{ "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": 675, "ranked": true, "rankOf": 1510, "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": "2026-11-07T19:22:04.810042763Z", "lastOk": true, "lastStatus": 405, "lastMs": 58, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 50, "p95ms24h": 129, "samples24h": 210, "samples30d": 210, "days": [ { "date": "2026-11-06", "probes": 13, "ok": 13 }, { "date": "2026-11-07", "probes": 197, "ok": 197 } ] }, "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": "2026-11-07T19:22:04.810042763Z" } }, "answer": "Eden AI scores 59.9 (C) on agent readiness against Jais 3's 52.1 (D), and leads in 3 of 7 scored categories. Jais 3 leads on reliability and payments \u0026 pricing.", "b": { "slug": "jais-3", "name": "Jais 3", "vendor": "Institute of Foundation Models (MBZUAI) and Inception42", "vendorUrl": "https://ifm.ai/blog/jais3/", "kind": "model", "category": "inference", "summary": "Jais 3 is an open-weight family of Arabic and English reasoning models from MBZUAI's Institute of Foundation Models and Inception42, from 0.9B to 375B parameters with up to 512K tokens of context, released under Apache-2.0 for self-hosting.", "url": "https://www.anchorterminal.com/tools/jais-3", "markdownUrl": "https://www.anchorterminal.com/tools/jais-3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/jais-3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/jais-3.json", "dns": "jais-3.grade.anchorterminal.com", "repo": "https://huggingface.co/inception42/Jais-3-375B-A23B", "license": "Apache-2.0, declared in the metadata of every model card read. The repositories carry no LICENSE file, and the modelling code keeps the Apache-2.0 header of the Qwen3-MoE code it was generated from", "transports": [ "http" ], "packages": [], "auth": "none", "authNotes": "No account or key. The inception42 and IFM repositories on Hugging Face are not gated and download anonymously. A local server has whatever authentication the owner adds, such as vLLM's `--api-key`. The cards' examples call the local server with `api_key=\"EMPTY\"`.", "pricing": "free", "pricingNotes": "Free to download and run under Apache-2.0, with the owner's GPUs as the cost. No hosted endpoint from IFM or Inception42 was found, and on 4 November 2026 Hugging Face listed no inference provider for any of the six main repositories.", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Jais 3 is a set of weights the owner serves, with no payment route and no hosted API from the makers found (checked 2026-10-17).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-17" }, "docsUrl": "https://huggingface.co/inception42/Jais-3-375B-A23B", "capabilities": [ "inference.llm", "inference.open-weights", "inference.local" ], "tags": [ "model", "open-weights", "self-hosted", "local", "free", "no-card", "apache-2.0", "openai-compatible", "gpu", "apple-silicon", "mlx", "new" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 52.1, "grade": "D", "agentReady": false, "rank": 998, "ranked": true, "rankOf": 1510, "categoryRank": 34, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 65, "maintenance": 62, "payments": 60, "reliability": 48, "schema": 54, "security": 31, "transparency": 55 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-04" }, "negative": 0, "verdict": "Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code and no security contact is published.", "bestFor": "An agent or team that needs Arabic and English reasoning and tool use on its own hardware, from a laptop-sized 0.9B to a cluster-sized 375B, under Apache-2.0.", "strengths": [ "Six checkpoints from 0.9B (2.2 GB) to 375B-A23B (758 GB), with FP8, GGUF and MLX builds from IFM", "Apache-2.0 on every card read, ungated downloads, and the CPT, Cooldown, SFT and FTPO training checkpoints published as branches", "With the `k2_horizon` parsers, reasoning returns in `reasoning_content` and tool calls as `tool_calls`, in JSON, XML or typed XML formats", "Launch recipes in the vLLM and SGLang projects' own docs, and K2 Horizon support in llama.cpp's tagged builds since 6 October 2026", "The chat template validates tool schemas and rejects unknown options with named errors, such as an unsupported `reasoning_effort`" ], "weaknesses": [ "Every documented vLLM, SGLang, Transformers and MLX command passes `--trust-remote-code`, so the repository's Python runs on