# Meta Model API > Meta Model API is Meta's pay-as-you-go API for its hosted Muse models, including Muse Wonder and Muse Spark, through OpenAI-style Responses and Chat Completions endpoints and an Anthropic-style Messages endpoint, plus image, speech and segmentation routes. - Canonical: https://www.anchorterminal.com/tools/meta-model-api - Markdown: https://www.anchorterminal.com/tools/meta-model-api.md (~7,350 tokens) - Slim: https://www.anchorterminal.com/tools/meta-model-api.min.md (~1,780 tokens, same facts, less prose, for token-sensitive contexts) - JSON: https://www.anchorterminal.com/tools/meta-model-api.json (this page as data, same URL with Accept: application/json) - Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt) - API: https://www.anchorterminal.com/api/v1/index.json - Updated: 2026-10-12 ## Overview **Grade D · 52.2/100 · rank #757 of 993 · #16 in Model APIs & inference · not agent-ready · confidence medium** More from Meta, listed separately because each is its own product: [Muse Code](https://www.anchorterminal.com/tools/muse-code.md) (Agent harnesses). ## Assessment Meta Model API serves Muse Wonder, in preview since 12 October 2026, and Muse Spark with 1M-token context, constrained JSON Schema output, automatic prompt caching and per-token prices published without a login. Standard-mode content is not used for training. There's no free allowance, status history, OpenAPI file, deprecation policy or way to force a tool call. ## Facts | Field | Value | | --- | --- | | Vendor | Meta (https://dev.meta.ai) | | Kind | Model API | | Category | Model APIs & inference (https://www.anchorterminal.com/categories/inference) | | Transport | HTTP | | Endpoint | `https://api.meta.ai/v1` | | Auth | API key · A bearer API key created on the API keys tab of the Model API dashboard after a browser sign-up with email, mobile number, Facebook, Google or GitHub. Keys belong to a team, are shown once and are revoked by deletion. No key scopes were found, and every key on a team shares the team's rate limits. | | Pricing | Pay per use ($2.50 / 1k req) · Muse Wonder is $4.00 per million input tokens, $0.20 cached and $20.00 per million output tokens, Standard mode only (checked 2026-10-12). Muse Spark is $1.25, $0.15 and $4.25, or $0.10, $0.002 and $0.20 in Contributor mode, where Meta may train on the content. Billed in arrears to a card. No free allowance was found. | | x402 | No · No x402, MPP or L402 in dev.meta.ai/llms.txt, the pricing page, the API reference or the billing help pages, checked 12 October 2026. Usage is charged to a card at a payment threshold or on the 1st of each month. | | Licence | Closed service under the Meta Model API Terms of Service (last updated 9 October 2026), with Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, as the contracting party. | | Docs | https://dev.meta.ai/docs/overview | | llms.txt | https://dev.meta.ai/llms.txt | | Last release | 2026-10-12 | | Surface graded | The Meta Model API at api.meta.ai/v1, read from its docs, pricing, help centre, terms and policies on 12 October 2026, with Muse Wonder as the model in question. No authenticated call was made. An unauthenticated `GET /v1/models` answered 401 and `GET /v1/status` answered 200 with `service_status` operational. | | Muse Wonder | Model ID muse-wonder-1.0, in preview. Text, image, video, audio and PDF in, text out, 1,048,576-token context, reasoning effort from minimal to max. Standard mode only on the API. Muse Wonder Full is not on the API. | | Muse Spark | muse-spark-1.3 (recommended for new work), muse-spark-1.2 and muse-spark-1.1, with contributor variants muse-spark-1.3-contributor and muse-spark-1.2-contributor. Same inputs and 1,048,576-token context. | | Other models | muse-image-1.0 for image generation and editing at $0.01 an image, muse-voice-transcribe-1.0 for speech to text at $0.18 an hour of audio, and sam-3.1 for segmentation at $2.50 per 1,000 images or $0.20 per 1,000 video frames. | | Endpoints | Responses (with background mode), Chat Completions, Messages, token counting, Files, Models, image generation and edits, file transcription, a realtime transcription WebSocket at wss://api.meta.ai/v1/asr/realtime, and an unauthenticated Status endpoint. | | Rate limits | Per team and per model. muse-wonder-1.0 and muse-spark-1.3 at 3,000 RPM and 4,000,000 TPM, the contributor variants at 150 RPM and 3,000,000 TPM, and new background responses at 600 a minute per team. | | Pricing modes | Standard mode, where content is not used for training, and Contributor mode, at lower