com/v1beta.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"eden-ai\", \"b\": \"gemini-api\"}`. From a terminal: `anchor compare eden-ai gemini-api`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/eden-ai.json and https://www.anchorterminal.com/api/v1/tools/gemini-api.json\n\n## Other comparisons with Eden AI or Gemini Developer API\n\n- [Amazon Bedrock vs Eden AI](https://www.anchorterminal.com/compare/amazon-bedrock-vs-eden-ai.md)\n- [Amazon Bedrock vs Gemini Developer API](https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-api.md)\n- [Claude API vs Eden AI](https://www.anchorterminal.com/compare/anthropic-api-vs-eden-ai.md)\n- [Claude API vs Gemini Developer API](https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-api.md)\n- [Antseed vs Gemini Developer API](https://www.anchorterminal.com/compare/antseed-vs-gemini-api.md)\n- [Azure OpenAI in Foundry Models vs Eden AI](https://www.anchorterminal.com/compare/azure-openai-vs-eden-ai.md)\n- [Azure OpenAI in Foundry Models vs Gemini Developer API](https://www.anchorterminal.com/compare/azure-openai-vs-gemini-api.md)\n- [BlockRun.AI vs Eden AI](https://www.anchorterminal.com/compare/blockrun-ai-vs-eden-ai.md)\n- [BlockRun.AI vs Gemini Developer API](https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api.md)\n- [Cerebras Inference vs Eden AI](https://www.anchorterminal.com/compare/cerebras-inference-vs-eden-ai.md)\n- [Cerebras Inference vs Gemini Developer API](https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-api.md)\n- [Cloudflare AI Gateway vs Eden AI](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-eden-ai.md)\n- [Cloudflare AI Gateway vs Gemini Developer API](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-api.md)\n- [Cloudflare Workers AI vs Eden AI](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-eden-ai.md)\n- [Cloudflare Workers AI vs Gemini Developer API](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-api.md)\n- [Cohere Chat API (Command models) vs Eden AI](https://www.anchorterminal.com/compare/cohere-chat-vs-eden-ai.md)\n- [Cohere Chat API (Command models) vs Gemini Developer API](https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-api.md)\n- [DeepInfra vs Eden AI](https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai.md)\n- [DeepInfra vs Gemini Developer API](https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api.md)\n- [DeepSeek API vs Eden AI](https://www.anchorterminal.com/compare/deepseek-api-vs-eden-ai.md)\n- [DeepSeek API vs Gemini Developer API](https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-api.md)\n- [Eden AI vs Glimpse](https://www.anchorterminal.com/compare/eden-ai-vs-elorian-glimpse.md)\n- [Eden AI vs Fireworks AI Inference](https://www.anchorterminal.com/compare/eden-ai-vs-fireworks-inference.md)\n- [Eden AI vs GroqCloud](https://www.anchorterminal.com/compare/eden-ai-vs-groq.md)\n- [Eden AI vs Inkling 2 Small](https://www.anchorterminal.com/compare/eden-ai-vs-inkling-2-small.md)\n- [Eden AI vs Jais 3](https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.md)\n- [Eden AI vs Meta Model API](https://www.anchorterminal.com/compare/eden-ai-vs-meta-model-api.md)\n- [Eden AI vs MiniMax API](https://www.anchorterminal.com/compare/eden-ai-vs-minimax-api.md)\n- [Eden AI vs Mistral AI API](https://www.anchorterminal.com/compare/eden-ai-vs-mistral-api.md)\n- [Eden AI vs Muse Spark Open](https://www.anchorterminal.com/compare/eden-ai-vs-muse-spark-open.md)\n- [Eden AI vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/eden-ai-vs-nemotron-3-5-super-vl.md)\n- [Eden AI vs Novita AI](https://www.anchorterminal.com/compare/eden-ai-vs-novita-ai.md)\n- [Eden AI vs OpenAI API](https://www.anchorterminal.com/compare/eden-ai-vs-openai-api.md)\n- [Eden AI vs Prism Inference](https://www.anchorterminal.com/compare/eden-ai-vs-prism-inference.md)\n- [Eden AI vs Beam 1.0](https://www.anchorterminal.com/compare/eden-ai-vs-reflection-beam.md)\n- [Eden AI vs SambaCloud](https://www.anchorterminal.com/compare/eden-ai-vs-sambanova.md)\n- [Eden AI vs SiliconFlow](https://www.anchorterminal.com/compare/eden-ai-vs-siliconflow.md)\n- [Eden AI vs Simplismart](https://www.anchorterminal.com/compare/eden-ai-vs-simplismart.md)\n- [Eden AI vs StepFun API](https://www.anchorterminal.com/compare/eden-ai-vs-stepfun-api.md)\n- [Eden AI vs SWE-2 Flash](https://www.anchorterminal.com/compare/eden-ai-vs-swe-2-flash.md)\n- [Eden AI vs Together AI Inference](https://www.anchorterminal.com/compare/eden-ai-vs-together-inference.md)\n- [Eden AI vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/eden-ai-vs-vertex-ai.md)\n- [Eden AI vs xAI API (Grok)](https://www.anchorterminal.com/compare/eden-ai-vs-xai-api.md)\n- [Glimpse vs Gemini Developer API](https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-api.md)\n- [Fireworks AI Inference vs Gemini Developer API](https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-api.md)\n- [Gemini Developer API vs GroqCloud](https://www.anchorterminal.com/compare/gemini-api-vs-groq.md)\n- [Gemini Developer API vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/gemini-api-vs-hugging-face-inference-providers.md)\n- [Gemini Developer API vs Inkling 2 Small](https://www.anchorterminal.com/compare/gemini-api-vs-inkling-2-small.md)\n- [Gemini Developer API vs Jais 3](https://www.anchorterminal.com/compare/gemini-api-vs-jais-3.md)\n- [Gemini Developer API vs Meta Model API](https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.md)\n- [Gemini Developer API vs MiniMax API](https://www.anchorterminal.com/compare/gemini-api-vs-minimax-api.md)\n- [Gemini Developer API vs Mistral AI API](https://www.anchorterminal.com/compare/gemini-api-vs-mistral-api.md)\n- [Gemini Developer API vs Muse Spark Open](https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open.md)\n- [Gemini Developer API vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/gemini-api-vs-nemotron-3-5-super-vl.md)\n- [Gemini Developer API vs Novita AI](https://www.anchorterminal.com/compare/gemini-api-vs-novita-ai.md)\n- [Gemini Developer API vs OpenAI API](https://www.anchorterminal.com/compare/gemini-api-vs-openai-api.md)\n- [Gemini Developer API vs OpenRouter](https://www.anchorterminal.com/compare/gemini-api-vs-openrouter.md)\n- [Gemini Developer API vs Prism Inference](https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference.md)\n- [Gemini Developer API vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/gemini-api-vs-ramp-router.md)\n- [Gemini Developer API vs Beam 1.0](https://www.anchorterminal.com/compare/gemini-api-vs-reflection-beam.md)\n- [Gemini Developer API vs RouterPlus](https://www.anchorterminal.com/compare/gemini-api-vs-routerplus.md)\n- [Gemini Developer API vs SambaCloud](https://www.anchorterminal.com/compare/gemini-api-vs-sambanova.md)\n- [Gemini Developer API vs SiliconFlow](https://www.anchorterminal.com/compare/gemini-api-vs-siliconflow.md)\n- [Gemini Developer API vs Simplismart](https://www.anchorterminal.com/compare/gemini-api-vs-simplismart.md)\n- [Gemini Developer API vs StepFun API](https://www.anchorterminal.com/compare/gemini-api-vs-stepfun-api.md)\n- [Gemini Developer API vs SWE-2 Flash](https://www.anchorterminal.com/compare/gemini-api-vs-swe-2-flash.md)\n- [Gemini Developer API vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/gemini-api-vs-tencent-tokenhub.md)\n- [Gemini Developer API vs Together AI Inference](https://www.anchorterminal.com/compare/gemini-api-vs-together-inference.md)\n- [Gemini Developer API vs Vercel AI Gateway](https://www.anchorterminal.com/compare/gemini-api-vs-vercel-ai-gateway.md)\n- [Gemini Developer API vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/gemini-api-vs-vertex-ai.md)\n- [Gemini Developer API vs xAI API (Grok)](https://www.anchorterminal.com/compare/gemini-api-vs-xai-api.md)\n- [Antseed vs Eden AI](https://www.anchorterminal.com/compare/antseed-vs-eden-ai.md)\n- [Eden AI vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/eden-ai-vs-hugging-face-inference-providers.md)\n- [Eden AI vs LiteLLM](https://www.anchorterminal.com/compare/eden-ai-vs-litellm.md)\n- [Eden AI vs OpenRouter](https://www.anchorterminal.com/compare/eden-ai-vs-openrouter.md)\n- [Eden AI vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/eden-ai-vs-ramp-router.md)\n- [Eden AI vs RouterPlus](https://www.anchorterminal.com/compare/eden-ai-vs-routerplus.md)\n- [Eden AI vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/eden-ai-vs-tencent-tokenhub.md)\n- [Eden AI vs Vercel AI Gateway](https://www.anchorterminal.com/compare/eden-ai-vs-vercel-ai-gateway.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-07", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Eden AI vs Gemini Developer API", "url": "" } ], "description": "Gemini Developer scores 62.2 (B) to Eden AI's 59.9 (C) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Eden AI C 59.9", "Gemini Developer API B 62.2", "scores" ], "h1": "Eden AI vs Gemini Developer API", "image": "https://www.anchorterminal.com/assets/og/default.png", "path": "/compare/eden-ai-vs-gemini-api", "published": "2026-10-01", "section": "tools", "title": "Eden AI vs Gemini Developer for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-11-07", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api" }, "tokens": { "markdown": 6650, "slim": 3330 }, "version": 1 }