{ "data": { "a": { "slug": "meta-model-api", "name": "Meta Model API", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai", "kind": "model", "category": "inference", "summary": "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.", "url": "https://www.anchorterminal.com/tools/meta-model-api", "markdownUrl": "https://www.anchorterminal.com/tools/meta-model-api.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/meta-model-api.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/meta-model-api.json", "license": "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.", "transports": [ "http" ], "remoteUrl": "https://api.meta.ai/v1", "packages": [], "auth": "api-key", "authNotes": "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": "usage", "pricingNotes": "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.", "priceSummary": "$2.50 / 1k req", "where": "hosted", "x402": { "level": "no", "evidence": "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.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-12" }, "docsUrl": "https://dev.meta.ai/docs/overview", "llmsTxt": "https://dev.meta.ai/llms.txt", "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" ], "lastRelease": "2026-10-12", "graded": true, "anchor": { "graded": true, "score": 52.2, "grade": "D", "agentReady": false, "rank": 757, "ranked": true, "rankOf": 993, "categoryRank": 16, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 75, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-12" }, "negative": 0, "verdict": "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.", "bestFor": "Long-running agent and coding work over text, images, video, audio and PDFs with a 1M-token context, where training must stay off by default.", "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" ], "agentNotes": [ "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", "Keep personal or confidential data away from `-contributor` model IDs. Meta may train on that content", "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", "Pass the key to the OpenAI SDK explicitly or set `OPENAI_API_KEY`. The SDK doesn't read `MODEL_API_KEY`", "Leave `tool_choice` at `auto`. The docs say `none` and `required` are not supported" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "D", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 52.2 } ], "editorialScores": { "ergonomics": 75, "maintenance": 46, "payments": 20, "reliability": 40, "schema": 60, "security": 64, "transparency": 41 }, "provenanceScore": 63 }, "connect": { "http": "curl -X POST \"https://api.meta.ai/v1/responses\" \\\n -H \"Authorization: Bearer $MODEL_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"muse-spark-1.3\", \"input\": \"What is the capital of France?\"}'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/meta-model-api" }, "sameCompany": [ "muse-code" ], "area": "models", "unitPrices": [ { "item": "Muse Wonder input", "unit": "1m-tokens", "usd": 4, "note": "Standard mode, muse-wonder-1.0. No long-context premium." }, { "item": "Muse Wonder cached input", "unit": "1m-tokens", "usd": 0.2 }, { "item": "Muse Wonder output", "unit": "1m-tokens", "usd": 20 }, { "item": "Muse Spark input", "unit": "1m-tokens", "usd": 1.25, "note": "Standard mode, muse-spark-1.3, 1.2 and 1.1." }, { "item": "Muse Spark cached input", "unit": "1m-tokens", "usd": 0.15 }, { "item": "Muse Spark output", "unit": "1m-tokens", "usd": 4.25 }, { "item": "Muse Spark input, Contributor mode", "unit": "1m-tokens", "usd": 0.1, "note": "Meta may use the content to train its models." }, { "item": "Muse Spark output, Contributor mode", "unit": "1m-tokens", "usd": 0.2, "note": "Cached input is $0.002 per million tokens." }, { "item": "Web search grounding", "unit": "1k-requests", "usd": 2.5, "note": "Per 1,000 search queries, on top of token cost." }, { "item": "Muse Image generated image", "unit": "image", "usd": 0.01 } ], "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "meta.