prices in exchange for training use, chosen by model ID. Contributor mode isn't available for Muse Wonder on the API. | | Data handling | Content and usage data are retained as needed to provide the service, for the law, for Meta's rights under the terms, for flagged policy violations and for safety and security review, with no period stated. Zero data retention for qualified accounts through sales, with flagged inputs and outputs kept up to 2 years. | | Availability | The geographic use policy (last updated 12 October 2026) lists 21 restricted territories, among them China including Hong Kong and Macau, Russia and Pakistan, and excludes India from Muse Image. | | Capabilities | inference.llm, image.generate, image.edit, speech.stt, speech.streaming | | Tags | official, hosted, model, usage-priced, closed-source, llms-txt, multimodal, streaming, openai-compatible, anthropic-compatible, card-required | | JSON | https://www.anchorterminal.com/api/v1/tools/meta-model-api.json | ## Score breakdown (methodology v0.4, October 2026 research run) Assessed 2026-10-12 from public evidence against the published checklist (https://www.anchorterminal.com/benchmark/#checklist). Confidence: medium. Performance and Task success pending (no score, not in the total); the total is Σ(score × weight) ÷ 80 over the 7 assessed categories. "This run" is each category's share of the 100 points. | Category | Weight | This run | Score (0–100) | Points | | --- | --- | --- | --- | --- | | Reliability | 16% | 20 | 40 | 8.0 | | Performance | 10% | pending | pending | n/a | | Schema & documentation | 13% | 16.2 | 60 | 9.8 | | Agent ergonomics | 13% | 16.2 | 75 | 12.2 | | Security & auth | 14% | 17.5 | 64 | 11.2 | | Payments & pricing | 10% | 12.5 | 20 | 2.5 | | Task success | 10% | pending | pending | n/a | | Maintenance & community | 7% | 8.8 | 46 | 4.0 | | Transparency & trust (editorial 41, provenance 63) | 7% | 8.8 | 52 | 4.5 | | Negative events | up to −15 | up to −15 | none recorded | 0 | | **Total** | | | | **52.2 → D** | ### Why each score - Reliability 40: No status page with component history. The error guide's link to dev.meta.ai/status redirects to the home page, and `GET /v1/status` reports only the current state, `operational` when we called it on 12 October (0). No readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-wonder-1.0, 150 RPM for the contributor variants, 600 background submissions a minute (15). 429 and 503 carry Retry-After (60 seconds for `service_overloaded` and `backend_unavailable`), with backoff-with-jitter guidance, rate-limit headers on every token response and a documented mid-stream `server_shutting_down` event to retry (15). No SLA found, and the terms let Meta throttle or suspend access without notice (0). Muse Wonder is labelled a preview. Muse Spark 1.3 carries no preview label, but the API opened on 8 July 2026 as a public preview and we found no general-availability notice, so half (5 of 10, a departure from the all-or-nothing line). - Performance: Pending. Latency is measured per call by our probes, which haven't run yet, so this run doesn't score it. Its weight is shared across the assessed categories until the first probe window closes. - Schema & documentation 60: No OpenAPI or similar machine-readable contract found. The reference is Markdown pages with schema sections, and /openapi.json paths on dev.meta.ai return 404 (0). llms.txt at dev.meta.ai/llms.txt, and every docs page has a Markdown copy (10). The models page says when to choose Muse Wonder and when Muse Spark, and the tool-calling and structured-output guides state their limits, such as `tool_choice` only accepting `auto` and which schema keywords `strict` allows (16 of 20). JSON Schema tools and output, `reasoning_effort` an enum from minimal to max, a strict subset that returns 400 when violated, though `tool_choice` has one value (12 of 15). Curl, Python and TypeScript examples on each page and an error guide covering 400 to 504 with the error envelope, codes and a fix for each (15). Versioned model IDs (muse-spark-1.1 to 1.3, muse-wonder-1.0) and dated launch posts on the developer blog, but no API changelog (7 of 15). - Agent ergonomics 75: Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool calls on all three endpoints, parallel calls, tool search with deferred loading, built-in web search and a computer-use tool, but `tool_choice` accepts only `auto`, so a call can't be forced or forbidden (15 of 20). Output constrained to the JSON Schema on every surface (15). Automatic prompt caching with no setup, and cached input at 5 per cent of the input price on Muse Wonder (15). 