the host", "The 375B-A23B reference deployment is 16 H100 GPUs across two nodes, or one 8-GPU H200 node", "The code repository is marked 'coming soon', and the technical report, blog and press release on ifm.ai sit behind a bot check", "No LICENSE file in the repositories, no changelog, no deprecation policy and no security contact or security.txt found", "Released on 15 October 2026, with template fixes on 14 and 15 October, and mlx-lm v0.32.2 has no K2 Horizon support of its own" ], "agentNotes": [ "Start vLLM or SGLang with `--reasoning-parser k2_horizon`, and add `--tool-call-parser k2_horizon` (vLLM also `--enable-auto-tool-choice`) for tools, or tool calls come back as raw text", "Pass `reasoning_effort` (`high`, `medium` or `low`) and `tool_call_format` inside `chat_template_kwargs`. Other values return a template error", "Send each earlier assistant turn back with `reasoning_content` as a string, even an empty one. A missing or null thinking field makes the template raise an error", "Send your own system message, or set `default_system_prompt` to an empty string. Without one the template inserts an identity prompt naming IFM, Inception42 and Cerebras", "Budget at least 32,768 output tokens at `high` effort, which the cards recommend for every request" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.1 } ], "editorialScores": { "ergonomics": 65, "maintenance": 62, "payments": 60, "reliability": 48, "schema": 54, "security": 31, "transparency": 51 }, "provenanceScore": 58, "checked": { "categories": { "ergonomics": "2026-11-04", "maintenance": "2026-11-04", "payments": "2026-11-04", "reliability": "2026-11-04", "schema": "2026-11-04", "security": "2026-11-04", "transparency": "2026-11-04" }, "last": "2026-11-04", "lastMoved": "2026-10-27", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "vllm serve inception42/Jais-3-32B \\\n --trust-remote-code \\\n --dtype bfloat16 \\\n --tensor-parallel-size 2 \\\n --reasoning-parser k2_horizon \\\n --enable-auto-tool-choice \\\n --tool-call-parser k2_horizon", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"model\": \"inception42/Jais-3-32B\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Summarise this contract in Arabic.\"}],\n \"max_tokens\": 32768,\n \"chat_template_kwargs\": {\"reasoning_effort\": \"high\"}\n }'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/jais-3" }, "area": "models", "provenance": { "legalEntity": "Mohamed bin Zayed University of Artificial Intelligence (Institute of Foundation Models), with Inception42 (a G42 company)", "domain": "ifm.ai", "domainRegistered": "2022-09-06", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-17", "notes": [ "The chat template's default system prompt names the Institute of Foundation Models (IFM) at MBZUAI, with Inception42 (a G42 company) and Cerebras, and gives MBZUAI's Arabic name, Mohamed bin Zayed University of Artificial Intelligence. The citation names the IFM, Inception42 and Cerebras teams. No registered company name for Inception42 was read first-hand.", "RDAP gives a registration date of 6 September 2022 for ifm.ai. The weights are on huggingface.co under the inception42 and IFM organisations.", "The licence is Apache-2.0 in each card's metadata. There is no LICENSE file and no terms or privacy document governing the weights, and no input reaches the makers when the owner serves them.", "ifm.ai answered our reader with a Cloudflare bot check on the press release, the blog post and the sitemap, so they weren't read. inceptionai.ai and mbzuai.ac.ae answered with bot or firewall pages too. ifm.ai/.well-known/security.txt returned 404 on 17 October 2026.", "No hosted endpoint, so no status page. No changelog. The Hugging Face commit history is public and was read." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/jais-3.json", "live": { "slug": "jais-3", "securityTxt": { "url": "https://ifm.