{ "data": { "a": { "slug": "blockrun-ai", "name": "BlockRun.AI", "vendor": "BlockRun, Inc.", "vendorUrl": "https://blockrun.ai", "kind": "mcp", "category": "inference", "summary": "Model and API gateway with an OpenAI-compatible interface, MCP support and per-request stablecoin payments through x402.", "url": "https://www.anchorterminal.com/tools/blockrun-ai", "markdownUrl": "https://www.anchorterminal.com/tools/blockrun-ai.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/blockrun-ai.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/blockrun-ai.json", "repo": "https://github.com/BlockRunAI/blockrun-llm", "license": "MIT", "transports": [ "stdio" ], "packages": [ { "registry": "pypi", "name": "blockrun-llm" }, { "registry": "npm", "name": "@blockrun/llm" }, { "registry": "npm", "name": "@blockrun/mcp" } ], "auth": "mixed", "authNotes": "Either an API key backed by prepaid card credit (user.blockrun.ai) or a non-custodial wallet signing x402 USDC payments (Base mainnet at https://blockrun.ai/api, Solana at https://sol.blockrun.ai/api, testnets available).", "pricing": "usage", "pricingNotes": "Per-token LLM pricing by model ($0.20-$30 per 1M input, $0.40-$180 per 1M output per SDK README; site examples $10/$50 for Claude Fable 5.1, $4/$20 for GPT-5.6 Sol per 1M tokens); per-token chat models billed at zero platform margin since 2026-08-08, plus a flat $0.001 network fee per wallet call on Base since 2026-07-29 (the account rail and the Solana gateway charge no fee); images $0.015-$0.10; web search $0.011/search; some data endpoints $0.002-$0.0085 per call; six or seven free models (the vendor's repository says 6, its llms.txt names 7, after four NVIDIA free models retired on 2026-08-30); failed calls (400, 402, 429, 5xx) aren't charged; x402 in USDC on Base or Solana (https://blockrun.ai/docs/resources/changelog; https://blockrun.ai/docs/api-reference/errors; https://github.com/BlockRunAI/blockrun-mcp).", "priceSummary": "Pay per use", "where": "local", "x402": { "level": "yes", "evidence": "Core business model: x402 v2 micropayments in USDC (ERC-20 on Base mainnet, SPL on Solana) per request; the '~$0.002/call' figure could NOT be verified for LLM inference, which is per-token; $0.002 appears only for trading-market/stock data endpoints (https://github.com/BlockRunAI/blockrun-llm; https://github.com/BlockRunAI/awesome-blockrun)", "endpoints": [ { "url": "https://blockrun.ai/api/v1/chat/completions", "priceUsd": null, "network": "eip155:8453" } ] }, "toolCount": 19, "popularity": { "githubStars": 5, "npmWeekly": null, "pypiWeekly": 679, "asOf": "2026-09-26" }, "docsUrl": "https://github.com/BlockRunAI/awesome-blockrun", "llmsTxt": "https://blockrun.ai/llms.txt", "openapi": "https://blockrun.ai/api/openapi", "capabilities": [ "inference.llm", "web.search", "market.crypto" ], "tags": [ "hosted", "x402", "inference", "openai-compatible" ], "lastRelease": "2026-09-29", "graded": true, "anchor": { "graded": true, "score": 72.4, "grade": "BB", "agentReady": true, "rank": 104, "ranked": true, "rankOf": 993, "categoryRank": 4, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 70, "maintenance": 85, "payments": 100, "reliability": 55, "schema": 75, "security": 70, "transparency": 65 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-01" }, "negative": 0, "verdict": "x402 v2 on its own gateway, USDC on Base and Solana, with Base Sepolia for testing. The status page shows live checks only and keeps no incident history.", "bestFor": "Agents that must start and pay with no human sign-up, and builders testing x402 against a real inference gateway.", "strengths": [ "x402 v2 on its own gateway, USDC on Base and Solana, with Base Sepolia for testing", "Rate limits and 429 headers published, and 400, 402, 429 and 5xx responses aren't charged", "Privacy policy says prompts aren't stored past the request and aren't used for training, with request metadata kept 30 days", "The MCP server marks every one of its 19 tools with readOnlyHint or destructiveHint, and trades need `confirm:true`", "Dated gateway changelog with entries most weeks, last on 2026-09-29" ], "weaknesses": [ "The status page shows live checks only and keeps no incident history", "The terms