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": true, "terms": "https://dev.meta.ai/legal/terms-of-service", "privacy": "https://www.facebook.com/privacy/policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-12", "notes": [ "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." ], "score": 63 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/meta-model-api.json", "live": { "slug": "meta-model-api", "probe": { "target": "https://api.meta.ai/v1", "method": "get", "lastAt": "2026-10-12T22:35:32.230718004Z", "lastOk": true, "lastStatus": 404, "lastMs": 115, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 87, "p95ms24h": 166, "samples24h": 19, "samples30d": 19, "days": [ { "date": "2026-10-12", "probes": 19, "ok": 19 } ] }, "updatedAt": "2026-10-12T22:35:32.230718004Z" } }, "answer": "Prism Inference scores 60.1 (C) on agent readiness against Meta Model API's 52.2 (D), and leads in 6 of 7 scored categories. Meta Model API leads on agent ergonomics.", "b": { "slug": "prism-inference", "name": "Prism Inference", "vendor": "Prism Technologies Inc", "vendorUrl": "https://prisminference.com", "kind": "model", "category": "inference", "summary": "Prism is a hosted inference API from Prism Technologies Inc for open-weight models, aimed at coding agents. It accepts OpenAI Chat Completions, OpenAI Responses and Anthropic Messages requests at api.prisminference.com. It launched on 24 September 2026.", "url": "https://www.anchorterminal.com/tools/prism-inference", "markdownUrl": "https://www.anchorterminal.com/tools/prism-inference.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/prism-inference.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/prism-inference.json", "repo": "https://github.com/prismhq/hermes-prism-provider", "license": "Proprietary service under Prism's terms of service. The OpenAPI file declares `LicenseRef-Proprietary`. The Hermes provider plugin repository carries no licence file", "transports": [ "http" ], "remoteUrl": "https://api.prisminference.com/v1", "packages": [], "auth": "api-key", "authNotes": "`Authorization: Bearer` with a key issued from account settings after sign-up at prisminference.com/signup. The inference endpoints also accept the key in `x-api-key`. A missing, invalid, expired or revoked key returns 401. No key scopes were found in the reviewed documentation. An agent can call `POST https://prisminference.com/api/agent-signups` with the owner's email and a username and receive a key once, but that key can't run inference until the owner supplies a six-digit emailed code, and it expires after 30 days. `GET /v1/models` needs no key.", "pricing": "usage", "pricingNotes": "Prepaid per-token pricing with no minimum. DeepSeek-V4.1-Flash is $0.09 in, $1.20 out and $0.06 cache read per million tokens, and Gemma 4 31B is $0.30, $0.40 and $0.15 (https://prisminference.com/pricing, matched by https://api.prisminference.com/v1/models). No free tier or trial credit was found, and a workspace without credit gets 402. A person funds the workspace in a browser. Elastic endpoints, dedicated deployments and batch are sold through sales with no published price.", "priceSummary": "from $0.09 / 1M in", "where": "hosted", "x402": { "level": "no", "evidence": "No x402, MPP or L402 in llms.txt, the docs index, the OpenAPI file or the pricing page, read 2026-10-08. The docs describe prepaid credit funded by a person in the browser.", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-08" }, "docsUrl": "https://docs.prisminference.com", "rateLimitsUrl": "https://docs.prisminference.com/rate-limits", "llmsTxt": "https://prisminference.com/llms.txt", "openapi": "https://docs.prisminference.com/openapi.yaml", "capabilities": [ "inference.fast", "inference.open-weights", "inference.llm" ], "tags": [ "hosted", "model", "open-weights", "fast", "usage-priced", "prepaid", "openapi", "llms-txt", "openai-compatible", "anthropic-compatible", "zero-retention", "status-page", "new" ], "lastRelease": "2026-10-06", "graded": true, "anchor": { "graded": true, "score": 60.1, "grade": "C", "agentReady": false, "rank": 543, "ranked": true, "rankOf": 993, "categoryRank": 13, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 68, "maintenance": 49, "payments": 30, "reliability": 65, "schema": 82, "security": 65, "transparency": 61 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-08" }, "negative": -2, "negativeNotes": [ "2026-10-08. The home page shows a '99.99% Uptime SLA' tile, and the pricing page says 'No minimums, no rate limits' above the per-token table. The terms of 9 September 2026 say the services have no guaranteed uptime or service credit unless a separate written agreement says otherwise, and the docs describe per-key rate limits that return 429. No SLA document was found. A misleading claim, with the smallest deduction because the terms and docs state the real position (https://prisminference.com/, https://prisminference.com/pricing, https://prisminference.com/terms, https://docs.prisminference.com/rate-limits)." ], "verdict": "Three wire formats, a public OpenAPI 