{ "data": { "a": { "slug": "deepinfra", "name": "DeepInfra", "vendor": "Deep Infra Inc.", "vendorUrl": "https://deepinfra.com", "kind": "model", "category": "inference", "summary": "DeepInfra is a hosted inference API for open-weight and some third-party models, covering chat, embeddings, reranking, image, video and speech. It answers OpenAI-style and Anthropic-style calls at api.deepinfra.com with a Bearer key.", "url": "https://www.anchorterminal.com/tools/deepinfra", "markdownUrl": "https://www.anchorterminal.com/tools/deepinfra.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepinfra.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepinfra.json", "dns": "deepinfra.grade.anchorterminal.com", "repo": "https://github.com/deepinfra/deepinfra-python", "license": "Proprietary service under the DeepInfra Terms of Service. The Python and Node SDKs and the docs repository are MIT", "transports": [ "http" ], "remoteUrl": "https://api.deepinfra.com/v1/openai", "packages": [ { "registry": "pypi", "name": "deepinfra" }, { "registry": "npm", "name": "deepinfra" } ], "auth": "api-key", "authNotes": "Self-serve Bearer key from the dashboard at https://deepinfra.com/dash/api_keys after a browser sign-up with Google, GitHub, email or Okta SSO. The Anthropic-style routes also take the key in `x-api-key`. Keys can carry an IP allowlist and a monthly spending limit, and a key can mint scoped JWTs limited by model, expiry and spend.", "pricing": "usage", "pricingNotes": "Pay per token, image, audio minute or GPU-hour, with no free tier. An account must add a card or prepay. DeepSeek-V4-Flash-0731 costs $0.06 in and $0.18 out per 1M tokens, the priority tier is 1.5x, flex 0.8x and batch 20 per cent off (https://deepinfra.com/pricing, checked 2026-10-08).", "priceSummary": "from $0.06 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in the docs index, the docs repository, the OpenAPI document or the pricing page (checked 2026-10-08).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": 21, "npmWeekly": 1250, "pypiWeekly": 50, "asOf": "2026-10-08" }, "docsUrl": "https://docs.deepinfra.com/", "rateLimitsUrl": "https://docs.deepinfra.com/account/rate-limits", "llmsTxt": "https://docs.deepinfra.com/llms.txt", "openapi": "https://api.deepinfra.com/openapi.json", "capabilities": [ "inference.llm", "inference.open-weights", "embed.text", "rerank", "image.generate", "speech.stt", "speech.tts", "compute.batch" ], "tags": [ "hosted", "model", "open-weights", "usage-based", "card-required", "openapi", "llms-txt", "openai-compatible", "anthropic-compatible", "batch", "prompt-caching", "python", "typescript", "status-page", "soc2" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 63.7, "grade": "B", "agentReady": false, "rank": 503, "ranked": true, "rankOf": 1518, "categoryRank": 21, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 77, "maintenance": 61, "payments": 20, "reliability": 75, "schema": 69, "security": 64, "transparency": 68 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-02" }, "negative": 0, "verdict": "The model list, context sizes and per-token prices are readable without a key, and keys can carry an IP allowlist, a monthly spending cap and model-limited JWTs. Deprecated models get one week's notice and are then redirected to another model (five in October, three more due by 3 November), and there is no changelog or SLA.", "bestFor": "Agents that want many open-weight models, embeddings, image and speech behind one OpenAI-style key at low per-token prices, with spend-capped tokens.", "strengths": [ "`GET /v1/openai/models` answered without a key on 2 November 2026 with 173 models, each with context length, output cap and per-token prices", "Scoped JWTs limit a token to named models, an expiry and a USD spending limit, and each API key can carry an IP allowlist and a monthly cap", "The terms commit to zero data retention and no training on Customer Data, and say that clause controls over the privacy policy and docs", "Batch API at 20 per cent off, a flex tier at 0.8x, prompt caching with cached-input prices and server-side fallback across up to four models", "Status page with 90-day history for the API, the website and 168 models, plus a dated list of scheduled deprecations" ], "weaknesses": [ "A deprecated model gets at least one week's notice, and requests are then forwarded to a replacement model under the old id. Eight models were redirected or scheduled between 8 October and 3 November 2026", "The status feed's 50 newest items are automated major-outage observations on single models, several longer than 24 hours, with no written incident notes", "No free tier. The pricing page says an account must add a card or prepay before using the service", "No changelog, SLA, error reference or `Retry-After` header was found in the reviewed documentation", "The sub-processor list names three companies, while the docs say Google and Anthropic receive data for their models" ], "agentNotes": [ "Call `GET https://api.deepinfra.com/v1/openai/models` at start-up for ids, context sizes and prices. No key is needed", "Check the `model` field of each response. After a deprecation date, requests to the old id are served by a replacement model", "Ask the account owner for a scoped JWT limited to the models and spend the task needs, not the full API key", "Stay under 200 concurrent requests per model. On 429 `engine_overloaded`, retry after a delay, or send `models` with up to four fallbacks", "Never inspect a JWT with `GET /v1/scoped-jwt?jwtoken=`, which puts the token in the URL. Keep credentials in the `Authorization` header" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.7 } ], "editorialScores": { "ergonomics": 77, "maintenance": 61, "payments": 20, "reliability": 75, "schema": 69, "security": 64, "transparency": 60 }, "provenanceScore": 75, "checked": { "categories": { "ergonomics": "2026-11-02", "maintenance": "2026-11-02", "payments": "2026-11-02", "reliability": "2026-11-02", "schema": "2026-11-02", "security": "2026-11-02", "transparency": "2026-11-02" }, "last": "2026-11-02", "lastMoved": "2026-11-02", "oldest": "2026-11-02", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install openai # or: npm install openai", "http": "curl \"https://api.deepinfra.com/v1/openai/chat/completions\" \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer $DEEPINFRA_API_KEY\" \\\n -d '{\"model\":\"deepseek-ai/DeepSeek-V4-Flash-0731\",\"messages\":[{\"role\":\"user\",\"content\":\"Hello\"}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/deepinfra" }, "area": "models", "provenance": { "legalEntity": "Deep Infra Inc.", "domain": "deepinfra.com", "domainRegistered": "2017-12-08", "endpointOnVendorDomain": true, "terms": "https://deepinfra.com/terms", "privacy": "https://deepinfra.com/privacy", "statusPage": "https://status.deepinfra.com", "changelog": "", "securityTxt": "none", "checked": "2026-11-02", "notes": [ "The Terms of Service, last modified 15 October 2026, name Deep Infra Inc., a Delaware corporation, with California law and JAMS arbitration in San Francisco. They are written around Service Orders and govern the services, the API included.", "The Privacy Policy, last modified 15 August 2026, names DeepInfra, Inc., a Delaware corporation, covers the website and the APIs, and has a section on data sent to and returned by the inference service.", "No changelog or release notes page was found in the docs index or the site map.", "https://deepinfra.com/.well-known/security.txt returns 404.", "RDAP for deepinfra.com gives a registration date of 2017-12-08.", "The API answers at api.deepinfra.com, the docs at docs.deepinfra.com and the status page at status.deepinfra.com.", "No SLA was found. The terms incorporate a data processing agreement made available at trust.deepinfra.com, where it is listed among documents available on request." ], "score": 75 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepinfra.json", "live": { "slug": "deepinfra", "probe": { "target": "https://api.deepinfra.com/v1/openai", "method": "get", "lastAt": "2026-11-10T23:46:58.179506218Z", "lastOk": true, "lastStatus": 404, "lastMs": 374, "authRequired": false, "uptime24h": 100, "uptime30d": 99.92, "p50ms24h": 386, "p95ms24h": 526, "samples24h": 244, "samples30d": 7904, "days": [ { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 