say 'We do not commit to any particular uptime', and there's no SLA", "Models are removed on the day, with no notice period, and the terms let providers withdraw a model at any time", "No subprocessor list or data locations, and the privacy footer names a different entity from the terms", "The wallet key controls the whole balance and sits in a local file or environment variable" ], "agentNotes": [ "Fund a dedicated wallet with a small USDC balance. The balance is the spending cap", "Use `blockrun_wallet` delegate with `agent_limit` before handing a sub-agent the server", "On a 429, wait `Retry-After` and read `X-RateLimit-Source` to fail over to the same tier on another provider", "Call `blockrun_models` before hard-coding a model name, since models are removed without notice", "Use `mode:\"free\"` for drafts and classification, paid models for generation" ], "metrics": { "kind": "remote", "measured": false }, "reviewCount": 2, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.4", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 72.4 } ], "editorialScores": { "ergonomics": 70, "maintenance": 85, "payments": 100, "reliability": 55, "schema": 75, "security": 70, "transparency": 50 }, "provenanceScore": 80 }, "connect": { "claudeCode": "claude mcp add blockrun -- npx -y @blockrun/mcp", "config": { "mcpServers": { "blockrun": { "args": [ "-y", "@blockrun/mcp" ], "command": "npx", "env": { "BLOCKRUN_WALLET_KEY": "${BLOCKRUN_WALLET_KEY}" } } } }, "x402": "curl -s -i https://blockrun.ai/api/v1/chat/completions -H 'content-type: application/json' -d '{\"model\":\"\u003cmodel\u003e\",\"messages\":[{\"role\":\"user\",\"content\":\"hi\"}]}'\n# 402 Payment Required -\u003e PAYMENT-REQUIRED header -\u003e retry with PAYMENT-SIGNATURE" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/blockrun-ai" }, "area": "models", "provenance": { "legalEntity": "BlockRun, Inc.", "domain": "blockrun.ai", "domainRegistered": "2025-10-06", "domainNote": "The terms (updated 2026-09-02) name BlockRun, Inc., a Delaware corporation headquartered in San Francisco, while the privacy policy footer says BlockRun Labs, Inc. No founders or street address are published. The domain is under a year old. The status page is self-hosted and keeps no incident history.", "endpointOnVendorDomain": null, "terms": "https://blockrun.ai/terms", "privacy": "https://blockrun.ai/privacy", "statusPage": "https://blockrun.ai/status", "changelog": "https://blockrun.ai/docs/resources/changelog", "securityTxt": "valid", "checked": "2026-10-02", "score": 80 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/blockrun-ai.json", "live": { "slug": "blockrun-ai", "vendorStatus": { "page": "https://blockrun.ai/status", "indicator": "unknown", "summary": "no machine-readable status found", "checkedAt": "2026-10-12T21:46:30.2991678Z" }, "versions": [ { "registry": "github", "name": "BlockRunAI/blockrun-llm", "version": "v1.20.0", "released": "2026-10-12", "seenAt": "2026-10-12T21:51:25.051089491Z" }, { "registry": "npm", "name": "@blockrun/llm", "version": "3.24.0", "seenAt": "2026-10-12T21:51:22.841703721Z" }, { "registry": "npm", "name": "@blockrun/mcp", "version": "0.54.4", "seenAt": "2026-10-12T21:51:23.675130611Z" }, { "registry": "pypi", "name": "blockrun-llm", "version": "1.20.0", "released": "2026-10-12", "seenAt": "2026-10-12T21:51:22.723336484Z" } ], "githubStars": 5, "npmWeekly": 4752, "pypiWeekly": 543, "securityTxt": { "url": "https://blockrun.ai/.well-known/security.txt", "state": "valid", "expires": "2027-10-12T15:43:33.238Z", "checkedAt": "2026-10-12T15:43:33.027233086Z" }, "llmsTxt": { "url": "https://blockrun.