1,048,576-token context on Muse Wonder and every Muse Spark version, with no long-context premium (15). No batch endpoint or discount found. Background responses run asynchronously at the normal price (0). No SDK of Meta's own. The docs use the OpenAI and Anthropic SDKs against compatible endpoints, the same reading as StepFun (0). OpenAI-style error envelope with `type` and `code`, per-status fixes and named retryable codes (15). - Security & auth 64: Model reading. Bearer keys per team, named, shown once and revoked by deletion, with rotation advice. No scopes, read-only keys or per-key limits found (20 of 30). The terms say Meta will not use Content submitted to the Standard Services to train Meta Models, and training applies only to the `-contributor` model IDs a caller picks. Content may still be used to develop and improve safety and security systems (17 of 20). Retention is 'as needed' for the service, the law, flagged violations and safety review, with no period stated. Zero data retention for qualified accounts through sales, with flagged content kept up to 2 years (10 of 15). The usage dashboard shows tokens and requests per day by model, with no per-key or per-call log found (5 of 15). No security.txt on dev.meta.ai. Meta's bug bounty at bugbounty.meta.com is named in the Muse Code SDK's security policy, and a security commitments page describes TLS 1.2 or later, PCI-DSS card handling and an ISMS, with no named certification for the API (12 of 20). - Payments & pricing 20: No machine payment protocol (0). Per-token prices for Muse Wonder and Muse Spark, and per-image, per-hour and per-frame prices for the other models, published without a login (20). No free API allowance found. Billing needs a card, and Playground requests count towards usage (0). Browser sign-up with email, phone, Facebook, Google or GitHub (0). - Task success: Pending. Task success needs the category task suites run through each tool, which haven't run yet, so this run doesn't score it. Its weight is shared across the assessed categories until then. A data provider's data-quality score is published on its listing now and becomes half of this category when it's scored. - Maintenance & community 46: Model reading. Muse Wonder launched on 12 October 2026 (30). No deprecation policy or notice period found. All three Muse Spark versions are still served, so no retirement has tested one (0 of 12). Three dated launches in the last 90 days that we read, Muse Spark 1.2 on 5 August, Muse Code out of beta on 31 August and Muse Wonder on 12 October (8 of 8). No Model API changelog. A help centre with support tickets, answered 'typically within two business days' by Meta's own account, which we didn't test (8 of 15). No SDK of Meta's own for the API (0 of 15, 0 of 10). - Transparency & trust 52: Closed service with clear terms, under California law with AAA arbitration for Meta Platforms, Inc. and Irish law and courts for Meta Platforms Ireland Limited, with liability capped at the greater of 12 months of fees or $1,000. Muse Glimmer's open weights don't change the hosted service (15). The terms, the models page, the pricing page and the zero-retention help page agree that Standard content isn't used for training and Contributor content may be, but retention periods are not stated outside zero retention, and the privacy policy is Meta's general one (18 of 30). No deprecation policy or dated notices found, and the terms let Meta remove functionality without notice (0). A subprocessor list is linked from the terms but wasn't read, and no data locations were found (8 of 20). Fix list for a coding agent, everything this grade says the listing lacks, the biggest gain first (19 items): https://www.anchorterminal.com/fixes/meta-model-api.md (JSON https://www.anchorterminal.com/fixes/meta-model-api.json) ### What we couldn't check - unchecked: whether an API call works before a card is added. The help centre says an admin adds a payment method to set up billing, and no free allowance is named - unchecked: the subprocessor list at forwork.meta.com/legal/mfw-subprocessor-list/ and the Meta Global Processor Terms, not read, and the Meta Privacy Policy, which www.facebook.com's robots.txt asks readers like ours not to fetch - unchecked: www.meta.ai/.well-known/security.txt. www.meta.ai's robots.txt disallows ClaudeBot from every path, so it wasn't requested - Whether the API itself is generally available. The 8 July 2026 launch called it a public preview, the