247 }, { "date": "2026-10-12", "probes": 247, "ok": 247 }, { "date": "2026-10-13", "probes": 248, "ok": 248 }, { "date": "2026-10-14", "probes": 249, "ok": 249 }, { "date": "2026-10-15", "probes": 253, "ok": 253 }, { "date": "2026-10-16", "probes": 252, "ok": 252 }, { "date": "2026-10-17", "probes": 253, "ok": 253 }, { "date": "2026-10-18", "probes": 256, "ok": 256 }, { "date": "2026-10-19", "probes": 255, "ok": 255 }, { "date": "2026-10-20", "probes": 252, "ok": 252 }, { "date": "2026-10-21", "probes": 253, "ok": 253 }, { "date": "2026-10-22", "probes": 253, "ok": 253 }, { "date": "2026-10-23", "probes": 248, "ok": 248 }, { "date": "2026-10-24", "probes": 251, "ok": 251 }, { "date": "2026-10-25", "probes": 250, "ok": 250 }, { "date": "2026-10-26", "probes": 252, "ok": 252 }, { "date": "2026-10-27", "probes": 249, "ok": 249 }, { "date": "2026-10-28", "probes": 251, "ok": 251 }, { "date": "2026-10-29", "probes": 248, "ok": 248 }, { "date": "2026-10-30", "probes": 249, "ok": 249 }, { "date": "2026-10-31", "probes": 251, "ok": 251 }, { "date": "2026-11-01", "probes": 249, "ok": 243 }, { "date": "2026-11-02", "probes": 250, "ok": 250 }, { "date": "2026-11-03", "probes": 248, "ok": 248 }, { "date": "2026-11-04", "probes": 246, "ok": 246 }, { "date": "2026-11-05", "probes": 185, "ok": 185 }, { "date": "2026-11-06", "probes": 241, "ok": 241 }, { "date": "2026-11-07", "probes": 243, "ok": 243 }, { "date": "2026-11-08", "probes": 243, "ok": 243 }, { "date": "2026-11-09", "probes": 243, "ok": 243 }, { "date": "2026-11-10", "probes": 242, "ok": 242 } ], "outages": [ { "start": "2026-11-01T03:07:06.786817404Z", "end": "2026-11-01T03:30:15.593945224Z", "note": "dial tcp 38.101.151.21:443: i/o timeout" } ], "since": "2026-10-09T00:00:00Z" }, "vendorStatus": { "page": "https://status.deepinfra.com", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-10T23:00:29.695591006Z" }, "versions": [ { "registry": "github", "name": "deepinfra/deepinfra-python", "version": "v0.2.1", "released": "2026-08-05", "seenAt": "2026-11-10T03:34:05.200144084Z" }, { "registry": "npm", "name": "deepinfra", "version": "2.0.2", "seenAt": "2026-11-10T03:34:04.405071903Z" }, { "registry": "pypi", "name": "deepinfra", "version": "0.3.0", "released": "2026-08-12", "seenAt": "2026-11-10T03:34:04.212246951Z" } ], "githubStars": 1, "npmWeekly": 446, "pypiWeekly": 125, "securityTxt": { "url": "https://deepinfra.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:07:38.626947736Z" }, "llmsTxt": { "url": "https://docs.deepinfra.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-10T15:42:52.094884319Z" }, "domain": { "domain": "deepinfra.com", "registered": "2017-12-08", "source": "https://rdap.verisign.com/com/v1/domain/deepinfra.com", "checkedAt": "2026-11-08T14:50:31.504723812Z" }, "pages": [ { "url": "https://status.deepinfra.com/", "kind": "deprecations", "status": 200, "checkedAt": "2026-11-10T16:32:07.454774174Z", "changedAt": "2026-11-10T16:32:07.454774174Z", "fingerprint": "7500f58bbb94" }, { "url": "https://deepinfra.com/pricing", "kind": "pricing", "status": 200, "checkedAt": "2026-11-10T16:13:12.169513647Z", "changedAt": "2026-11-10T16:13:12.169513647Z", "fingerprint": "2774819c11d5" }, { "url": "https://deepinfra.com/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-11-10T16:13:14.645923223Z", "changedAt": "2026-11-10T16:13:14.645923223Z", "fingerprint": "3a8b00a6cb93" }, { "url": "https://deepinfra.com/terms", "kind": "terms", "status": 200, "checkedAt": "2026-11-10T16:13:18.213428335Z", "changedAt": "2026-11-10T16:13:18.213428335Z", "fingerprint": "ab556e9a400d" } ], "github": { "repo": "deepinfra/deepinfra-python", "checkedAt": "2026-11-09T10:19:11.303046248Z", "stars": 1, "archived": false, "issuesEnabled": true, "pushedAt": "2026-08-12T08:03:27Z", "openIssues": 7, "openPRs": 2, "issuesOpened90d": 2, "issuesClosed90d": 0, "prsMerged90d": 1, "ci": { "branch": "main", "runs": 7, "success": 6, "failure": 1, "other": 0, "latest": "success", "latestAt": "2026-08-12T06:36:17Z" }, "replies": { "sampled": 13, "unanswered": 7, "medianDays": 3 } }, "updatedAt": "2026-11-10T23:46:58.179506218Z" }, "streak": { "days": 7, "from": "2026-11-03", "last": { "kind": "shutdown", "date": "2026-11-03", "what": "`Meta-Llama-3.1-70B-Instruct-Turbo` scheduled to redirect to `meta-llama/Llama-3.3-70B-Instruct-Turbo`", "source": "https://status.deepinfra.com/" }, "best": 13 } }, "answer": "DeepInfra scores 63.7 (B) on agent readiness against Gemini 4 Carbon's 62.6 (B), and leads in 2 of 7 scored categories. Gemini 4 Carbon leads on schema \u0026 documentation, agent ergonomics and maintenance \u0026 community.", "b": { "slug": "gemini-4-carbon", "name": "Gemini 4 Carbon", "vendor": "Google", "vendorUrl": "https://ai.google.dev", "kind": "model", "category": "inference", "summary": "Gemini 4 Carbon is Google's strongest Gemini model, for long agentic coding, cybersecurity and research work, on the paid tier of the Gemini API with a 1,048,576-token limit shared between input and output.", "url": "https://www.anchorterminal.com/tools/gemini-4-carbon", "markdownUrl": "https://www.anchorterminal.com/tools/gemini-4-carbon.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/gemini-4-carbon.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/gemini-4-carbon.json", "dns": "gemini-4-carbon.grade.anchorterminal.com", "license": "Closed model, used through the Gemini API and Google's apps under Google's terms", "transports": [], "remoteUrl": "https://generativelanguage.googleapis.com/v1beta", "packages": [], "auth": "api-key", "authNotes": "An `x-goog-api-key` from AI Studio on a project with billing, since Carbon is on the paid tier only, per the Gemini API listing and Google's developer post. Google AI Ultra subscribers can also use it in AI Studio and Antigravity. The `gemini-4-carbon-cyber-permissive` variant needs a project approved through Google's Fairwind Program.", "pricing": "usage", "pricingNotes": "Google's announcement gives an introductory $2 per million input tokens and $10 output, cached input 95% off, then $4 and $20, with no end date for the introduction. Paid tier only, no free tier. Every processed token is billed, thinking included (checked 2026-11-10).", "priceSummary": "Pay per use", "where": "hosted", "x402": { "level": "no", "evidence": "No machine payment. Paid use needs billing on a Cloud project, per the Gemini API listing (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://docs.cloud.google.com/gemini-enterprise-agent-platform/models/guides/gemini-4-carbon", "llmsTxt": "https://ai.google.dev/gemini-api/docs/llms.txt", "capabilities": [ "inference.llm" ], "tags": [ "official", "hosted", "model", "multimodal", "reasoning", "long-context", "tool-calling", "usage-priced", "card-required", "closed-source", "llms-txt", "new" ], "lastRelease": "2026-11-10", "graded": true, "anchor": { "graded": true, "score": 62.6, "grade": "B", "agentReady": false, "rank": 555, "ranked": true, "rankOf": 1518, "categoryRank": 22, "methodology": "0.7", "run": "2026-10-01", "scores": { "ergonomics": 93, "maintenance": 83, "payments": 20, "reliability": 40, "schema": 85, "security": 55, "transparency": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-11-10" }, "negative": 0, "verdict": "Google's strongest model on the same Gemini API as Argon, with a 1M-token budget, function calling and structured output, at an introductory $2 and $10 per million tokens. Paid tier only, sampling parameters are ignored or refused, its launch stage isn't stated, and the price rises to $4 and $20 at a date Google hasn't given.", "bestFor": "Agents on the hardest long tasks (multi-step debugging, large codebases, open-ended research, security work) that can pay frontier prices and already use the Gemini API.", "strengths": [ "Introductory price of $2 input and $10 output per million tokens, with cached input 95% off", "1,048,576 tokens shared between input and output, with long decodes for big outputs", "Function calling, structured output, Google Search grounding and background execution", "Thinking levels LOW, MEDIUM (default) and HIGH, and implicit caching on by default", "The same API, keys and SDKs as Gemini 4 Argon, so switching is a model ID" ], "weaknesses": [ "Paid tier only, with no free tier to try it on", "temperature, top_p and top_k are ignored, and