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-12T14:08:32.673120911Z" }, "domain": { "domain": "blockrun.ai", "registered": "2025-10-06", "source": "https://rdap.identitydigital.services/rdap/domain/blockrun.ai", "checkedAt": "2026-10-11T13:18:50.395701416Z" }, "pages": [ { "url": "https://blockrun.ai/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-01T13:11:21.906696649Z", "changedAt": "2026-09-30T13:09:43.712367412Z", "fingerprint": "658ca7fbc8ce" }, { "url": "https://blockrun.ai/docs/resources/changelog", "kind": "changelog", "status": 200, "checkedAt": "2026-10-12T19:46:02.523466798Z", "changedAt": "2026-10-12T19:46:02.523466798Z", "fingerprint": "dfa5ab788b33" }, { "url": "https://blockrun.ai/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-12T19:46:05.429817707Z", "changedAt": "2026-10-09T18:33:12.706249242Z", "fingerprint": "39de12256659" }, { "url": "https://blockrun.ai/terms", "kind": "terms", "status": 200, "checkedAt": "2026-10-12T19:46:06.765704745Z", "changedAt": "2026-10-09T18:33:14.51765012Z", "fingerprint": "51bc10d5065d" } ], "updatedAt": "2026-10-12T21:51:25.051089491Z" } }, "answer": "BlockRun.AI scores 72.4 (BB) 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": "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" } }, "facts": [ { "a": "MCP server", "b": "Model API", "name": "Kind" }, { "a": "BlockRun, Inc.", "b": "Meta", "name": "Vendor" }, { "a": "no (local only)", "b": "https://api.meta.ai/v1", "name": "Hosted endpoint" }, { "a": "stdio", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "API key", "name": "Auth" }, { "a": "Pay per use", "b": "Pay per use", "name": "Pricing" }, { "a": "yes", "b": "no", "name": "x402" }, { "a": "MIT", "b": "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.", "name": "Licence" }, { "a": "19", "b": "none", "name": "Tools exposed" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "yes", "name": "llms.txt" }, { "a": "2026-09-29", "b": "2026-10-12", "name": "Last release" }, { "a": "2026-09-02", "b": "", "name": "Terms last updated" }, { "a": "2026-09-02", "b": "couldn't be read", "name": "Privacy policy last updated" }, { "a": "not found in the text", "b": "", "name": "Customer content may train models" }, { "a": "not found in the text", "b": "", "name": "Terms restrict automated access" }, { "a": "not found in the text", "b": "", "name": "Terms restrict benchmarking" }, { "a": "not found in the text", "b": "", "name": "Terms or service can change without notice" }, { "a": "not found in the text", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "5 stars, 679 PyPI/wk", "b": "none", "name": "Popularity" }, { "a": "3.5/5 (2)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "BlockRun.AI scores 72.4 (BB) 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, BlockRun.AI or Meta Model API?" }, { "answer": "BlockRun.AI takes an API key or an OAuth sign-in. Meta Model API needs an API key. An agent can also pay BlockRun.AI per call over x402, with no account.", "question": "Do BlockRun.AI and Meta Model API need an API key?" }, { "answer": "BlockRun.AI runs on your own machine, with no hosted endpoint listed. Meta Model API has a hosted endpoint at https://api.meta.ai/v1.", "question": "Can an agent call BlockRun.AI and Meta Model API without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 55 against 40", "Schema \u0026 documentation, 75 against 60", "Security \u0026 auth, 70 against 64", "Payments \u0026 pricing, 100 against 20", "Maintenance \u0026 community, 85 against 46", "Transparency \u0026 trust, 65 against 52" ], "also": [ "Agent-ready, a grade of BB or better", "An agent can pay per call over x402, with no account", "Runs on your own machine" ], "goodFor": "Agents that must start and pay with no human sign-up, and builders testing x402 against a real inference gateway.", "slug": "blockrun-ai", "watchFor": "The status page shows live checks only and keeps no incident history" }, { "aheadOn": [ "Agent ergonomics, 75 against 70" ], "also": [ "A hosted endpoint, with nothing to install" ], "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" } ], "job": { "capability": "inference.llm", "name": "LLM inference" }, "others": [ { "json": "https://www.anchorterminal.com/compare/anthropic-api-vs-blockrun-ai.json", "title": "Claude API vs BlockRun.AI", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-blockrun-ai" }, { "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/antseed-vs-blockrun-ai.json", "title": "Antseed vs BlockRun.AI", "url": "https://www.anchorterminal.com/compare/antseed-vs-blockrun-ai" }, { "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/blockrun-ai-vs-cloudflare-ai-gateway.json", "title": "BlockRun.AI vs Cloudflare AI