3.1 file, per-token prices in a keyless catalogue and zero data retention by default on every tier. The service launched on 24 September 2026 with two models, one of them by request, from a two-person company. No rate-limit numbers, SLA document, free tier or deprecation policy was found.", "bestFor": "Coding agents that want DeepSeek-V4.1-Flash at a low input price, with no retention, through whichever of the three wire formats the harness already speaks.", "strengths": [ "One key works across OpenAI Chat Completions, OpenAI Responses and Anthropic Messages, with a public OpenAPI 3.1 file, llms.txt and Markdown docs", "Zero data retention is the default on every tier, and the privacy policy, terms and docs all say inputs and outputs are never used for training", "Every error carries a stable `code`, a `retryable` flag, a `fix` hint and a `docs_url`, and 429 carries `Retry-After` in seconds", "`GET /v1/models` answers without a key and returns context length, maximum output and per-token prices for each model", "DeepSeek-V4.1-Flash is listed with a 1M-token context and 384,000 output tokens at $0.09 in and $1.20 out per million" ], "weaknesses": [ "Two models. The docs mark Gemma 4 31B as request access per organisation, while llms.txt and the keyless catalogue list it as available", "No rate-limit numbers are published. The docs say per-key limits exist, and the pricing page says 'no rate limits'", "The home page shows a '99.99% Uptime SLA' tile, while the terms say there is no guaranteed uptime without a separate written agreement", "No free tier found. Billing is prepaid, a person funds the workspace in a browser, and the agent sign-up key needs an emailed code", "The service launched on 24 September 2026. The changelog has one entry, and no deprecation policy, sub-processor list or certification was found" ], "agentNotes": [ "Call `GET https://api.prisminference.com/v1/models` at start-up, with no key, and use only ids it returns. Expect 403 on `gemma-4-31b` without organisation access", "Use base URL `https://api.prisminference.com/v1` for OpenAI clients and `https://api.prisminference.com` with no `/v1` for Anthropic clients", "Read `error.retryable` before retrying, and wait for `Retry-After` on 429, which covers both key limits and model capacity", "Send `reasoning_effort: \"none\"` or `low` when latency matters. Reasoning is on by default and its tokens are billed as output", "Keep conversation state yourself and send `store: false` on Responses. `previous_response_id`, stored responses and hosted tools aren't supported" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "C", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 60.1 } ], "editorialScores": { "ergonomics": 68, "maintenance": 49, "payments": 30, "reliability": 65, "schema": 82, "security": 65, "transparency": 43 }, "provenanceScore": 79 }, "connect": { "install": "pip install openai # or: npm install openai, base URL https://api.prisminference.com/v1. Anthropic SDKs use https://api.prisminference.com with no /v1", "http": "curl \"https://api.prisminference.com/v1/chat/completions\" \\\n -H \"Authorization: Bearer $PRISM_API_KEY\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\":\"deepseek-v4.1-flash\",\"messages\":[{\"role\":\"user\",\"content\":\"Return pong.\"}]}'", "claudeCode": "export ANTHROPIC_BASE_URL=https://api.prisminference.com\nexport ANTHROPIC_AUTH_TOKEN=$PRISM_API_KEY\nexport ANTHROPIC_MODEL=deepseek-v4.1-flash\nexport ANTHROPIC_SMALL_FAST_MODEL=gemma-4-31b" }, "letme": { "capability": "https://letme.dev/inference.fast", "tool": "https://letme.dev/prism-inference" }, "area": "models", "provenance": { "legalEntity": "Prism Technologies Inc", "domain": "prisminference.com", "domainRegistered": "2026-09-09", "endpointOnVendorDomain": true, "terms": "https://prisminference.com/terms", "privacy": "https://prisminference.com/privacy", "statusPage": "https://status.prisminference.com", "changelog": "https://docs.prisminference.com/changelog", "securityTxt": "valid", "checked": "2026-10-08", "notes": [ "The terms and the privacy policy, both last updated 9 September 2026, name Prism Technologies Inc. The terms are governed by California law with arbitration in San Francisco.", "RDAP gives a registration date of 2026-09-09 for prisminference.com, with Name.com as registrar.", "security.txt has a Contact line (founders@prisminference.com), an Expires date of 2027-10-06 and a Canonical line. No disclosure policy or bug bounty is named.", "The status page