thinking_budget, MINIMAL, penalties and extra candidates return a 400", "No explicit context caching, and prefilled model turns aren't accepted", "The price goes to $4 and $20 after an introduction with no end date given", "Benchmark figures are Google's own, and the launch stage isn't stated" ], "agentNotes": [ "Call generateContent at https://generativelanguage.googleapis.com/v1beta with model gemini-4-carbon and an x-goog-api-key on a billed project", "Set thinking_level (LOW, MEDIUM or HIGH) instead of thinking_budget, and don't send temperature, top_p or top_k", "Count output against the same 1,048,576-token limit as the input, and set max_output_tokens for long answers", "Put static prompt content first so implicit caching applies; explicit caches aren't supported", "Use gemini-4-argon for most coding and knowledge work, and Carbon for the hardest long tasks, as Google suggests" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.6 } ], "editorialScores": { "ergonomics": 93, "maintenance": 83, "payments": 20, "reliability": 40, "schema": 85, "security": 55, "transparency": 55 }, "provenanceScore": 89, "checked": { "categories": { "ergonomics": "2026-11-10", "maintenance": "2026-11-10", "payments": "2026-11-10", "reliability": "2026-11-10", "schema": "2026-11-10", "security": "2026-11-10", "transparency": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "http": "curl \"https://generativelanguage.googleapis.com/v1beta/models/gemini-4-carbon:generateContent\" \\\n -H \"x-goog-api-key: $GEMINI_API_KEY\" -H \"content-type: application/json\" \\\n -d '{\"contents\":[{\"parts\":[{\"text\":\"hello\"}]}]}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/gemini-4-carbon" }, "sameCompany": [ "vertex-ai", "gemini-embedding", "vertex-ai-tuning", "cloud-run-gpus", "google-model-armor", "google-imagen", "google-veo", "google-lyria", "google-speech-to-text", "google-cloud-tts", "gemini-live", "google-adk", "google-memory-bank", "google-secret-manager", "google-cloud-document-ai", "google-weather-api", "chrome-devtools-mcp", "google-maps-platform", "google-cloud-translation", "google-calendar-api", "firebase-cloud-messaging", "google-cloud-storage", "google-drive-api", "gemini-enterprise", "gemini-cli", "google-search-console", "google-ads-api", "google-forms", "google-sheets-api", "gmail-api", "google-people-api" ], "sameModel": [ "gemini-api" ], "area": "models", "unitPrices": [ { "item": "Gemini 4 Carbon input", "unit": "1m-tokens", "usd": 2, "note": "Introductory; $4 after, date not given" }, { "item": "Gemini 4 Carbon cached input", "unit": "1m-tokens", "usd": 0.1, "note": "95% off input" }, { "item": "Gemini 4 Carbon output", "unit": "1m-tokens", "usd": 10, "note": "Introductory; $20 after" } ], "provenance": { "legalEntity": "Google LLC", "domain": "google.com", "domainRegistered": "1997-09-15", "domainNote": "The endpoint is on googleapis.com, Google's API domain. google.com was registered in 1997.", "endpointOnVendorDomain": true, "terms": "https://ai.google.dev/gemini-api/terms", "privacy": "https://policies.google.com/privacy", "statusPage": "https://aistudio.google.com/status", "changelog": "https://ai.google.dev/gemini-api/docs/changelog", "securityTxt": "valid", "checked": "2026-11-10", "score": 89 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/gemini-4-carbon.json", "live": { "slug": "gemini-4-carbon", "probe": { "target": "https://generativelanguage.googleapis.com/v1beta", "method": "get", "lastAt": "2026-11-10T23:47:03.904361517Z", "lastOk": true, "lastStatus": 404, "lastMs": 35, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 41, "p95ms24h": 130, "samples24h": 63, "samples30d": 63, "days": [ { "date": "2026-11-10", "probes": 63, "ok": 63 } ], "since": "2026-11-10T17:42:25.910455739Z" }, "vendorStatus": { "page": "https://aistudio.google.com/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-11-10T23:00:42.593790699Z" }, "updatedAt": "2026-11-10T23:47:03.904361517Z" }, "streak": { "days": 0, "from": "2026-11-10", "sinceWatching": true, "best": 0 } }, "edges": [ { "by": 35, "category": "reliability", "leads": "deepinfra", "name": "Reliability", "reasons": { "a": "Hosted reading. Own status page at status.deepinfra.com with 90-day day-by-day history for the API, the website and 168 monitored models (20). On 2 November 2026 the API component shows 100 per cent over 86.7 measured days and the website 99.99 per cent, with one 8-minute degradation on 19 August 2026. The Atom feed's 50 newest items, 20 August to 30 October 2026, are all automated major-outage observations on single models, \"not an announced incident\", among them MiMo-V2.5 from 27 August to 4 September, Qwen3-TTS for 37 and 32 hours, Nemotron-3-Ultra for 25 hours and a run of short Nemotron-3.5-Lightning outages in early September. No written incident touched the API. Each observation is one model, partial under this run's rule, as together-inference's single-model monitor downtime was read, so minor only (20). The 8 October reading of 15 sat between checklist values. 200 concurrent requests per model, published (15). 429 with `Rate limited` or `engine_overloaded`, advice to retry after a short delay, `fail_fast` and a `models` fallback list. No `Retry-After` header or backoff figures were found (10 of 15). No SLA found. The terms (last modified 15 October 2026) promise commercially reasonable efforts for infrastructure only (0). Generally available (10). 20 + 20 + 15 + 10 + 0 + 10 = 75.", "b": "Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Status page at aistudio.google.com/status with no readable components or history (10 of 20) and no readable incident record (5 of 30). Rate-limit tiers and spend limits published, per-model limits in AI Studio (5 of 15). Backoff guidance for 429 and 503 and SDK retries (15). No SLA (0). Carbon's launch stage isn't stated in Google's developer post, the Antigravity post or the Cloud developer guide read on 10 November, and the model ID carries no preview label, so half (5 of 10, a judgement call; Argon's GA earned the platform 10). 10 + 5 + 5 + 15 + 0 + 5 = 40." }, "scores": { "a": 75, "b": 40 } }, { "by": 22, "category": "maintenance", "leads": "gemini-4-carbon", "name": "Maintenance \u0026 community", "reasons": { "a": "Model reading. The docs repository changed on 26 October 2026, and on 22 October documented Nemotron 3.5 image embeddings and reranker scores (30). Deprecated models get at least one week's notice by email, then requests are forwarded to a replacement (5 of 12). Churn. In the 90 days to 2 November four redirect dates passed (8, 9, 12 and 25 October, five models), Kimi-K2.7-Code was deprecated before 28 September, and three more are scheduled for 2 and 3 November. Three or more removal dates in 90 days give 0, as groq and fireworks-inference were read (0 of 8, was 4 when the five were only scheduled). No changelog. Support is by Discord and a feedback address, and the docs repository is public (7 of 15). Replies on the SDK repositories were not examined (3 of 10). Python `deepinfra` 0.3.0 from 12 August 2026 with a changelog. The Node repository tagged 2.1.0 on 1 September 2026, and npm couldn't be read on 2 November, so its 8 October reading of 2.0.2 from 8 May 2024 stands (9 of 15). CI workflows in both SDK repositories (7 of 10). 30 + 5 + 0 + 7 + 3 + 9 + 7 = 61.", "b": "Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released to developers on 10 November 2026, with the platform's changelog (30). Deprecations with earliest dates and no stated minimum notice (4 of 12). Shutdowns in the last 90 days kept to their table (4 of 8). Dated changelog (15). SDK replies not sampled (5 of 10). Current SDKs (15). Runtimes stated (10). 