Gateway", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-cloudflare-ai-gateway" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-cloudflare-workers-ai.json", "title": "BlockRun.AI vs Cloudflare Workers AI", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-cloudflare-workers-ai" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-cohere-chat.json", "title": "BlockRun.AI vs Cohere Chat API (Command models)", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-cohere-chat" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-deepinfra.json", "title": "BlockRun.AI vs DeepInfra", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-deepinfra" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-deepseek-api.json", "title": "BlockRun.AI vs DeepSeek API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-deepseek-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api.json", "title": "BlockRun.AI vs Gemini Developer API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-groq.json", "title": "BlockRun.AI vs GroqCloud", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-groq" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-mistral-api.json", "title": "BlockRun.AI vs Mistral AI API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-mistral-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-novita-ai.json", "title": "BlockRun.AI vs Novita AI", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-openai-api.json", "title": "BlockRun.AI vs OpenAI API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-openrouter.json", "title": "BlockRun.AI vs OpenRouter", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-openrouter" }, { "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/blockrun-ai-vs-sambanova.json", "title": "BlockRun.AI vs SambaCloud", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-siliconflow.json", "title": "BlockRun.AI vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-stepfun-api.json", "title": "BlockRun.AI vs StepFun API", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-stepfun-api" }, { "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-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/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/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/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/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/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/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-prism-inference.json", "title": "Meta Model API vs Prism Inference", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference" }, { "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" } ], "scores": [ { "blockrun-ai": 55, "by": 15, "edge": "blockrun-ai", "key": "reliability", "meta-model-api": 40, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "blockrun-ai": 75, "by": 15, "edge": "blockrun-ai", "key": "schema", "meta-model-api": 60, "name": "Schema \u0026 documentation", "weight": 13 }, { "blockrun-ai": 70, "by": 5, "edge": "meta-model-api", "key": "ergonomics", "meta-model-api": 75, "name": "Agent ergonomics", "weight": 13 }, { "blockrun-ai": 70, "by": 6, "edge": "blockrun-ai", "key": "security", "meta-model-api": 64, "name": "Security \u0026 auth", "weight": 14 }, { "blockrun-ai": 100, "by": 80, "edge": "blockrun-ai", "key": "payments", "meta-model-api": 20, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "blockrun-ai": 85, "by": 39, "edge": "blockrun-ai", "key": "maintenance", "meta-model-api": 46, "name": "Maintenance \u0026 community", "weight": 7 }, { "blockrun-ai": 65, "by": 13, "edge": "blockrun-ai", "key": "transparency", "meta-model-api": 52, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "BlockRun.AI scores 72.4 (BB) 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. BlockRun.AI accepts x402. Meta Model API doesn't.", "verdicts": { "blockrun-ai": "x402 v2 on its own gateway, USDC on Base and Solana, with Base Sepolia for testing. The status page shows live checks only and keeps no incident history.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api", "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.md", "slim": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.min.md" }, "markdown": "BlockRun.AI scores 72.4 (BB) 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. BlockRun.AI accepts x402. Meta Model API doesn't.