runs on incident.io with one component for each model and no component for the API or the website. Its incidents feed was empty on 8 October 2026.", "The YC directory lists Prism in the Spring 2025 batch, in San Francisco, with a team of 2. The home page links an X account named prism_videos, from the company's earlier video product." ], "score": 79 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/prism-inference.json", "live": { "slug": "prism-inference", "probe": { "target": "https://api.prisminference.com/v1", "method": "get", "lastAt": "2026-10-12T22:35:36.857325545Z", "lastOk": true, "lastStatus": 200, "lastMs": 110, "authRequired": false, "uptime24h": 100, "uptime30d": 99.91, "p50ms24h": 124, "p95ms24h": 486, "samples24h": 248, "samples30d": 1070, "days": [ { "date": "2026-10-08", "probes": 93, "ok": 93 }, { "date": "2026-10-09", "probes": 250, "ok": 250 }, { "date": "2026-10-10", "probes": 247, "ok": 247 }, { "date": "2026-10-11", "probes": 247, "ok": 246 }, { "date": "2026-10-12", "probes": 233, "ok": 233 } ] }, "vendorStatus": { "page": "https://status.prisminference.com", "indicator": "none", "summary": "All Systems Operational", "checkedAt": "2026-10-12T22:30:28.176265852Z" }, "versions": [ { "registry": "github", "name": "prismhq/hermes-prism-provider", "version": "v1.0.2", "released": "2026-09-16", "seenAt": "2026-10-12T22:23:39.049012796Z" } ], "githubStars": 0, "securityTxt": { "url": "https://prisminference.com/.well-known/security.txt", "state": "valid", "expires": "2027-10-06T00:00:00.000Z", "checkedAt": "2026-10-12T15:43:42.26730189Z" }, "llmsTxt": { "url": "https://prisminference.com/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-12T14:09:52.288252985Z" }, "domain": { "domain": "prisminference.com", "registered": "2026-09-09", "source": "https://rdap.verisign.com/com/v1/domain/prisminference.com", "checkedAt": "2026-10-11T13:11:34.492540014Z" }, "pages": [ { "url": "https://docs.prisminference.com/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-12T19:50:50.059816082Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "184a0fafdb8c" }, { "url": "https://prisminference.com/pricing", "kind": "pricing", "status": 304, "checkedAt": "2026-10-12T19:56:48.764381989Z", "changedAt": "2026-10-10T19:08:07.782517134Z", "fingerprint": "2bdff14f7c0f" }, { "url": "https://prisminference.com/privacy", "kind": "privacy", "status": 304, "checkedAt": "2026-10-12T19:56:50.904855862Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "64ea7b8f1b7b" }, { "url": "https://prisminference.com/terms", "kind": "terms", "status": 304, "checkedAt": "2026-10-12T19:56:52.897776211Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "27dd15c69f49" } ], "updatedAt": "2026-10-12T22:35:36.857325545Z" } }, "facts": [ { "a": "Model API", "b": "Model API", "name": "Kind" }, { "a": "Meta", "b": "Prism Technologies Inc", "name": "Vendor" }, { "a": "https://api.meta.ai/v1", "b": "https://api.prisminference.com/v1", "name": "Hosted endpoint" }, { "a": "HTTP", "b": "HTTP", "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": "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.", "b": "Proprietary service under Prism's terms of service. The OpenAPI file declares `LicenseRef-Proprietary`. The Hermes provider plugin repository carries no licence file", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-10-12", "b": "2026-10-06", "name": "Last release" }, { "a": "", "b": "2026-09-09", "name": "Terms last updated" }, { "a": "couldn't be read", "b": "2026-09-09", "name": "Privacy policy last updated" }, { "a": "", "b": "not found in the text", "name": "Customer content may train models" }, { "a": "", "b": "yes", "name": "Terms restrict automated access" }, { "a": "", "b": "not found in the text", "name": "Terms restrict benchmarking" }, { "a": "", "b": "not found in the text", "name": "Terms or service can change without notice" }, { "a": "", "b": "yes", "name": "Arbitration or class-action waiver" } ], "faq": [ { "answer": "Prism Inference scores 60.1 (C) on agent readiness against Meta Model API's 52.2 (D), and leads in 6 of 7 scored categories. Meta Model API leads on agent ergonomics.", "question": "Which is better for AI agents, Meta Model API or Prism Inference?" }, { "answer": "Both need an API key.", "question": "Do Meta Model API and Prism Inference need an API key?" }, { "answer": "Yes. Meta Model API has a hosted endpoint at https://api.meta.ai/v1 and Prism Inference at https://api.prisminference.com/v1.", "question": "Can an agent