30 + 4 + 4 + 15 + 5 + 15 + 10 = 83." }, "scores": { "a": 61, "b": 83 } }, { "by": 16, "category": "schema", "leads": "gemini-4-carbon", "name": "Schema \u0026 documentation", "reasons": { "a": "Model reading. Public OpenAPI 3.1.0 document at api.deepinfra.com/openapi.json with 131 paths, 157 operations and 257 schemas on 2 November 2026. `llms.txt` now links that document, though the docs host's own copy still returns Mintlify's sample plant store document, and the spec lists chat at `/v1/chat/completions` while every docs example uses `/v1/openai/chat/completions` (22 of 25). `llms.txt` with 224 links and a Markdown copy of every docs page (10). Guide pages for tool calling, structured output, caching, batch and service tiers say when to use each. Many reference operations have a name and no description (14 of 20). Typed parameters with 49 enums, 31 chat properties and two required (11 of 15). Curl, Python and JavaScript examples on each page. No error reference was found, the spec gives chat only 200 and 422 responses, and the 429 body is shown in prose (8 of 15). No changelog was found. The spec version reads 1.0.0, and the docs repository on GitHub has a public commit history (4 of 15).", "b": "Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Discovery document for GenerateContent (20 of 25). llms.txt with Markdown twins (10). The Cloud developer guide says what Carbon takes and refuses, the parameters that return 400 or are ignored, and how to migrate (15 of 20). Structured output to a JSON Schema with some keywords not enforced (10 of 15). Examples and an errors page (15). Dated changelog and model versions (15). 20 + 10 + 15 + 10 + 15 + 15 = 85." }, "scores": { "a": 69, "b": 85 } }, { "by": 16, "category": "ergonomics", "leads": "gemini-4-carbon", "name": "Agent ergonomics", "reasons": { "a": "Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `tool_choice` of none, auto, required or a named function, and streaming. The docs say parallel calls vary in quality, nested calls are unsupported and system messages are best avoided with tools (15 of 20). `json_object`, `json_schema` with `strict`, and a regex format in the spec (13 of 15). Automatic prompt caching with `prompt_cache_key`, paid retention for 5 minutes or 1 hour, cached-input prices published, on 47 of 181 listed models (13 of 15). Context of 1,048,576 tokens on DeepSeek V4, Kimi K3 and GLM 5.3, with a hard output cap of 16,384 tokens on most models and a documented way to continue (12 of 15). Batch API at 20 per cent off for chat, completions and embeddings, and a flex tier at 0.8x (10). The OpenAI and Anthropic clients work with a changed base URL. The Python package `deepinfra` 0.3.0 dates from 12 August 2026, and npm listed 2.0.2 from May 2024 on 8 October (7 of 10). 429 carries an `engine_overloaded` code. Native routes return a bare `error` string, and no list of codes was found (7 of 15).", "b": "Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Function calling, with forced calls by FunctionCallingConfig on the API (20). Structured output (13 of 15). Implicit caching with durable caching on by default, cached input 95% off, explicit caches not supported, per the Cloud guide read on 10 November (15). 1,048,576 tokens shared between input and output (15). Batch at half price on the platform (10). Python and JavaScript SDKs (10). Error codes with backoff guidance (10 of 15). 20 + 13 + 15 + 15 + 10 + 10 + 10 = 93." }, "scores": { "a": 77, "b": 93 } }, { "by": 9, "category": "security", "leads": "deepinfra", "name": "Security \u0026 auth", "reasons": { "a": "Model reading. Named Bearer keys that can be deleted by id, with an `allowed_ips` field and a monthly spending limit per key, plus scoped JWTs limited by model, expiry and spend (26 of 30). Less 10 because `GET /v1/scoped-jwt?jwtoken=` is still the documented way to inspect a JWT and puts a live token in the query string. The two deprecated routes that carried the API token in the URL path are no longer in the OpenAPI document (16 of 30). The terms say Customer Data is not used to train, fine-tune or improve any model. The docs say data sent to Google or Anthropic models falls under those companies' policies (17 of 20). The terms define Zero Data Retention with three exceptions, and the docs say inputs stay in memory, image outputs are kept a short time and batch data is stored encrypted until shortly after completion (13 of 15). Usage per key, request costs and deployment logs are in the API. No account audit log was found (8 of 15). The trust centre at trust.deepinfra.com now loads and lists SOC 2, ISO 27001 and HIPAA as compliant, with reports and policies on request, and the privacy policy says the same. No security.txt, disclosure policy or bug bounty was found (10 of 20, as other listings with certification claims and no disclosure route).", "b": "Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Keys bound to a service account and restricted to the Gemini API, with no scopes and a token accepted in the URL (15 of 30, the platform's deduction). Carbon is on the paid tier only, and Google's terms say paid-tier content isn't used to improve products; AI Studio use under a Google AI Ultra subscription falls under terms we didn't read, so 15 of 20 (a judgement call; the platform's 10 is for its free tier). Abuse retention with no number, and new paid projects store requests by default since 26 October (5 of 15). Request logs for billed projects (15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 15 + 5 + 15 + 5 = 55." }, "scores": { "a": 64, "b": 55 } } ], "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Deep Infra Inc.", "b": "Google", "name": "Vendor" }, { "a": "https://api.deepinfra.com/v1/openai", "b": "https://generativelanguage.googleapis.com/v1beta", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "", "name": "Transports" }, { "a": "API key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Proprietary service under the DeepInfra Terms of Service. The Python and Node SDKs and the docs repository are MIT", "b": "Closed model, used through the Gemini API and Google's apps under Google's terms", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-26", "b": "2026-11-10", "name": "Last release" }, { "a": "2026-08-17", "b": "2026-04-28", "name": "Terms last updated" }, { "a": "2026-08-15", "b": "2026-10-01", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "yes", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "yes", "name": "Terms restrict automated access" }, { "a": "yes", "b": "yes", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "yes", "b": "not found in the text", "name": "Arbitration or class-action waiver" }, { "a": "21 stars, 1.2k npm/wk, 50 PyPI/wk", "b": "none", "name": "Popularity" }, { "a": "3.5/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "DeepInfra scores 63.7 (B) on agent readiness against Gemini 4 Carbon's 62.6 (B), and leads in 2 of 7 scored categories. Gemini 4 Carbon leads on schema \u0026 documentation, agent ergonomics and maintenance \u0026 community.", "question": "Which is better for AI agents, DeepInfra or Gemini 4 Carbon?" }, { "answer": "Both need an API key.", "question": "Do DeepInfra and Gemini 4 Carbon need an API key?" }, { "answer": "Yes. DeepInfra has a hosted endpoint at https://api.deepinfra.com/v1/openai and Gemini 4 Carbon at https://generativelanguage.googleapis.com/v1beta.", "question": "Can an agent call DeepInfra and Gemini 4 Carbon without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 75 against 40", "Security \u0026 auth, 64 against 55" ], "also": null, "goodFor": "Agents that want many open-weight models, embeddings, image and speech behind one OpenAI-style key at low per-token prices, with spend-capped tokens.", "slug": "deepinfra", "watchFor": "A deprecated model gets at least one week's notice, and requests are then forwarded to a replacement model under the old id. Eight models were redirected or scheduled between 8 October and 3 November 2026" }, { "aheadOn": [ "Schema \u0026 documentation, 85 against 69", "Agent ergonomics, 93 against 77", "Maintenance \u0026 community, 83 against 61" ], "also": null, "goodFor": "Agents on the hardest long tasks (multi-step debugging, large codebases, open-ended research, security work) that can pay frontier prices and already use the Gemini API.", "slug": "gemini-4-carbon", "watchFor": "Paid tier only, with no free tier to try it on" } ], "history": { "deepinfra": [ { "causes": [ "product", "correction" ], "date": "2026-11-02", "delta": 0.6, "from": 63.1, "fromGrade": "B", "to": 63.7, "toGrade": "B" }, { "causes": [ "method" ], "date": "2026-10-27", "delta": 0.1, "from": 63, "fromGrade": "B", "to": 63.1, "toGrade": "B" } ], "gemini-4-carbon": null }, "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": 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"https://www.anchorterminal.com/compare/deepinfra-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-stepfun-api.json", "title": "DeepInfra