\n\n- BlockRun.AI: grade BB, 72.4/100, rank #104 of 993. Markdown https://www.anchorterminal.com/tools/blockrun-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/blockrun-ai.json\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- 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### BlockRun.AI (BB)\n\nGood for: Agents that must start and pay with no human sign-up, and builders testing x402 against a real inference gateway.\n\nAhead on:\n- Reliability, 55 against 40\n- Schema \u0026 documentation, 75 against 60\n- Security \u0026 auth, 70 against 64\n- Payments \u0026 pricing, 100 against 20\n- Maintenance \u0026 community, 85 against 46\n- Transparency \u0026 trust, 65 against 52\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- An agent can pay per call over x402, with no account\n- Runs on your own machine\n\nWatch for: The status page shows live checks only and keeps no incident history\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 70\n\nAlso in its favour:\n- A hosted endpoint, with nothing to install\n\nWatch for: Muse Wonder is labelled a preview, and no deprecation policy or notice period was found\n\n\n## Score by category\n\n| Category | Weight | BlockRun.AI | Meta Model API | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 40 | BlockRun.AI +15 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 75 | 60 | BlockRun.AI +15 |\n| Agent ergonomics | 13% (16.2 this run) | 70 | 75 | Meta Model API +5 |\n| Security \u0026 auth | 14% (17.5 this run) | 70 | 64 | BlockRun.AI +6 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 100 | 20 | BlockRun.AI +80 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 85 | 46 | BlockRun.AI +39 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 65 | 52 | BlockRun.AI +13 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.4 · BB** | **52.2 · D** | |\n\n## Facts side by side\n\n| Fact | BlockRun.AI | Meta Model API |\n| --- | --- | --- |\n| Kind | MCP server | Model API |\n| Vendor | BlockRun, Inc. | Meta |\n| Hosted endpoint | no (local only) | `https://api.meta.ai/v1` |\n| Transports | stdio | HTTP |\n| Auth | OAuth or key | API key |\n| Pricing | Pay per use | Pay per use |\n| x402 | yes | no |\n| Licence | MIT | 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. |\n| Tools exposed | 19 | none |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-09-29 | 2026-10-12 |\n| Terms last updated | 2026-09-02 | |\n| Privacy policy last updated | 2026-09-02 | couldn't be read |\n| Customer content may train models | not found in the text | |\n| Terms restrict automated access | not found in the text | |\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 | not found in the text | |\n| Popularity | 5 stars, 679 PyPI/wk | none |\n| Agent reviews | 3.5/5 (2) | none |\n\n## Verdicts\n\n**BlockRun.AI.** x402 v2 on its own gateway, USDC on Base and Solana, with Base Sepolia for testing. The status page shows live checks only and keeps no incident history.\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## Before you call either\n\n### BlockRun.AI\n\n1. Fund a dedicated wallet with a small USDC balance. The balance is the spending cap\n2. Use `blockrun_wallet` delegate with `agent_limit` before handing a sub-agent the server\n3. On a 429, wait `Retry-After` and read `X-RateLimit-Source` to fail over to the same tier on another provider\n4. Call `blockrun_models` before hard-coding a model name, since models are removed without notice\n5. Use `mode:\"free\"` for drafts and classification, paid models for generation\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## Questions\n\n### Which is better for AI agents, BlockRun.AI or Meta Model API?\n\nBlockRun.AI scores 72.4 (BB) 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 BlockRun.AI and Meta Model API need an API key?\n\nBlockRun.AI takes an API key or an OAuth sign-in. Meta Model API needs an API key. An agent can also pay BlockRun.AI per call over x402, with no account.\n\n### Can an agent call BlockRun.AI and Meta Model API without installing anything?\n\nBlockRun.AI runs on your own machine, with no hosted endpoint listed. Meta Model API has a hosted endpoint at https://api.meta.ai/v1.