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-07T16:01:26.731681483Z" }, "domain": { "domain": "ifm.ai", "registered": "2022-09-06", "source": "https://rdap.identitydigital.services/rdap/domain/ifm.ai", "checkedAt": "2026-11-01T14:11:01.629779131Z" }, "updatedAt": "2026-11-07T16:01:26.731681483Z" } }, "edges": [ { "by": 51, "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": "Read as software the owner runs. No account or key, ungated downloads, and server authentication is the owner's to add (12 of 30). A model that calls whatever tools the operator wires in. The cards rule high-stakes decisions without human oversight out of scope, but nothing in the model or template enforces approval (6 of 20). The template renders tool results in their own `tool` role, but string results go in as raw text (only non-string content is serialised with `tojson`), no escaping of special-token sequences in caller or tool text was found, and no injection guidance or results are published (4 of 15). No call log of its own. Visibility depends on the serving stack (5 of 15). Every launch command passes `--trust-remote-code`, so `modeling_k2_horizon.py` from the repository runs on the host. We read it and found only torch and transformers imports. No security.txt on ifm.ai (404 on 17 October, not re-read), no security contact on the cards and no safety evaluation found. Since 25 October the cards link a technical report on ifm.ai, which answered a Cloudflare bot check on 4 November, so what it covers is unread (4 of 20). 12 + 6 + 4 + 5 + 4 = 31." }, "scores": { "a": 82, "b": 31 } }, { "by": 30, "category": "payments", "leads": "jais-3", "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": "Self-hosted rule. Apache-2.0 weights with nothing to buy and no hosted option from the makers (20). Free to run with no card (20). Downloads are ungated and anonymous, so an agent can fetch the weights with no sign-up (20). No payment protocol (0). 20 + 20 + 20 + 0 = 60." }, "scores": { "a": 30, "b": 60 } }, { "by": 27, "category": "schema", "leads": "eden-ai", "name": "Schema \u0026 documentation", "reasons": { "a": "Router, scored on the API lines. OpenAPI 3.1 document for v3 (version 3.0.0, 46 paths) at api.edenai.run/v3/docs/openapi.json, plus a management API spec (25). llms.txt on the site and in the docs, llms-full.txt and Markdown pages (10). llms.txt states when to use Eden AI and when not to, and the docs separate chat models from expert models (17). Requests follow the OpenAI shape, and expert-model tools on the MCP server carry JSON Schema inputs (12). Examples throughout, but the spec documents only 200, 201, 202, 302 and 422, while llms.txt names 401 and 451 for region refusals (10). Versioned /v3 path, but no public changelog was found (7). 25+10+17+12+10+7 = 81.", "b": "Read for a model the owner serves. No specification of its own. The contract is the Jinja chat template, which validates tool JSON Schemas (required fields must exist in properties) and rejects unknown options, plus the OpenAI-compatible servers in vLLM and SGLang with the `k2_horizon` parsers (10 of 25). The cards are Markdown on Hugging Face, and SGLang's docs have an llms.txt, but no llms.txt or Markdown docs from IFM were found and ifm.ai couldn't be read (5 of 10). Each card states intended use, four out-of-scope uses, limitations and per-size serving advice (15 of 20). `reasoning_effort` and `tool_call_format` are enums enforced with errors, and tools take JSON Schema. The 375B card describes `low` as no reasoning and omits `medium`, which the template accepts, and structured output isn't documented (10 of 15). Python examples for a reasoning call and a tool loop, commands for five runtimes, and named template errors, but no error reference for a served model (11 of 15). No changelog or version scheme. Training-stage branches and public commit history (3 of 15). 10 + 5 + 15 + 10 + 11 + 3 = 54." }, "scores": { "a": 81, "b": 54 } }, { "by": 15, "category": "reliability", "leads": "jais-3", "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": "Read with the local-software lines, since the owner serves the weights. Ungated weights on Hugging Face with stated runtimes (vLLM 0.30.0 or later, SGLang 0.5.20 or later, Transformers validated at 5.15.0, mlx-lm with shipped model code), but every launch command trusts remote code. The GGUF card still says to build llama.cpp from master or wait for a release, though llama.cpp's own tags carry K2 Horizon support from b11454 (6 October 2026) (16 of 20, held). No CI or test suite of the makers' was found, and the code repository is 'coming soon'. SGLang's cookbook reports a validation pass of the tool-call parser on all six checkpoints, and IFM's MLX cards describe conversion checks against the PyTorch code (3 of 25). No crash reports. On 4 November the six main repositories held seven discussions, six of them closed citation updates and one open MoVA-36B-A4B thread from 26 October with no reply yet. Two template fixes for OpenAI-style message history landed on 14 October, before the launch (15 of 25). No changelog or version tags, only public commit messages (2 of 15). Released as named final checkpoints with no preview label, though mlx-lm v0.32.2 still has no K2 Horizon model code of its own (12 of 15). 