call Meta Model API and Prism Inference without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Agent ergonomics, 75 against 68" ], "also": null, "goodFor": "Long-running agent and coding work over text, images, video, audio and PDFs with a 1M-token context, where training must stay off by default.", "slug": "meta-model-api", "watchFor": "Muse Wonder is labelled a preview, and no deprecation policy or notice period was found" }, { "aheadOn": [ "Reliability, 65 against 40", "Schema \u0026 documentation, 82 against 60", "Payments \u0026 pricing, 30 against 20", "Transparency \u0026 trust, 61 against 52" ], "also": null, "goodFor": "Coding agents that want DeepSeek-V4.1-Flash at a low input price, with no retention, through whichever of the three wire formats the harness already speaks.", "slug": "prism-inference", "watchFor": "Two models. The docs mark Gemma 4 31B as request access per organisation, while llms.txt and the keyless catalogue list it as available" } ], "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api.json", "title": "Claude API vs Meta Model API", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-prism-inference.json", "title": "Claude API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-meta-model-api.json", "title": "Antseed vs Meta Model API", "url": "https://www.anchorterminal.com/compare/antseed-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/antseed-vs-prism-inference.json", "title": "Antseed vs Prism Inference", "url": "https://www.anchorterminal.com/compare/antseed-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.json", "title": "BlockRun.AI vs Meta Model API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-prism-inference.json", "title": "BlockRun.AI vs Prism Inference", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-meta-model-api.json", "title": "Cloudflare AI Gateway vs Meta Model API", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-prism-inference.json", "title": "Cloudflare AI Gateway vs Prism Inference", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-meta-model-api.json", "title": "Cloudflare Workers AI vs Meta Model API", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-prism-inference.json", "title": "Cloudflare Workers AI vs Prism Inference", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-meta-model-api.json", "title": "Cohere Chat API (Command models) vs Meta Model API", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-prism-inference.json", "title": "Cohere Chat API (Command models) vs Prism Inference", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api.json", "title": "DeepInfra vs Meta Model API", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-prism-inference.json", "title": "DeepInfra vs Prism Inference", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-meta-model-api.json", "title": "DeepSeek API vs Meta Model API", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-prism-inference.json", "title": "DeepSeek API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.json", "title": "Gemini Developer API vs Meta Model API", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference.json", "title": "Gemini Developer API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-meta-model-api.json", "title": "GroqCloud vs Meta Model API", "url": "https://www.anchorterminal.com/compare/groq-vs-meta-model-api" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-prism-inference.json", "title": "GroqCloud vs Prism Inference", "url": "https://www.anchorterminal.com/compare/groq-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-mistral-api.json", "title": "Meta Model API vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-novita-ai.json", "title": "Meta Model API vs Novita AI", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api.json", "title": "Meta Model API vs OpenAI API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-openrouter.json", "title": "Meta Model API vs OpenRouter", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova.json", "title": "Meta Model API vs SambaCloud", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow.json", "title": "Meta Model API vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api.json", "title": "Meta Model API vs StepFun API", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-prism-inference.json", "title": "Mistral AI API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/novita-ai-vs-prism-inference.json", "title": "Novita AI vs Prism