vs StepFun API", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-swe-2-flash.json", "title": "DeepInfra vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-tencent-tokenhub.json", "title": "DeepInfra vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-together-inference.json", "title": "DeepInfra vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-vercel-ai-gateway.json", "title": "DeepInfra vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai.json", "title": "DeepInfra vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-xai-api.json", "title": "DeepInfra vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-xai-api" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-4-carbon.json", "title": "DeepSeek API vs Gemini 4 Carbon", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-4-carbon" }, { "json": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-4-carbon.json", "title": "Eden AI vs Gemini 4 Carbon", "url": "https://www.anchorterminal.com/compare/eden-ai-vs-gemini-4-carbon" }, { "json": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-4-carbon.json", "title": "Glimpse vs Gemini 4 Carbon", "url": "https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-4-carbon" }, { "json": "https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-4-carbon.json", "title": "Fireworks AI Inference vs Gemini 4 Carbon", "url": "https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-4-carbon" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-gemini-api.json", "title": "Gemini 4 Carbon vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-groq.json", "title": "Gemini 4 Carbon vs GroqCloud", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-hugging-face-inference-providers.json", "title": "Gemini 4 Carbon vs Hugging Face Inference Providers", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-hugging-face-inference-providers" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-inkling-2-small.json", "title": "Gemini 4 Carbon vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-jais-3.json", "title": "Gemini 4 Carbon vs Jais 3", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-jais-3" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-meta-model-api.json", "title": "Gemini 4 Carbon vs Meta Model API", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-minimax-api.json", "title": "Gemini 4 Carbon vs MiniMax API", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-minimax-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-mistral-api.json", "title": "Gemini 4 Carbon vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-ember.json", "title": "Gemini 4 Carbon vs Muse Ember", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-ember" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-spark-open.json", "title": "Gemini 4 Carbon vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-nemotron-3-5-super-vl.json", "title": "Gemini 4 Carbon vs Nemotron 3.5 Super VL", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-nemotron-3-5-super-vl" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-novita-ai.json", "title": "Gemini 4 Carbon vs Novita AI", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-openai-api.json", "title": "Gemini 4 Carbon vs OpenAI API", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-openrouter.json", "title": "Gemini 4 Carbon vs OpenRouter", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-prism-inference.json", "title": "Gemini 4 Carbon vs Prism Inference", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-ramp-router.json", "title": "Gemini 4 Carbon vs Router.com (Ramp Router)", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-ramp-router" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-reflection-beam.json", "title": "Gemini 4 Carbon vs Beam 1.0", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-reflection-beam" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-routerplus.json", "title": "Gemini 4 Carbon vs RouterPlus", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-sambanova.json", "title": "Gemini 4 Carbon vs SambaCloud", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-siliconflow.json", "title": "Gemini 4 Carbon vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-simplismart.json", "title": "Gemini 4 Carbon vs Simplismart", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-simplismart" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-stepfun-api.json", "title": "Gemini 4 Carbon vs StepFun API", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-swe-2-flash.json", "title": "Gemini 4 Carbon vs SWE-2 Flash", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-swe-2-flash" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-tencent-tokenhub.json", "title": "Gemini 4 Carbon vs Tencent Cloud TokenHub", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-tencent-tokenhub" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-together-inference.json", "title": "Gemini 4 Carbon vs Together AI Inference", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-together-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-vercel-ai-gateway.json", "title": "Gemini 4 Carbon vs Vercel AI Gateway", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-vercel-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-vertex-ai.json", "title": "Gemini 4 Carbon vs Vertex AI (Gemini Enterprise Agent Platform)", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-vertex-ai" }, { "json": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-xai-api.json", "title": "Gemini 4 Carbon vs xAI API (Grok)", "url": "https://www.anchorterminal.com/compare/gemini-4-carbon-vs-xai-api" } ], "panel": null, "scores": [ { "by": 35, "deepinfra": 75, "edge": "deepinfra", "gemini-4-carbon": 40, "key": "reliability", "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 16, "deepinfra": 69, "edge": "gemini-4-carbon", "gemini-4-carbon": 85, "key": "schema", "name": "Schema \u0026 documentation", "weight": 13 }, { "by": 16, "deepinfra": 77, "edge": "gemini-4-carbon", "gemini-4-carbon": 93, "key": "ergonomics", "name": "Agent ergonomics", "weight": 13 }, { "by": 9, "deepinfra": 64, "edge": "deepinfra", "gemini-4-carbon": 55, "key": "security", "name": "Security \u0026 auth", "weight": 14 }, { "by": 0, "deepinfra": 20, "edge": "", "gemini-4-carbon": 20, "key": "payments", "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 22, "deepinfra": 61, "edge": "gemini-4-carbon", "gemini-4-carbon": 83, "key": "maintenance", "name": "Maintenance \u0026 community", "weight": 7 }, { "by": 4, "deepinfra": 68, "edge": "gemini-4-carbon", "gemini-4-carbon": 72, "key": "transparency", "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "DeepInfra scores 63.7 (B) on agent readiness against Gemini 4 Carbon's 62.6 (B), and leads in 2 of 7 scored categories. Gemini 4 Carbon leads on schema \u0026 documentation, agent ergonomics and maintenance \u0026 community. Both do llm inference.", "tasks": null, "tutorials": [], "verdicts": { "deepinfra": "The model list, context sizes and per-token prices are readable without a key, and keys can carry an IP allowlist, a monthly spending cap and model-limited JWTs. Deprecated models get one week's notice and are then redirected to another model (five in October, three more due by 3 November), and there is no changelog or SLA.", "gemini-4-carbon": "Google's strongest model on the same Gemini API as Argon, with a 1M-token budget, function calling and structured output, at an introductory $2 and $10 per million tokens. Paid tier only, sampling parameters are ignored or refused, its launch stage isn't stated, and the price rises to $4 and $20 at a date Google hasn't given." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-carbon", "json": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-carbon.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-carbon.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-carbon.min.md" }, "markdown": "DeepInfra scores 63.7 (B) on agent readiness against Gemini 4 Carbon's 62.6 (B), and leads in 2 of 7 scored categories. Gemini 4 Carbon leads on schema \u0026 documentation, agent ergonomics and maintenance \u0026 community. Both do llm inference.