16 + 3 + 15 + 2 + 12 = 48." }, "scores": { "a": 33, "b": 48 } }, { "by": 10, "category": "ergonomics", "leads": "eden-ai", "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, throughput, defaults and SDKs, errors). OpenAI-format tools come back as `tool_calls` in three selectable wire formats, with a multi-turn loop example. Forced `tool_choice` and parallel calls aren't documented (15 of 20). JSON mode and schemas only through each runtime's guided decoding, not documented by the makers (5 of 15). Prefix caching is the runtime's, and SGLang's page covers HiCache (7 of 10). 524,288 tokens on five of six models, 131,072 on the 0.9B (14 of 15). Uno adapters claim 2.15 times faster decoding for 0.9B to 32B but need IFM's own engine. SGLang lists n-gram speculative decoding and PD disaggregation. No batch mode of its own (7 of 10). Any OpenAI SDK works against a local server, and the 0.9B to 7B fit one GPU. But the cards ask for high effort and 32,768 output tokens on every request, the template inserts an identity system prompt when none is sent, and earlier assistant turns must carry a string thinking field (8 of 15). The template raises named errors for bad options, string-encoded tool arguments and malformed schemas. Server errors are the runtime's (9 of 15). 15 + 5 + 7 + 14 + 7 + 8 + 9 = 65." }, "scores": { "a": 75, "b": 65 } } ], "facts": [ { "a": "Model router", "b": "Model API", "name": "Kind" }, { "a": "Eden AI SAS", "b": "Institute of Foundation Models (MBZUAI) and Inception42", "name": "Vendor" }, { "a": "https://mcp.edenai.run/mcp", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "HTTP, Streamable HTTP", "b": "HTTP", "name": "Transports" }, { "a": "API key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "none", "b": "Apache-2.0, declared in the metadata of every model card read. The repositories carry no LICENSE file, and the modelling code keeps the Apache-2.0 header of the Qwen3-MoE code it was generated from", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-05", "b": "2026-10-15", "name": "Last release" }, { "a": "2026-10-22", "b": "no document linked", "name": "Terms last updated" }, { "a": "2026-10-22", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "not read yet", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not read yet", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "not read yet", "name": "Arbitration or class-action waiver" }, { "a": "none", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Eden AI scores 59.9 (C) on agent readiness against Jais 3's 52.1 (D), and leads in 3 of 7 scored categories. Jais 3 leads on reliability and payments \u0026 pricing.", "question": "Which is better for AI agents, Eden AI or Jais 3?" }, { "answer": "Eden AI needs an API key. Jais 3 needs no key.", "question": "Do Eden AI and Jais 3 need an API key?" }, { "answer": "Eden AI has a hosted endpoint at https://mcp.edenai.run/mcp. No hosted endpoint is listed for Jais 3.", "question": "Can an agent call Eden AI and Jais 3 without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 81 against 54", "Agent ergonomics, 75 against 65", "Security \u0026 auth, 82 against 31" ], "also": [ "A hosted endpoint, with nothing to install" ], "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, 48 against 33", "Payments \u0026 pricing, 60 against 30" ], "also": [ "No key needed to call it", "Free to start without a card" ], "goodFor": "An agent or team that needs Arabic and English reasoning and tool use on its own hardware, from a laptop-sized 0.9B to a cluster-sized 375B, under Apache-2.0.", "slug": "jais-3", "watchFor": "Every documented vLLM, SGLang, Transformers and MLX command passes `--trust-remote-code`, so the repository's Python runs on the host" } ], "history": { "eden-ai": null, "jais-3": [ { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.4, "from": 51.7, "fromGrade": "D", "to": 