Inference", "url": "https://www.anchorterminal.com/compare/novita-ai-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/openai-api-vs-prism-inference.json", "title": "OpenAI API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/openai-api-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/openrouter-vs-prism-inference.json", "title": "OpenRouter vs Prism Inference", "url": "https://www.anchorterminal.com/compare/openrouter-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-siliconflow.json", "title": "Prism Inference vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-stepfun-api.json", "title": "Prism Inference vs StepFun API", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-stepfun-api" }, { "json": "https://www.anchorterminal.com/compare/prism-inference-vs-sambanova.json", "title": "Prism Inference vs SambaCloud", "url": "https://www.anchorterminal.com/compare/prism-inference-vs-sambanova" } ], "scores": [ { "by": 25, "edge": "prism-inference", "key": "reliability", "meta-model-api": 40, "name": "Reliability", "prism-inference": 65, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 22, "edge": "prism-inference", "key": "schema", "meta-model-api": 60, "name": "Schema \u0026 documentation", "prism-inference": 82, "weight": 13 }, { "by": 7, "edge": "meta-model-api", "key": "ergonomics", "meta-model-api": 75, "name": "Agent ergonomics", "prism-inference": 68, "weight": 13 }, { "by": 1, "edge": "prism-inference", "key": "security", "meta-model-api": 64, "name": "Security \u0026 auth", "prism-inference": 65, "weight": 14 }, { "by": 10, "edge": "prism-inference", "key": "payments", "meta-model-api": 20, "name": "Payments \u0026 pricing", "prism-inference": 30, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 3, "edge": "prism-inference", "key": "maintenance", "meta-model-api": 46, "name": "Maintenance \u0026 community", "prism-inference": 49, "weight": 7 }, { "by": 9, "edge": "prism-inference", "key": "transparency", "meta-model-api": 52, "name": "Transparency \u0026 trust", "prism-inference": 61, "weight": 7 } ], "summary": "Prism Inference scores 60.1 (C) on agent readiness against Meta Model API's 52.2 (D), and leads in 6 of 7 scored categories. Meta Model API leads on agent ergonomics. Both do llm inference.", "verdicts": { "meta-model-api": "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.", "prism-inference": "Three wire formats, a public OpenAPI 3.1 file, per-token prices in a keyless catalogue and zero data retention by default on every tier. The service launched on 24 September 2026 with two models, one of them by request, from a two-person company. No rate-limit numbers, SLA document, free tier or deprecation policy was found." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference", "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.md", "slim": "https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.min.md" }, "markdown": "Prism Inference scores 60.1 (C) on agent readiness against Meta Model API's 52.2 (D), and leads in 6 of 7 scored categories. Meta Model API leads on agent ergonomics. Both do llm inference.\n\n- Meta Model API: grade D, 52.2/100, rank #757 of 993. Markdown https://www.anchorterminal.com/tools/meta-model-api.md · JSON https://www.anchorterminal.com/api/v1/tools/meta-model-api.json\n- Prism Inference: grade C, 60.1/100, rank #543 of 993. Markdown https://www.anchorterminal.com/tools/prism-inference.md · JSON https://www.anchorterminal.com/api/v1/tools/prism-inference.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 171 models comparisons: https://www.anchorterminal.com/compare/inference/index.md\n\n## Which one, for what\n\n### Meta Model API (D)\n\nGood for: Long-running agent and coding work over text, images, video, audio and PDFs with a 1M-token context, where training must stay off by default.\n\nAhead on:\n- Agent ergonomics, 75 against 68\n\nWatch for: Muse Wonder is labelled a preview, and no deprecation policy or notice period was found\n\n### Prism Inference (C)\n\nGood for: Coding agents that want DeepSeek-V4.1-Flash at a low input price, with no retention, through whichever of the three wire formats the harness already speaks.