docs now label only Muse Wonder a preview, and no GA notice was found - When Muse Spark 1.3 was released. The docs call it the latest version, and no dated page for it was found - No authenticated call was made. That `muse-wonder-1.0` answers on api.meta.ai rests on the models, pricing and rate-limit pages, which list it - No Meta Model API listing existed before. This listing covers the whole API with Muse Wonder as its flagship model, in line with the other model-vendor APIs in this category. llama-guard and llamafirewall are Meta's open-weight safety models and stay separate ### Sources - Muse Wonder announcement, research blog: (seen 2026-10-12) - Muse Wonder model card (PDF): (seen 2026-10-12) - Muse Wonder launch post, developer blog: (seen 2026-10-12) - Muse Wonder model page: (seen 2026-10-12) - docs index: (seen 2026-10-12) - models, IDs, context and availability: (seen 2026-10-12) - pricing and rate limits: (seen 2026-10-12) - authentication: (seen 2026-10-12) - quickstart: (seen 2026-10-12) - API reference and Status endpoint: (seen 2026-10-12) - error handling: (seen 2026-10-12) - SDKs and libraries: (seen 2026-10-12) - structured output: (seen 2026-10-12) - tool calling: (seen 2026-10-12) - Meta Model API Terms of Service, last updated 9 October 2026: (seen 2026-10-12) - Model Designation and Geographic Use Policy, last updated 12 October 2026: (seen 2026-10-12) - security commitments: (seen 2026-10-12) - zero data retention: (seen 2026-10-12) - sign-up: (seen 2026-10-12) - billing set-up and how charges work: (seen 2026-10-12) - contact support: (seen 2026-10-12) - Muse Spark on the API, 8 July 2026: (seen 2026-10-12) - Muse Spark 1.2 and global access, 5 August 2026: (seen 2026-10-12) - unauthenticated status and models calls: (seen 2026-10-12) ## Who's behind it (provenance 63/100, checked 2026-10-12) | Check | Finding | Points | | --- | --- | --- | | Legal entity named | Meta Platforms, Inc. | 20/20 | | Domain age | meta.ai, registered 2017-12-16 (8 years) | 11/15 | | Endpoint on the vendor's domain | api.meta.ai | 15/15 | | Terms of service | published | 10/10 | | Privacy policy | published, but our reader couldn't read it | 7/10 | | Status page | not found | 0/10 | | Changelog | not found | 0/10 | | security.txt | not found | 0/10 | The terms (last updated 9 October 2026) are an agreement with Meta Platforms, Inc., or Meta Platforms Ireland Limited for users in the European Region. The model card names the same two. The privacy field is the Meta Privacy Policy, which the terms link. www.facebook.com's robots.txt asks readers like ours not to fetch it, as the 8 October policy reading records, so it wasn't read. The terms also link a subprocessor list at forwork.meta.com/legal/mfw-subprocessor-list/ and the Meta Global Processor Terms, neither of which was read. RDAP for meta.ai gives a registration date of 16 December 2017. The API answers at api.meta.ai. No status page. The error guide links dev.meta.ai/status, which redirects to the home page. `GET /v1/status` is an unauthenticated health endpoint with no history. No Model API changelog was found. The only changelog on dev.meta.ai is Muse Code's. dev.meta.ai/.well-known/security.txt returns 404. www.meta.ai's robots.txt disallows ClaudeBot, so its security.txt wasn't requested. www.facebook.com/.well-known/security.txt is valid until 11 November 2026 and points to bugbounty.meta.com. developer.meta.com/ai/ redirects to dev.meta.ai. ### Terms and privacy, as read A reading by a fixed set of rules, each answered with the vendor's own sentence. Not legal advice. **Terms of service** (https://dev.meta.ai/legal/terms-of-service), not read yet. **Privacy policy** (https://www.facebook.com/privacy/policy/), read 2026-10-08. Our reader couldn't read it (robots.txt asks readers like ours not to fetch it). ## Live (updated 2026-10-12 22:35 UTC) - Right now: up, HTTP 404, 115 ms, checked 2026-10-12 22:35 UTC (get on `https://api.meta.ai/v1`) - Uptime 24h 100.0% (19 probes) · 30 days 100.0% (19 probes) · p50 87 ms · p95 166 ms - Always current: https://www.anchorterminal.com/api/v1/live/meta-model-api.json ## Probe metrics Not measured yet. Our benchmark probes haven't run, so there's no availability, latency or error rate from a run and Performance is pending. Live uptime, where we poll the endpoint, is under Live and doesn't change the score. ## Prices | Item | Price | Unit | Note | | --- | --- | --- | --- | | Muse Wonder input | $4 | per 1M tokens | Standard mode, muse-wonder-1.0. No long-context premium. | | Muse Wonder cached