52.1, "toGrade": "D" } ] }, "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-jais-3.json", "title": "Amazon Bedrock vs Jais 3", "url": "https://www.anchorterminal.com/compare/amazon-bedrock-vs-jais-3" }, { "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-jais-3.json", "title": 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"https://www.anchorterminal.com/compare/jais-3-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-vercel-ai-gateway.json", "title": "Jais 3 vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/jais-3-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai.json", "title": "Jais 3 vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/jais-3-vs-xai-api.json", "title": "Jais 3 vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/jais-3-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": 15, "eden-ai": 33, "edge": "jais-3", "jais-3": 48, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 27, "eden-ai": 81, "edge": "eden-ai", "jais-3": 54, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 10, "eden-ai": 75, "edge": "eden-ai", "jais-3": 65, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 51, "eden-ai": 82, "edge": "eden-ai", "jais-3": 31, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 30, "eden-ai": 30, "edge": "jais-3", "jais-3": 60, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 4, "eden-ai": 58, "edge": "jais-3", "jais-3": 62, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 0, "eden-ai": 55, "edge": "", "jais-3": 55, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "Eden AI scores 59.9 (C) on agent readiness against Jais 3's 52.1 (D), and leads in 3 of 7 scored categories. Jais 3 leads on reliability and payments \u0026 pricing. 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.", "jais-3": "Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code and no security contact is published." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3", "json": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.min.md" }, "markdown": "Eden AI scores 59.9 (C) on agent readiness against Jais 3's 52.1 (D), and leads in 3 of 7 scored categories. Jais 3 leads on reliability and payments \u0026 pricing. Both do llm inference.\n\n- Eden AI: grade C, 59.9/100, rank #675 of 1510. Markdown https://www.anchorterminal.com/tools/eden-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/eden-ai.json\n- Jais 3: grade D, 52.1/100, rank #998 of 1510. Markdown https://www.anchorterminal.com/tools/jais-3.md · JSON https://www.anchorterminal.com/api/v1/tools/jais-3.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- Schema \u0026 documentation, 81 against 54\n- Agent ergonomics, 75 against 65\n- Security \u0026 auth, 82 against 31\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: No public status page, incident history or SLA found\n\n### Jais 3 (D)\n\nGood for: An agent or team that needs Arabic and English reasoning and tool use on its own hardware, from a laptop-sized 0.9B to a cluster-sized 375B, under Apache-2.0.\n\nAhead on:\n- Reliability, 48 against 33\n- Payments \u0026 pricing, 60 against 30\n\nAlso in its favour:\n- No key needed to call it\n- Free to start without a card\n\nWatch for: Every documented vLLM, SGLang, Transformers and MLX command passes `--trust-remote-code`, so the repository's Python runs on the host\n\n\n## Why the edge\n\n### Security \u0026 auth: Eden AI +51\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- Jais 3 (31): Read as software the owner runs. No account or key, ungated downloads, and server authentication is the owner's to add (12 of 30). A model that calls whatever tools the operator wires in. The cards rule high-stakes decisions without human oversight out of scope, but nothing in the model or template enforces approval (6 of 20). The template renders tool results in their own `tool` role, but string results go in as raw text (only non-string content is serialised with `tojson`), no escaping of special-token sequences in caller or tool text was found, and no injection guidance or results are published (4 of 15). No call log of its own. Visibility depends on the serving stack (5 of 15). Every launch command passes `--trust-remote-code`, so `modeling_k2_horizon.py` from the repository runs on the host. We read it and found only torch and transformers imports. No security.txt on ifm.ai (404 on 17 October, not re-read), no security contact on the cards and no safety evaluation found. Since 25 October the cards link a technical report on ifm.ai, which answered a Cloudflare bot check on 4 November, so what it covers is unread (4 of 20). 