\n\nAhead on:\n- Reliability, 65 against 40\n- Schema \u0026 documentation, 82 against 60\n- Payments \u0026 pricing, 30 against 20\n- Transparency \u0026 trust, 61 against 52\n\nWatch for: Two models. The docs mark Gemma 4 31B as request access per organisation, while llms.txt and the keyless catalogue list it as available\n\n\n## Score by category\n\n| Category | Weight | Meta Model API | Prism Inference | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 40 | 65 | Prism Inference +25 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 60 | 82 | Prism Inference +22 |\n| Agent ergonomics | 13% (16.2 this run) | 75 | 68 | Meta Model API +7 |\n| Security \u0026 auth | 14% (17.5 this run) | 64 | 65 | Prism Inference +1 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 20 | 30 | Prism Inference +10 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 46 | 49 | Prism Inference +3 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 52 | 61 | Prism Inference +9 |\n| Negative events | ≤15 | 0 | -2 | |\n| **Total** | | **52.2 · D** | **60.1 · C** | |\n\n## Facts side by side\n\n| Fact | Meta Model API | Prism Inference |\n| --- | --- | --- |\n| Kind | Model API | Model API |\n| Vendor | Meta | Prism Technologies Inc |\n| Hosted endpoint | `https://api.meta.ai/v1` | `https://api.prisminference.com/v1` |\n| Transports | HTTP | HTTP |\n| Auth | API key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | no | no |\n| 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. | Proprietary service under Prism's terms of service. The OpenAPI file declares `LicenseRef-Proprietary`. The Hermes provider plugin repository carries no licence file |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-10-12 | 2026-10-06 |\n| Terms last updated | | 2026-09-09 |\n| Privacy policy last updated | couldn't be read | 2026-09-09 |\n| Customer content may train models | | not found in the text |\n| Terms restrict automated access | | yes |\n| Terms restrict benchmarking | | not found in the text |\n| Terms or service can change without notice | | not found in the text |\n| Arbitration or class-action waiver | | yes |\n\n## Verdicts\n\n**Meta Model API.** 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.\n\n**Prism Inference.** Three wire formats, a public OpenAPI 3.1 file, per-token prices in a keyless catalogue and zero data retention by default on every tier. The service launched on 24 September 2026 with two models, one of them by request, from a two-person company. No rate-limit numbers, SLA document, free tier or deprecation policy was found.\n\n## Before you call either\n\n### Meta Model API\n\n1. 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\n2. Keep personal or confidential data away from `-contributor` model IDs. Meta may train on that content\n3. 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\n4. Pass the key to the OpenAI SDK explicitly or set `OPENAI_API_KEY`. The SDK doesn't read `MODEL_API_KEY`\n5. Leave `tool_choice` at `auto`. The docs say `none` and `required` are not supported\n\n### Prism Inference\n\n1. Call `GET https://api.prisminference.com/v1/models` at start-up, with no key, and use only ids it returns. Expect 403 on `gemma-4-31b` without organisation access\n2. Use base URL `https://api.prisminference.com/v1` for OpenAI clients and `https://api.prisminference.com` with no `/v1` for Anthropic clients\n3. Read `error.retryable` before retrying, and wait for `Retry-After` on 429, which covers both key limits and model capacity\n4. Send `reasoning_effort: \"none\"` or `low` when latency matters. Reasoning is on by default and its tokens are billed as output\n5. Keep conversation state yourself and send `store: false` on Responses. `previous_response_id`, stored responses and hosted tools aren't supported\n\n## Questions\n\n### Which is better for AI agents, Meta Model API or Prism Inference?\n\nPrism Inference scores 60.1 (C) on agent readiness against Meta Model API's 52.2 (D), and leads in 6 of 7 scored categories. Meta Model API leads on agent ergonomics.\n\n### Do Meta Model API and Prism Inference need an API key?\n\nBoth need an API key.\n\n### Can an agent call Meta Model API and Prism Inference without installing anything?