\n\n- DeepInfra: grade B, 63.7/100, rank #503 of 1518. Markdown https://www.anchorterminal.com/tools/deepinfra.md · JSON https://www.anchorterminal.com/api/v1/tools/deepinfra.json\n- Gemini 4 Carbon: grade B, 62.6/100, rank #555 of 1518. Markdown https://www.anchorterminal.com/tools/gemini-4-carbon.md · JSON https://www.anchorterminal.com/api/v1/tools/gemini-4-carbon.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 1000 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### DeepInfra (B)\n\nGood for: Agents that want many open-weight models, embeddings, image and speech behind one OpenAI-style key at low per-token prices, with spend-capped tokens.\n\nAhead on:\n- Reliability, 75 against 40\n- Security \u0026 auth, 64 against 55\n\nWatch for: A deprecated model gets at least one week's notice, and requests are then forwarded to a replacement model under the old id. Eight models were redirected or scheduled between 8 October and 3 November 2026\n\n### Gemini 4 Carbon (B)\n\nGood for: Agents on the hardest long tasks (multi-step debugging, large codebases, open-ended research, security work) that can pay frontier prices and already use the Gemini API.\n\nAhead on:\n- Schema \u0026 documentation, 85 against 69\n- Agent ergonomics, 93 against 77\n- Maintenance \u0026 community, 83 against 61\n\nWatch for: Paid tier only, with no free tier to try it on\n\n\n## Why the edge\n\n### Reliability: DeepInfra +35\n\n- DeepInfra (75): Hosted reading. Own status page at status.deepinfra.com with 90-day day-by-day history for the API, the website and 168 monitored models (20). On 2 November 2026 the API component shows 100 per cent over 86.7 measured days and the website 99.99 per cent, with one 8-minute degradation on 19 August 2026. The Atom feed's 50 newest items, 20 August to 30 October 2026, are all automated major-outage observations on single models, \"not an announced incident\", among them MiMo-V2.5 from 27 August to 4 September, Qwen3-TTS for 37 and 32 hours, Nemotron-3-Ultra for 25 hours and a run of short Nemotron-3.5-Lightning outages in early September. No written incident touched the API. Each observation is one model, partial under this run's rule, as together-inference's single-model monitor downtime was read, so minor only (20). The 8 October reading of 15 sat between checklist values. 200 concurrent requests per model, published (15). 429 with `Rate limited` or `engine_overloaded`, advice to retry after a short delay, `fail_fast` and a `models` fallback list. No `Retry-After` header or backoff figures were found (10 of 15). No SLA found. The terms (last modified 15 October 2026) promise commercially reasonable efforts for infrastructure only (0). Generally available (10). 20 + 20 + 15 + 10 + 0 + 10 = 75.\n- Gemini 4 Carbon (40): Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Status page at aistudio.google.com/status with no readable components or history (10 of 20) and no readable incident record (5 of 30). Rate-limit tiers and spend limits published, per-model limits in AI Studio (5 of 15). Backoff guidance for 429 and 503 and SDK retries (15). No SLA (0). Carbon's launch stage isn't stated in Google's developer post, the Antigravity post or the Cloud developer guide read on 10 November, and the model ID carries no preview label, so half (5 of 10, a judgement call; Argon's GA earned the platform 10). 10 + 5 + 5 + 15 + 0 + 5 = 40.\n\n### Maintenance \u0026 community: Gemini 4 Carbon +22\n\n- DeepInfra (61): Model reading. The docs repository changed on 26 October 2026, and on 22 October documented Nemotron 3.5 image embeddings and reranker scores (30). Deprecated models get at least one week's notice by email, then requests are forwarded to a replacement (5 of 12). Churn. In the 90 days to 2 November four redirect dates passed (8, 9, 12 and 25 October, five models), Kimi-K2.7-Code was deprecated before 28 September, and three more are scheduled for 2 and 3 November. Three or more removal dates in 90 days give 0, as groq and fireworks-inference were read (0 of 8, was 4 when the five were only scheduled). No changelog. Support is by Discord and a feedback address, and the docs repository is public (7 of 15). Replies on the SDK repositories were not examined (3 of 10). Python `deepinfra` 0.3.0 from 12 August 2026 with a changelog. The Node repository tagged 2.1.0 on 1 September 2026, and npm couldn't be read on 2 November, so its 8 October reading of 2.0.2 from 8 May 2024 stands (9 of 15). CI workflows in both SDK repositories (7 of 10). 30 + 5 + 0 + 7 + 3 + 9 + 7 = 61.\n- Gemini 4 Carbon (83): Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Released to developers on 10 November 2026, with the platform's changelog (30). Deprecations with earliest dates and no stated minimum notice (4 of 12). Shutdowns in the last 90 days kept to their table (4 of 8). Dated changelog (15). SDK replies not sampled (5 of 10). Current SDKs (15). Runtimes stated (10). 30 + 4 + 4 + 15 + 5 + 15 + 10 = 83.\n\n### Schema \u0026 documentation: Gemini 4 Carbon +16\n\n- DeepInfra (69): Model reading. Public OpenAPI 3.1.0 document at api.deepinfra.com/openapi.json with 131 paths, 157 operations and 257 schemas on 2 November 2026. `llms.txt` now links that document, though the docs host's own copy still returns Mintlify's sample plant store document, and the spec lists chat at `/v1/chat/completions` while every docs example uses `/v1/openai/chat/completions` (22 of 25). `llms.txt` with 224 links and a Markdown copy of every docs page (10). Guide pages for tool calling, structured output, caching, batch and service tiers say when to use each. Many reference operations have a name and no description (14 of 20). Typed parameters with 49 enums, 31 chat properties and two required (11 of 15). Curl, Python and JavaScript examples on each page. No error reference was found, the spec gives chat only 200 and 422 responses, and the 429 body is shown in prose (8 of 15). No changelog was found. The spec version reads 1.0.0, and the docs repository on GitHub has a public commit history (4 of 15).\n- Gemini 4 Carbon (85): Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Discovery document for GenerateContent (20 of 25). llms.txt with Markdown twins (10). The Cloud developer guide says what Carbon takes and refuses, the parameters that return 400 or are ignored, and how to migrate (15 of 20). Structured output to a JSON Schema with some keywords not enforced (10 of 15). Examples and an errors page (15). Dated changelog and model versions (15). 20 + 10 + 15 + 10 + 15 + 15 = 85.\n\n### Agent ergonomics: Gemini 4 Carbon +16\n\n- DeepInfra (77): Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Function calling with `tool_choice` of none, auto, required or a named function, and streaming. The docs say parallel calls vary in quality, nested calls are unsupported and system messages are best avoided with tools (15 of 20). `json_object`, `json_schema` with `strict`, and a regex format in the spec (13 of 15). Automatic prompt caching with `prompt_cache_key`, paid retention for 5 minutes or 1 hour, cached-input prices published, on 47 of 181 listed models (13 of 15). Context of 1,048,576 tokens on DeepSeek V4, Kimi K3 and GLM 5.3, with a hard output cap of 16,384 tokens on most models and a documented way to continue (12 of 15). Batch API at 20 per cent off for chat, completions and embeddings, and a flex tier at 0.8x (10). The OpenAI and Anthropic clients work with a changed base URL. The Python package `deepinfra` 0.3.0 dates from 12 August 2026, and npm listed 2.0.2 from May 2024 on 8 October (7 of 10). 429 carries an `engine_overloaded` code. Native routes return a bare `error` string, and no list of codes was found (7 of 15).\n- Gemini 4 Carbon (93): Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Function calling, with forced calls by FunctionCallingConfig on the API (20). Structured output (13 of 15). Implicit caching with durable caching on by default, cached input 95% off, explicit caches not supported, per the Cloud guide read on 10 November (15). 1,048,576 tokens shared between input and output (15). Batch at half price on the platform (10). Python and JavaScript SDKs (10). Error codes with backoff guidance (10 of 15). 20 + 13 + 15 + 15 + 10 + 10 + 10 = 93.