input | $0.20 | per 1M tokens | | | Muse Wonder output | $20 | per 1M tokens | | | Muse Spark input | $1.25 | per 1M tokens | Standard mode, muse-spark-1.3, 1.2 and 1.1. | | Muse Spark cached input | $0.15 | per 1M tokens | | | Muse Spark output | $4.25 | per 1M tokens | | | Muse Spark input, Contributor mode | $0.10 | per 1M tokens | Meta may use the content to train its models. | | Muse Spark output, Contributor mode | $0.20 | per 1M tokens | Cached input is $0.002 per million tokens. | | Web search grounding | $2.50 | per 1,000 requests | Per 1,000 search queries, on top of token cost. | | Muse Image generated image | $0.01 | per image | | Across all listings: https://www.anchorterminal.com/prices/index.md ## Strengths - Muse Wonder and Muse Spark take text, image, video, audio and PDF input with a 1,048,576-token context window - Per-token prices, per-model rate limits and rate-limit headers published without a login - Content sent in Standard mode is not used to train Meta models, and training use is opt-in through `-contributor` model IDs - Responses, Chat Completions and Messages endpoints, so the OpenAI and Anthropic SDKs work with a base-URL change - llms.txt and a Markdown copy of every docs page, with an error guide that gives the Retry-After value for each retryable error ## Weaknesses - Muse Wonder is labelled a preview, and no deprecation policy or notice period was found - No free API allowance found, and billing is set up with a card - No status page with history. The docs' status link redirects to the home page, and `/v1/status` reports only the current state - `tool_choice` accepts only `auto`, so a call can't be forced or ruled out - No OpenAPI file, batch endpoint or Model API changelog found, and API keys have no scopes ## Before you call it (notes for agents) 1. Use `muse-wonder-1.0` for long multi-step work and `muse-spark-1.3` for cheaper, high-volume calls. Both take the same requests 2. Keep personal or confidential data away from `-contributor` model IDs. Meta may train on that content 3. Honour Retry-After on 429 and 503, which is 60 seconds for `service_overloaded`, and check `GET https://api.meta.ai/v1/status`, which needs no key 4. Pass the key to the OpenAI SDK explicitly or set `OPENAI_API_KEY`. The SDK doesn't read `MODEL_API_KEY` 5. Leave `tool_choice` at `auto`. The docs say `none` and `required` are not supported ## Connect First request: ```bash curl -X POST "https://api.meta.ai/v1/responses" \ -H "Authorization: Bearer $MODEL_API_KEY" \ -H "Content-Type: application/json" \ -d '{"model": "muse-spark-1.3", "input": "What is the capital of France?"}' ``` ## Similar tools Ranked by shared capabilities, then score. Same-category tools with no shared capability key are listed last. | Tool | Grade | Score | Rank | Shared capabilities | x402 | Markdown | | --- | --- | --- | --- | --- | --- | --- | | Cloudflare Workers AI | B | 68.3 | 227 | inference.llm, image.generate, speech.stt | no | https://www.anchorterminal.com/tools/cloudflare-workers-ai.md | | DeepInfra | B | 63 | 422 | inference.llm, image.generate, speech.stt | no | https://www.anchorterminal.com/tools/deepinfra.md | | SiliconFlow | D | 46.7 | 871 | inference.llm, image.generate, speech.stt | no | https://www.anchorterminal.com/tools/siliconflow.md | | Amazon Transcribe | BB | 73.4 | 89 | speech.stt, speech.streaming | no | https://www.anchorterminal.com/tools/amazon-transcribe.md | | Azure AI Speech speech-to-text | BB | 73 | 93 | speech.stt, speech.streaming | no | https://www.anchorterminal.com/tools/azure-speech-to-text.md | | OpenAI Image API | BB | 72.4 | 105 | image.generate, image.edit | no | https://www.anchorterminal.com/tools/openai-image-api.md | ## Panel reviews (0) Reviewed by the Anchor panel (https://www.anchorterminal.com/reviewers/index.md): . Desk reviews, written from public documentation, pricing, terms, source and status history on 1 October 2026. No calls made. For a desk review, the outcome says whether the reviewer's questions could be answered from public material: success, partial or failure. How reviews work: https://www.anchorterminal.com/reviews/how-it-works.md ## Notable - Meta launched Muse Wonder (`muse-wonder-1.0`) on Meta Model API and Muse Code on 12 October 2026 at $4 input and $20 output per million tokens. Muse Wonder Full is only in Muse Code, on the Power Usage and Maximum Usage plans (source: ) - Muse Wonder takes text, image, video, audio and PDF input, has a 1,048,576-token context window and reasoning effort from `minimal` to `max`, and is labelled a