12 + 6 + 4 + 5 + 4 = 31.\n\n### Payments \u0026 pricing: Jais 3 +30\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- Jais 3 (60): Self-hosted rule. Apache-2.0 weights with nothing to buy and no hosted option from the makers (20). Free to run with no card (20). Downloads are ungated and anonymous, so an agent can fetch the weights with no sign-up (20). No payment protocol (0). 20 + 20 + 20 + 0 = 60.\n\n### Schema \u0026 documentation: Eden AI +27\n\n- Eden AI (81): Router, scored on the API lines. OpenAPI 3.1 document for v3 (version 3.0.0, 46 paths) at api.edenai.run/v3/docs/openapi.json, plus a management API spec (25). llms.txt on the site and in the docs, llms-full.txt and Markdown pages (10). llms.txt states when to use Eden AI and when not to, and the docs separate chat models from expert models (17). Requests follow the OpenAI shape, and expert-model tools on the MCP server carry JSON Schema inputs (12). Examples throughout, but the spec documents only 200, 201, 202, 302 and 422, while llms.txt names 401 and 451 for region refusals (10). Versioned /v3 path, but no public changelog was found (7). 25+10+17+12+10+7 = 81.\n- Jais 3 (54): Read for a model the owner serves. No specification of its own. The contract is the Jinja chat template, which validates tool JSON Schemas (required fields must exist in properties) and rejects unknown options, plus the OpenAI-compatible servers in vLLM and SGLang with the `k2_horizon` parsers (10 of 25). The cards are Markdown on Hugging Face, and SGLang's docs have an llms.txt, but no llms.txt or Markdown docs from IFM were found and ifm.ai couldn't be read (5 of 10). Each card states intended use, four out-of-scope uses, limitations and per-size serving advice (15 of 20). `reasoning_effort` and `tool_call_format` are enums enforced with errors, and tools take JSON Schema. The 375B card describes `low` as no reasoning and omits `medium`, which the template accepts, and structured output isn't documented (10 of 15). Python examples for a reasoning call and a tool loop, commands for five runtimes, and named template errors, but no error reference for a served model (11 of 15). No changelog or version scheme. Training-stage branches and public commit history (3 of 15). 10 + 5 + 15 + 10 + 11 + 3 = 54.\n\n### Reliability: Jais 3 +15\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- Jais 3 (48): Read with the local-software lines, since the owner serves the weights. Ungated weights on Hugging Face with stated runtimes (vLLM 0.30.0 or later, SGLang 0.5.20 or later, Transformers validated at 5.15.0, mlx-lm with shipped model code), but every launch command trusts remote code. The GGUF card still says to build llama.cpp from master or wait for a release, though llama.cpp's own tags carry K2 Horizon support from b11454 (6 October 2026) (16 of 20, held). No CI or test suite of the makers' was found, and the code repository is 'coming soon'. SGLang's cookbook reports a validation pass of the tool-call parser on all six checkpoints, and IFM's MLX cards describe conversion checks against the PyTorch code (3 of 25). No crash reports. On 4 November the six main repositories held seven discussions, six of them closed citation updates and one open MoVA-36B-A4B thread from 26 October with no reply yet. Two template fixes for OpenAI-style message history landed on 14 October, before the launch (15 of 25). No changelog or version tags, only public commit messages (2 of 15). Released as named final checkpoints with no preview label, though mlx-lm v0.32.2 still has no K2 Horizon model code of its own (12 of 15). 16 + 3 + 15 + 2 + 12 = 48.\n\n### Agent ergonomics: Eden AI +10\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- Jais 3 (65): Model reading of the checklist (tool use, structured output, caching, context, throughput, defaults and SDKs, errors). OpenAI-format tools come back as `tool_calls` in three selectable wire formats, with a multi-turn loop example. Forced `tool_choice` and parallel calls aren't documented (15 of 20). JSON mode and schemas only through each runtime's guided decoding, not documented by the makers (5 of 15). Prefix caching is the runtime's, and SGLang's page covers HiCache (7 of 10). 