\n\n### Security \u0026 auth: DeepInfra +9\n\n- DeepInfra (64): Model reading. Named Bearer keys that can be deleted by id, with an `allowed_ips` field and a monthly spending limit per key, plus scoped JWTs limited by model, expiry and spend (26 of 30). Less 10 because `GET /v1/scoped-jwt?jwtoken=` is still the documented way to inspect a JWT and puts a live token in the query string. The two deprecated routes that carried the API token in the URL path are no longer in the OpenAPI document (16 of 30). The terms say Customer Data is not used to train, fine-tune or improve any model. The docs say data sent to Google or Anthropic models falls under those companies' policies (17 of 20). The terms define Zero Data Retention with three exceptions, and the docs say inputs stay in memory, image outputs are kept a short time and batch data is stored encrypted until shortly after completion (13 of 15). Usage per key, request costs and deployment logs are in the API. No account audit log was found (8 of 15). The trust centre at trust.deepinfra.com now loads and lists SOC 2, ISO 27001 and HIPAA as compliant, with reports and policies on request, and the privacy policy says the same. No security.txt, disclosure policy or bug bounty was found (10 of 20, as other listings with certification claims and no disclosure route).\n- Gemini 4 Carbon (55): Model reading for gemini-4-carbon on the Gemini API. Platform facts are the Gemini API listing's (gemini-api, re-read 1 November), carried over as RULES.md allows for a model on an already graded platform. Keys bound to a service account and restricted to the Gemini API, with no scopes and a token accepted in the URL (15 of 30, the platform's deduction). Carbon is on the paid tier only, and Google's terms say paid-tier content isn't used to improve products; AI Studio use under a Google AI Ultra subscription falls under terms we didn't read, so 15 of 20 (a judgement call; the platform's 10 is for its free tier). Abuse retention with no number, and new paid projects store requests by default since 26 October (5 of 15). Request logs for billed projects (15). security.txt and a reward programme, no certification naming the API (5 of 20). 15 + 15 + 5 + 15 + 5 = 55.\n\n## Score by category\n\n| Category | Weight | DeepInfra | Gemini 4 Carbon | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 75 | 40 | DeepInfra +35 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 69 | 85 | Gemini 4 Carbon +16 |\n| Agent ergonomics | 13% (16.2 this run) | 77 | 93 | Gemini 4 Carbon +16 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 55 | DeepInfra +9 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 20 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 61 | 83 | Gemini 4 Carbon +22 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 68 | 72 | Gemini 4 Carbon +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **63.7 · B** | **62.6 · B** | |\n\n## Facts side by side\n\n| Fact | DeepInfra | Gemini 4 Carbon |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Deep Infra Inc. | Google |\n| Hosted endpoint | `https://api.deepinfra.com/v1/openai` | `https://generativelanguage.googleapis.com/v1beta` |\n| Transports | HTTP | |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| Licence | Proprietary service under the DeepInfra Terms of Service. The Python and Node SDKs and the docs repository are MIT | Closed model, used through the Gemini API and Google's apps under Google's terms |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-26 | 2026-11-10 |\n| Terms last updated | 2026-08-17 | 2026-04-28 |\n| Privacy policy last updated | 2026-08-15 | 2026-10-01 |\n| Customer content may train models | not found in the text | yes |\n| Terms restrict automated access | not found in the text | yes |\n| Terms restrict benchmarking | yes | yes |\n| Terms or service can change without notice | not found in the text | not found in the text |\n| Arbitration or class-action waiver | yes | not found in the text |\n| Popularity | 21 stars, 1.2k npm/wk, 50 PyPI/wk | none |\n| Agent reviews | 3.5/5 (4) | none |\n\n## How the scores have moved\n\n### DeepInfra\n\n- +0.6 on 2 November 2026: product, correction\n- +0.1 on 27 October 2026: checklist\n\n### Gemini 4 Carbon\n\nNo score change since it was listed.\n\n## Verdicts\n\n**DeepInfra.** The model list, context sizes and per-token prices are readable without a key, and keys can carry an IP allowlist, a monthly spending cap and model-limited JWTs. Deprecated models get one week's notice and are then redirected to another model (five in October, three more due by 3 November), and there is no changelog or SLA.\n\n**Gemini 4 Carbon.** Google's strongest model on the same Gemini API as Argon, with a 1M-token budget, function calling and structured output, at an introductory $2 and $10 per million tokens. Paid tier only, sampling parameters are ignored or refused, its launch stage isn't stated, and the price rises to $4 and $20 at a date Google hasn't given.\n\n## Before you call either\n\n### DeepInfra\n\n1. Call `GET https://api.deepinfra.com/v1/openai/models` at start-up for ids, context sizes and prices. No key is needed\n2. Check the `model` field of each response. After a deprecation date, requests to the old id are served by a replacement model\n3. Ask the account owner for a scoped JWT limited to the models and spend the task needs, not the full API key\n4. Stay under 200 concurrent requests per model. On 429 `engine_overloaded`, retry after a delay, or send `models` with up to four fallbacks\n5. Never inspect a JWT with `GET /v1/scoped-jwt?jwtoken=`, which puts the token in the URL. Keep credentials in the `Authorization` header\n\n### Gemini 4 Carbon\n\n1. Call generateContent at https://generativelanguage.googleapis.com/v1beta with model gemini-4-carbon and an x-goog-api-key on a billed project\n2. Set thinking_level (LOW, MEDIUM or HIGH) instead of thinking_budget, and don't send temperature, top_p or top_k\n3. Count output against the same 1,048,576-token limit as the input, and set max_output_tokens for long answers\n4. Put static prompt content first so implicit caching applies; explicit caches aren't supported\n5. Use gemini-4-argon for most coding and knowledge work, and Carbon for the hardest long tasks, as Google suggests\n\n## Questions\n\n### Which is better for AI agents, DeepInfra or Gemini 4 Carbon?\n\nDeepInfra scores 63.7 (B) on agent readiness against Gemini 4 Carbon's 62.6 (B), and leads in 2 of 7 scored categories. Gemini 4 Carbon leads on schema \u0026 documentation, agent ergonomics and maintenance \u0026 community.\n\n### Do DeepInfra and Gemini 4 Carbon need an API key?\n\nBoth need an API key.\n\n### Can an agent call DeepInfra and Gemini 4 Carbon without installing anything?\n\nYes. DeepInfra has a hosted endpoint at https://api.deepinfra.com/v1/openai and Gemini 4 Carbon at https://generativelanguage.googleapis.com/v1beta.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-carbon.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepinfra-vs-gemini-4-carbon.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepinfra\", \"b\": \"gemini-4-carbon\"}`. From a terminal: `anchor compare deepinfra gemini-4-carbon`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepinfra.json and https://www.anchorterminal.com/api/v1/tools/gemini-4-carbon.json\n\n## Other comparisons with DeepInfra or Gemini 4 Carbon\n\n- [Amazon Bedrock vs DeepInfra](https://www.anchorterminal.com/compare/amazon-bedrock-vs-deepinfra.md)\n- [Amazon Bedrock vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/amazon-bedrock-vs-gemini-4-carbon.md)\n- [Claude API vs DeepInfra](https://www.anchorterminal.com/compare/anthropic-api-vs-deepinfra.md)\n- [Claude API vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/anthropic-api-vs-gemini-4-carbon.md)\n- [Antseed vs DeepInfra](https://www.anchorterminal.com/compare/antseed-vs-deepinfra.md)\n- [Antseed vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/antseed-vs-gemini-4-carbon.md)\n- [Azure OpenAI in Foundry Models vs DeepInfra](https://www.anchorterminal.com/compare/azure-openai-vs-deepinfra.md)\n- [Azure OpenAI in Foundry Models vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/azure-openai-vs-gemini-4-carbon.md)\n- [BlockRun.AI vs DeepInfra](https://www.anchorterminal.com/compare/blockrun-ai-vs-deepinfra.md)\n- [BlockRun.AI vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-4-carbon.md)\n- [Cerebras Inference vs