\n\nYes. Meta Model API has a hosted endpoint at https://api.meta.ai/v1 and Prism Inference at https://api.prisminference.com/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.json, and with the fewest tokens: https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"meta-model-api\", \"b\": \"prism-inference\"}`. From a terminal: `anchor compare meta-model-api prism-inference`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/meta-model-api.json and https://www.anchorterminal.com/api/v1/tools/prism-inference.json\n\n## Other comparisons with Meta Model API or Prism Inference\n\n- [Claude API vs Meta Model API](https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api.md)\n- [Claude API vs Prism Inference](https://www.anchorterminal.com/compare/anthropic-api-vs-prism-inference.md)\n- [Antseed vs Meta Model API](https://www.anchorterminal.com/compare/antseed-vs-meta-model-api.md)\n- [Antseed vs Prism Inference](https://www.anchorterminal.com/compare/antseed-vs-prism-inference.md)\n- [BlockRun.AI vs Meta Model API](https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.md)\n- [BlockRun.AI vs Prism Inference](https://www.anchorterminal.com/compare/blockrun-ai-vs-prism-inference.md)\n- [Cloudflare AI Gateway vs Meta Model API](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-meta-model-api.md)\n- [Cloudflare AI Gateway vs Prism Inference](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-prism-inference.md)\n- [Cloudflare Workers AI vs Meta Model API](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-meta-model-api.md)\n- [Cloudflare Workers AI vs Prism Inference](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-prism-inference.md)\n- [Cohere Chat API (Command models) vs Meta Model API](https://www.anchorterminal.com/compare/cohere-chat-vs-meta-model-api.md)\n- [Cohere Chat API (Command models) vs Prism Inference](https://www.anchorterminal.com/compare/cohere-chat-vs-prism-inference.md)\n- [DeepInfra vs Meta Model API](https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api.md)\n- [DeepInfra vs Prism Inference](https://www.anchorterminal.com/compare/deepinfra-vs-prism-inference.md)\n- [DeepSeek API vs Meta Model API](https://www.anchorterminal.com/compare/deepseek-api-vs-meta-model-api.md)\n- [DeepSeek API vs Prism Inference](https://www.anchorterminal.com/compare/deepseek-api-vs-prism-inference.md)\n- [Gemini Developer API vs Meta Model API](https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.md)\n- [Gemini Developer API vs Prism Inference](https://www.anchorterminal.com/compare/gemini-api-vs-prism-inference.md)\n- [GroqCloud vs Meta Model API](https://www.anchorterminal.com/compare/groq-vs-meta-model-api.md)\n- [GroqCloud vs Prism Inference](https://www.anchorterminal.com/compare/groq-vs-prism-inference.md)\n- [Meta Model API vs Mistral AI API](https://www.anchorterminal.com/compare/meta-model-api-vs-mistral-api.md)\n- [Meta Model API vs Novita AI](https://www.anchorterminal.com/compare/meta-model-api-vs-novita-ai.md)\n- [Meta Model API vs OpenAI API](https://www.anchorterminal.com/compare/meta-model-api-vs-openai-api.md)\n- [Meta Model API vs OpenRouter](https://www.anchorterminal.com/compare/meta-model-api-vs-openrouter.md)\n- [Meta Model API vs SambaCloud](https://www.anchorterminal.com/compare/meta-model-api-vs-sambanova.md)\n- [Meta Model API vs SiliconFlow](https://www.anchorterminal.com/compare/meta-model-api-vs-siliconflow.md)\n- [Meta Model API vs StepFun API](https://www.anchorterminal.com/compare/meta-model-api-vs-stepfun-api.md)\n- [Mistral AI API vs Prism Inference](https://www.anchorterminal.com/compare/mistral-api-vs-prism-inference.md)\n- [Novita AI vs Prism Inference](https://www.anchorterminal.com/compare/novita-ai-vs-prism-inference.md)\n- [OpenAI API vs Prism Inference](https://www.anchorterminal.com/compare/openai-api-vs-prism-inference.md)\n- [OpenRouter vs Prism Inference](https://www.anchorterminal.com/compare/openrouter-vs-prism-inference.md)\n- [Prism Inference vs SiliconFlow](https://www.anchorterminal.com/compare/prism-inference-vs-siliconflow.md)\n- [Prism Inference vs StepFun API](https://www.anchorterminal.com/compare/prism-inference-vs-stepfun-api.md)\n- [Prism Inference vs SambaCloud](https://www.anchorterminal.com/compare/prism-inference-vs-sambanova.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-12", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.4", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Meta Model API vs Prism Inference", "url": "" } ], "description": "Prism Inference scores 60.1 (C) to Meta Model's 52.2 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Meta Model API D 52.2", "Prism Inference C 60.1", "scores" ], "h1": "Meta Model API vs Prism Inference", "image": "https://www.anchorterminal.com/assets/og/compare-meta-model-api-vs-prism-inference.png", "path": "/compare/meta-model-api-vs-prism-inference", "published": "2026-10-01", "section": "tools", "title": "Meta Model vs Prism Inference for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-12", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference" }, "tokens": { "markdown": 2900, "slim": 730 }, "version": 1 }