preview (source: ) - The Muse Wonder model card gives 12 October 2026 as the date of release and 1M tokens of context at inference (source: ) - Muse Spark reached the API as a public preview for US developers on 8 July 2026, and global access opened with `muse-spark-1.2` on 5 August 2026 (source: ) - The terms say Meta will not use Content submitted to the Standard Services to train Meta Models, and that Contributor Services content may be used to train, develop, evaluate and improve Meta's models, products and services (source: ) - Meta injects a system prompt and related scaffolding into every prompt. Those tokens are not billed and are left out of reported usage (source: ) - The terms let Meta add, change or remove functionality and throttle, limit, suspend or terminate access at any time without notice (source: ) - #16 of 19 in Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md - All 171 models comparisons: https://www.anchorterminal.com/compare/inference/index.md ## Compare - [Claude API vs Meta Model API](https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api.md): BB 77.3 vs D 52.2 - [Antseed vs Meta Model API](https://www.anchorterminal.com/compare/antseed-vs-meta-model-api.md): C 55.1 vs D 52.2 - [BlockRun.AI vs Meta Model API](https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.md): BB 72.4 vs D 52.2 - [Cloudflare AI Gateway vs Meta Model API](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-meta-model-api.md): B 66.8 vs D 52.2 - [Cloudflare Workers AI vs Meta Model API](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-meta-model-api.md): B 68.3 vs D 52.2 - [Cohere Chat API (Command models) vs Meta Model API](https://www.anchorterminal.com/compare/cohere-chat-vs-meta-model-api.md): B 64.7 vs D 52.2 - [DeepInfra vs Meta Model API](https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api.md): B 63 vs D 52.2 - [DeepSeek API vs Meta Model API](https://www.anchorterminal.com/compare/deepseek-api-vs-meta-model-api.md): D 46.8 vs D 52.2 - [Gemini Developer API vs Meta Model API](https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.md): B 65.5 vs D 52.2 - [GroqCloud vs Meta Model API](https://www.anchorterminal.com/compare/groq-vs-meta-model-api.md): BB 75.6 vs D 52.2 - [Meta Model API vs Mistral AI API](https://www.anchorterminal.com/compare/meta-model-api-vs-mistral-api.md): D 52.2 vs BB 71.2 - [Meta Model API vs Novita AI](https://www.anchorterminal.com/compare/meta-model-api-vs-novita-ai.md): D 52.2 vs D 53.5 - [Meta Model API vs OpenAI API](https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api.md): D 52.2 vs A 83.3 - [Meta Model API vs OpenRouter](https://www.anchorterminal.com/compare/meta-model-api-vs-openrouter.md): D 52.2 vs B 68.5 - [Meta Model API vs Prism Inference](https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.md): D 52.2 vs C 60.1 - [Meta Model API vs SambaCloud](https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova.md): D 52.2 vs B 66.4 - [Meta Model API vs SiliconFlow](https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow.md): D 52.2 vs D 46.7 - [Meta Model API vs StepFun API](https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api.md): D 52.2 vs E 44.1 ## Verify this listing For the vendor. The badge or a plain link to this page verifies the listing, from a page on meta.ai or one of its subdomains. It shows the listing is the vendor's and that the vendor knows it's here, and it never changes a grade, rank or review. The vendor sends the page's address to `POST https://www.anchorterminal.com/api/v1/verify` as `{"slug": "meta-model-api", "url": "…"}`, or calls the `verify_listing` tool at https://www.anchorterminal.com/mcp. We fetch the page once, then again every week; two failed checks in a row and the verification lapses, and a later pass restores it. What we check: https://www.anchorterminal.com/builders/index.md#verify HTML badge: ```html Meta Model API on Anchor Terminal ``` Markdown badge, for a README: ```markdown [![Meta Model API on Anchor Terminal](https://www.anchorterminal.com/badges/meta-model-api.svg)](https://www.anchorterminal.com/tools/meta-model-api) ``` Plain link: ```html Meta Model API on Anchor Terminal ``` ## Share this listing For the vendor. Sharing assets for social media, two PNGs of 1200 × 630 that say Meta Model API is listed on Anchor Terminal, with the vendor's logo and this page's address and no grade or score. - Dark: https://www.anchorterminal.com/assets/share/meta-model-api-dark.png - Light: https://www.anchorterminal.com/assets/share/meta-model-api-light.png