DeepInfra](https://www.anchorterminal.com/compare/cerebras-inference-vs-deepinfra.md)\n- [Cerebras Inference vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/cerebras-inference-vs-gemini-4-carbon.md)\n- [Claude Fable 5.2 vs DeepInfra](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-deepinfra.md)\n- [Claude Fable 5.2 vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/claude-fable-5-2-vs-gemini-4-carbon.md)\n- [Claude Mythos 5.2 vs DeepInfra](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-deepinfra.md)\n- [Claude Mythos 5.2 vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/claude-mythos-5-2-vs-gemini-4-carbon.md)\n- [Cloudflare AI Gateway vs DeepInfra](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-deepinfra.md)\n- [Cloudflare AI Gateway vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-gemini-4-carbon.md)\n- [Cloudflare Workers AI vs DeepInfra](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-deepinfra.md)\n- [Cloudflare Workers AI vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-gemini-4-carbon.md)\n- [Cohere Chat API (Command models) vs DeepInfra](https://www.anchorterminal.com/compare/cohere-chat-vs-deepinfra.md)\n- [Cohere Chat API (Command models) vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/cohere-chat-vs-gemini-4-carbon.md)\n- [DeepInfra vs DeepSeek API](https://www.anchorterminal.com/compare/deepinfra-vs-deepseek-api.md)\n- [DeepInfra vs Eden AI](https://www.anchorterminal.com/compare/deepinfra-vs-eden-ai.md)\n- [DeepInfra vs Glimpse](https://www.anchorterminal.com/compare/deepinfra-vs-elorian-glimpse.md)\n- [DeepInfra vs Fireworks AI Inference](https://www.anchorterminal.com/compare/deepinfra-vs-fireworks-inference.md)\n- [DeepInfra vs Gemini Developer API](https://www.anchorterminal.com/compare/deepinfra-vs-gemini-api.md)\n- [DeepInfra vs GroqCloud](https://www.anchorterminal.com/compare/deepinfra-vs-groq.md)\n- [DeepInfra vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/deepinfra-vs-hugging-face-inference-providers.md)\n- [DeepInfra vs Inkling 2 Small](https://www.anchorterminal.com/compare/deepinfra-vs-inkling-2-small.md)\n- [DeepInfra vs Jais 3](https://www.anchorterminal.com/compare/deepinfra-vs-jais-3.md)\n- [DeepInfra vs Meta Model API](https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api.md)\n- [DeepInfra vs MiniMax API](https://www.anchorterminal.com/compare/deepinfra-vs-minimax-api.md)\n- [DeepInfra vs Mistral AI API](https://www.anchorterminal.com/compare/deepinfra-vs-mistral-api.md)\n- [DeepInfra vs Muse Ember](https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.md)\n- [DeepInfra vs Muse Spark Open](https://www.anchorterminal.com/compare/deepinfra-vs-muse-spark-open.md)\n- [DeepInfra vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/deepinfra-vs-nemotron-3-5-super-vl.md)\n- [DeepInfra vs Novita AI](https://www.anchorterminal.com/compare/deepinfra-vs-novita-ai.md)\n- [DeepInfra vs OpenAI API](https://www.anchorterminal.com/compare/deepinfra-vs-openai-api.md)\n- [DeepInfra vs OpenRouter](https://www.anchorterminal.com/compare/deepinfra-vs-openrouter.md)\n- [DeepInfra vs Prism Inference](https://www.anchorterminal.com/compare/deepinfra-vs-prism-inference.md)\n- [DeepInfra vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/deepinfra-vs-ramp-router.md)\n- [DeepInfra vs Beam 1.0](https://www.anchorterminal.com/compare/deepinfra-vs-reflection-beam.md)\n- [DeepInfra vs RouterPlus](https://www.anchorterminal.com/compare/deepinfra-vs-routerplus.md)\n- [DeepInfra vs SambaCloud](https://www.anchorterminal.com/compare/deepinfra-vs-sambanova.md)\n- [DeepInfra vs SiliconFlow](https://www.anchorterminal.com/compare/deepinfra-vs-siliconflow.md)\n- [DeepInfra vs Simplismart](https://www.anchorterminal.com/compare/deepinfra-vs-simplismart.md)\n- [DeepInfra vs StepFun API](https://www.anchorterminal.com/compare/deepinfra-vs-stepfun-api.md)\n- [DeepInfra vs SWE-2 Flash](https://www.anchorterminal.com/compare/deepinfra-vs-swe-2-flash.md)\n- [DeepInfra vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/deepinfra-vs-tencent-tokenhub.md)\n- [DeepInfra vs Together AI Inference](https://www.anchorterminal.com/compare/deepinfra-vs-together-inference.md)\n- [DeepInfra vs Vercel AI Gateway](https://www.anchorterminal.com/compare/deepinfra-vs-vercel-ai-gateway.md)\n- [DeepInfra vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/deepinfra-vs-vertex-ai.md)\n- [DeepInfra vs xAI API (Grok)](https://www.anchorterminal.com/compare/deepinfra-vs-xai-api.md)\n- [DeepSeek API vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/deepseek-api-vs-gemini-4-carbon.md)\n- [Eden AI vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/eden-ai-vs-gemini-4-carbon.md)\n- [Glimpse vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/elorian-glimpse-vs-gemini-4-carbon.md)\n- [Fireworks AI Inference vs Gemini 4 Carbon](https://www.anchorterminal.com/compare/fireworks-inference-vs-gemini-4-carbon.md)\n- [Gemini 4 Carbon vs Gemini Developer API](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-gemini-api.md)\n- [Gemini 4 Carbon vs GroqCloud](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-groq.md)\n- [Gemini 4 Carbon vs Hugging Face Inference Providers](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-hugging-face-inference-providers.md)\n- [Gemini 4 Carbon vs Inkling 2 Small](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-inkling-2-small.md)\n- [Gemini 4 Carbon vs Jais 3](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-jais-3.md)\n- [Gemini 4 Carbon vs Meta Model API](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-meta-model-api.md)\n- [Gemini 4 Carbon vs MiniMax API](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-minimax-api.md)\n- [Gemini 4 Carbon vs Mistral AI API](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-mistral-api.md)\n- [Gemini 4 Carbon vs Muse Ember](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-ember.md)\n- [Gemini 4 Carbon vs Muse Spark Open](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-muse-spark-open.md)\n- [Gemini 4 Carbon vs Nemotron 3.5 Super VL](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-nemotron-3-5-super-vl.md)\n- [Gemini 4 Carbon vs Novita AI](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-novita-ai.md)\n- [Gemini 4 Carbon vs OpenAI API](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-openai-api.md)\n- [Gemini 4 Carbon vs OpenRouter](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-openrouter.md)\n- [Gemini 4 Carbon vs Prism Inference](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-prism-inference.md)\n- [Gemini 4 Carbon vs Router.com (Ramp Router)](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-ramp-router.md)\n- [Gemini 4 Carbon vs Beam 1.0](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-reflection-beam.md)\n- [Gemini 4 Carbon vs RouterPlus](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-routerplus.md)\n- [Gemini 4 Carbon vs SambaCloud](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-sambanova.md)\n- [Gemini 4 Carbon vs SiliconFlow](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-siliconflow.md)\n- [Gemini 4 Carbon vs Simplismart](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-simplismart.md)\n- [Gemini 4 Carbon vs StepFun API](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-stepfun-api.md)\n- [Gemini 4 Carbon vs SWE-2 Flash](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-swe-2-flash.md)\n- [Gemini 4 Carbon vs Tencent Cloud TokenHub](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-tencent-tokenhub.md)\n- [Gemini 4 Carbon vs Together AI Inference](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-together-inference.md)\n- [Gemini 4 Carbon vs Vercel AI Gateway](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-vercel-ai-gateway.md)\n- [Gemini 4 Carbon vs Vertex AI (Gemini Enterprise Agent Platform)](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-vertex-ai.md)\n- [Gemini 4 Carbon vs xAI API (Grok)](https://www.anchorterminal.com/compare/gemini-4-carbon-vs-xai-api.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": "2026-11-10", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "DeepInfra vs Gemini 4 Carbon", "url": "" } ], "description": "DeepInfra scores 63.7 (B) to Gemini 4 Carbon's 62.6 (B) for 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