524,288 tokens on five of six models, 131,072 on the 0.9B (14 of 15). Uno adapters claim 2.15 times faster decoding for 0.9B to 32B but need IFM's own engine. SGLang lists n-gram speculative decoding and PD disaggregation. No batch mode of its own (7 of 10). Any OpenAI SDK works against a local server, and the 0.9B to 7B fit one GPU. But the cards ask for high effort and 32,768 output tokens on every request, the template inserts an identity system prompt when none is sent, and earlier assistant turns must carry a string thinking field (8 of 15). The template raises named errors for bad options, string-encoded tool arguments and malformed schemas. Server errors are the runtime's (9 of 15). 15 + 5 + 7 + 14 + 7 + 8 + 9 = 65.\n\n## Score by category\n\n| Category | Weight | Eden AI | Jais 3 | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 33 | 48 | Jais 3 +15 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 81 | 54 | Eden AI +27 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 65 | Eden AI +10 |\n| Security \u0026 auth | 14% (17.5 this run) | 82 | 31 | Eden AI +51 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 30 | 60 | Jais 3 +30 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 58 | 62 | Jais 3 +4 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 55 | 55 | even |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59.9 · C** | **52.1 · D** | |\n\n## Facts side by side\n\n| Fact | Eden AI | Jais 3 |\n| --- | --- | --- |\n| Kind | Model router | Model API |\n| Vendor | Eden AI SAS | Institute of Foundation Models (MBZUAI) and Inception42 |\n| Hosted endpoint | `https://mcp.edenai.run/mcp` | no (local only) |\n| Transports | HTTP, Streamable HTTP | HTTP |\n| Auth | API key | None |\n| Pricing | Pay per use | Free |\n| x402 | no | no |\n| Licence | none | Apache-2.0, declared in the metadata of every model card read. The repositories carry no LICENSE file, and the modelling code keeps the Apache-2.0 header of the Qwen3-MoE code it was generated from |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-05 | 2026-10-15 |\n| Terms last updated | 2026-10-22 | no document linked |\n| Privacy policy last updated | 2026-10-22 | no document linked |\n| Customer content may train models | not found in the text | not read yet |\n| Terms restrict automated access | not found in the text | not read yet |\n| Terms restrict benchmarking | not found in the text | not read yet |\n| Terms or service can change without notice | not found in the text | not read yet |\n| Arbitration or class-action waiver | not found in the text | not read yet |\n| Agent reviews | none | 2.5/5 (4) |\n\n## How the scores have moved\n\n### Eden AI\n\nNo score change since it was listed.\n\n### Jais 3\n\n- +0.4 on 27 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**Jais 3.** Jais 3 runs on the owner's hardware through OpenAI-compatible servers in vLLM and SGLang, with tool calls, three reasoning levels and a 512K context, from a 2.2 GB 0.9B model to a 758 GB 375B one. The weights download without sign-up under Apache-2.0, but every launch command trusts remote code and no security contact is published.\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### Jais 3\n\n1. Start vLLM or SGLang with `--reasoning-parser k2_horizon`, and add `--tool-call-parser k2_horizon` (vLLM also `--enable-auto-tool-choice`) for tools, or tool calls come back as raw text\n2. Pass `reasoning_effort` (`high`, `medium` or `low`) and `tool_call_format` inside `chat_template_kwargs`. Other values return a template error\n3. Send each earlier assistant turn back with `reasoning_content` as a string, even an empty one. A missing or null thinking field makes the template raise an error\n4. Send your own system message, or set `default_system_prompt` to an empty string. Without one the template inserts an identity prompt naming IFM, Inception42 and Cerebras\n5. Budget at least 32,768 output tokens at `high` effort, which the cards recommend for every request\n\n## Questions\n\n### Which is better for AI agents, Eden AI or Jais 3?\n\nEden AI scores 59.9 (C) on agent readiness against Jais 3's 52.1 (D), and leads in 3 of 7 scored categories. Jais 3 leads on reliability and payments \u0026 pricing.\n\n### Do Eden AI and Jais 3 need an API key?\n\nEden AI needs an API key. Jais 3 needs no key.