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.json, and with the fewest tokens: https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"blockrun-ai\", \"b\": \"meta-model-api\"}`. From a terminal: `anchor compare blockrun-ai meta-model-api`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/blockrun-ai.json and https://www.anchorterminal.com/api/v1/tools/meta-model-api.json\n\n## Other comparisons with BlockRun.AI or Meta Model API\n\n- [Claude API vs BlockRun.AI](https://www.anchorterminal.com/compare/anthropic-api-vs-blockrun-ai.md)\n- [Claude API vs Meta Model API](https://www.anchorterminal.com/compare/anthropic-api-vs-meta-model-api.md)\n- [Antseed vs BlockRun.AI](https://www.anchorterminal.com/compare/antseed-vs-blockrun-ai.md)\n- [Antseed vs Meta Model API](https://www.anchorterminal.com/compare/antseed-vs-meta-model-api.md)\n- [BlockRun.AI vs Cloudflare AI Gateway](https://www.anchorterminal.com/compare/blockrun-ai-vs-cloudflare-ai-gateway.md)\n- [BlockRun.AI vs Cloudflare Workers AI](https://www.anchorterminal.com/compare/blockrun-ai-vs-cloudflare-workers-ai.md)\n- [BlockRun.AI vs Cohere Chat API (Command models)](https://www.anchorterminal.com/compare/blockrun-ai-vs-cohere-chat.md)\n- [BlockRun.AI vs DeepInfra](https://www.anchorterminal.com/compare/blockrun-ai-vs-deepinfra.md)\n- [BlockRun.AI vs DeepSeek API](https://www.anchorterminal.com/compare/blockrun-ai-vs-deepseek-api.md)\n- [BlockRun.AI vs Gemini Developer API](https://www.anchorterminal.com/compare/blockrun-ai-vs-gemini-api.md)\n- [BlockRun.AI vs GroqCloud](https://www.anchorterminal.com/compare/blockrun-ai-vs-groq.md)\n- [BlockRun.AI vs Mistral AI API](https://www.anchorterminal.com/compare/blockrun-ai-vs-mistral-api.md)\n- [BlockRun.AI vs Novita AI](https://www.anchorterminal.com/compare/blockrun-ai-vs-novita-ai.md)\n- [BlockRun.AI vs OpenAI API](https://www.anchorterminal.com/compare/blockrun-ai-vs-openai-api.md)\n- [BlockRun.AI vs OpenRouter](https://www.anchorterminal.com/compare/blockrun-ai-vs-openrouter.md)\n- [BlockRun.AI vs Prism Inference](https://www.anchorterminal.com/compare/blockrun-ai-vs-prism-inference.md)\n- [BlockRun.AI vs SambaCloud](https://www.anchorterminal.com/compare/blockrun-ai-vs-sambanova.md)\n- [BlockRun.AI vs SiliconFlow](https://www.anchorterminal.com/compare/blockrun-ai-vs-siliconflow.md)\n- [BlockRun.AI vs StepFun API](https://www.anchorterminal.com/compare/blockrun-ai-vs-stepfun-api.md)\n- [Cloudflare AI Gateway vs Meta Model API](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-meta-model-api.md)\n- [Cloudflare Workers AI vs Meta Model API](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-meta-model-api.md)\n- [Cohere Chat API (Command models) vs Meta Model API](https://www.anchorterminal.com/compare/cohere-chat-vs-meta-model-api.md)\n- [DeepInfra vs Meta Model API](https://www.anchorterminal.com/compare/deepinfra-vs-meta-model-api.md)\n- [DeepSeek API vs Meta Model API](https://www.anchorterminal.com/compare/deepseek-api-vs-meta-model-api.md)\n- [Gemini Developer API vs Meta Model API](https://www.anchorterminal.com/compare/gemini-api-vs-meta-model-api.md)\n- [GroqCloud vs Meta Model API](https://www.anchorterminal.com/compare/groq-vs-meta-model-api.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 Prism Inference](https://www.anchorterminal.com/compare/meta-model-api-vs-prism-inference.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", "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": "BlockRun.AI vs Meta Model API", "url": "" } ], "description": "BlockRun.AI scores 72.4 (BB) to Meta Model's 52.2 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "BlockRun.AI BB 72.4", "Meta Model API D 52.2", "scores" ], "h1": "BlockRun.AI vs Meta Model API", "image": "https://www.anchorterminal.com/assets/og/compare-blockrun-ai-vs-meta-model-api.png", "path": "/compare/blockrun-ai-vs-meta-model-api", "published": "2026-10-01", "section": "tools", "title": "BlockRun.AI vs Meta Model for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-12", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api" }, "tokens": { "markdown": 2800, "slim": 780 }, "version": 1 }