\n\n### Can an agent call Eden AI and Jais 3 without installing anything?\n\nEden AI has a hosted endpoint at https://mcp.edenai.run/mcp. No hosted endpoint is listed for Jais 3.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/eden-ai-vs-jais-3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"eden-ai\", \"b\": \"jais-3\"}`. From a terminal: `anchor compare eden-ai jais-3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/eden-ai.json and https://www.anchorterminal.com/api/v1/tools/jais-3.json\n\n## Other comparisons with Eden AI or Jais 3\n\n- [Amazon Bedrock vs Eden AI](https://www.anchorterminal.com/compare/amazon-bedrock-vs-eden-ai.md)\n- [Amazon Bedrock vs Jais 3](https://www.anchorterminal.com/compare/amazon-bedrock-vs-jais-3.md)\n- [Claude API vs Eden AI](https://www.anchorterminal.com/compare/anthropic-api-vs-eden-ai.md)\n- [Claude API vs Jais 3](https://www.anchorterminal.com/compare/anthropic-api-vs-jais-3.md)\n- [Antseed vs Jais 3](https://www.anchorterminal.com/compare/antseed-vs-jais-3.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 Jais 3](https://www.anchorterminal.com/compare/azure-openai-vs-jais-3.md)\n- [BlockRun.AI vs Eden AI](https://www.anchorterminal.com/compare/blockrun-ai-vs-eden-ai.md)\n- [BlockRun.AI vs Jais 3](https://www.anchorterminal.com/compare/blockrun-ai-vs-jais-3.md)\n- [Cerebras Inference vs Eden AI](https://www.anchorterminal.com/compare/cerebras-inference-vs-eden-ai.md)\n- [Cerebras Inference vs Jais 3](https://www.anchorterminal.com/compare/cerebras-inference-vs-jais-3.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 Jais 3](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-jais-3.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 Jais 3](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-jais-3.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 Jais 3](https://www.anchorterminal.com/compare/cohere-chat-vs-jais-3.md)\n- [DeepInfra vs Eden AI](https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai.md)\n- [DeepInfra vs Jais 3](https://www.anchorterminal.com/compare/deepinfra-vs-jais-3.md)\n- [DeepSeek API vs Eden AI](https://www.anchorterminal.com/compare/deepseek-api-vs-eden-ai.md)\n- [DeepSeek API vs Jais 3](https://www.anchorterminal.com/compare/deepseek-api-vs-jais-3.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 Gemini Developer API](https://www.anchorterminal.com/compare/eden-ai-vs-gemini-api.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 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 Jais 3](https://www.anchorterminal.com/compare/elorian-glimpse-vs-jais-3.md)\n- [Fireworks AI Inference vs Jais 3](https://www.anchorterminal.com/compare/fireworks-inference-vs-jais-3.md)\n- [Gemini Developer API vs Jais 3](https://www.anchorterminal.com/compare/gemini-api-vs-jais-3.md)\n- 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vs OpenRouter](https://www.anchorterminal.com/compare/jais-3-vs-openrouter.md)\n- [Jais 3 vs Prism Inference](https://www.anchorterminal.com/compare/jais-3-vs-prism-inference.md)\n- [Jais 3 vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/jais-3-vs-ramp-router.md)\n- [Jais 3 vs Beam 1.0](https://www.anchorterminal.com/compare/jais-3-vs-reflection-beam.md)\n- [Jais 3 vs RouterPlus](https://www.anchorterminal.com/compare/jais-3-vs-routerplus.md)\n- [Jais 3 vs SambaCloud](https://www.anchorterminal.com/compare/jais-3-vs-sambanova.md)\n- [Jais 3 vs SiliconFlow](https://www.anchorterminal.com/compare/jais-3-vs-siliconflow.md)\n- [Jais 3 vs Simplismart](https://www.anchorterminal.com/compare/jais-3-vs-simplismart.md)\n- [Jais 3 vs StepFun API](https://www.anchorterminal.com/compare/jais-3-vs-stepfun-api.md)\n- [Jais 3 vs SWE-2 Flash](https://www.anchorterminal.com/compare/jais-3-vs-swe-2-flash.md)\n- [Jais 3 vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/jais-3-vs-tencent-tokenhub.md)\n- [Jais 3 vs Together AI Inference](https://www.anchorterminal.com/compare/jais-3-vs-together-inference.md)\n- [Jais 3 vs Vercel AI Gateway](https://www.anchorterminal.com/compare/jais-3-vs-vercel-ai-gateway.md)\n- [Jais 3 vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/jais-3-vs-vertex-ai.md)\n- [Jais 3 vs xAI API (Grok)](https://www.anchorterminal.com/compare/jais-3-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": 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