{ "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": 110, "ranked": true, "rankOf": 1095, "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": "2026-10-15T21:44:42.286475437Z" }, "versions": [ { "registry": "github", "name": "BlockRunAI/blockrun-llm", "version": "v1.20.0", "released": "2026-10-12", "seenAt": "2026-10-14T23:42:02.999431006Z" }, { "registry": "npm", "name": "@blockrun/llm", "version": "3.24.0", "seenAt": "2026-10-14T23:42:00.767161098Z" }, { "registry": "npm", "name": "@blockrun/mcp", "version": "0.54.4", "seenAt": "2026-10-14T23:42:01.583106779Z" }, { "registry": "pypi", "name": "blockrun-llm", "version": "1.20.0", "released": "2026-10-12", "seenAt": "2026-10-14T23:42:00.563828802Z" } ], "githubStars": 5, "npmWeekly": 5355, "pypiWeekly": 511, "securityTxt": { "url": "https://blockrun.ai/.well-known/security.txt", "state": "valid", "expires": "2027-10-15T15:47:53.363Z", "checkedAt": "2026-10-15T15:47:53.178126428Z" }, "llmsTxt": { "url": "https://blockrun.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-10-15T14:16:06.902973313Z" }, "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": "2026-10-15T21:51:11.221358169Z", "changedAt": "2026-10-12T19:46:02.523466798Z", "fingerprint": "dfa5ab788b33" }, { "url": "https://blockrun.ai/privacy", "kind": "privacy", "status": 200, "checkedAt": "2026-10-15T21:51:13.645120977Z", "changedAt": "2026-10-13T20:10:50.291177457Z", "fingerprint": "58c04b45c204" }, { "url": "https://blockrun.ai/terms", "kind": "terms", "status": 200, "checkedAt": "2026-10-15T21:51:15.370915312Z", "changedAt": "2026-10-13T20:10:52.022078075Z", "fingerprint": "b1e4605ada87" } ], "updatedAt": "2026-10-15T21:51:15.370915312Z" } }, "answer": "BlockRun.AI scores 72.4 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in every scored category.", "b": { "slug": "muse-spark-open", "name": "Muse Spark Open", "vendor": "Meta", "vendorUrl": "https://dev.meta.ai/models/muse-spark-open", "kind": "model", "category": "inference", "summary": "Muse Spark Open is Meta's open-weight multimodal model for coding and agentic work, with 816B total and 42B active parameters and a 1M-token context, released on 15 October 2026 under the Muse Model Licence for self-hosting.", "url": "https://www.anchorterminal.com/tools/muse-spark-open", "markdownUrl": "https://www.anchorterminal.com/tools/muse-spark-open.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/muse-spark-open.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/muse-spark-open.json", "repo": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B", "license": "Muse Model Licence (source-available weights, not an OSI licence), free of charge with an attribution notice, a separate agreement with Meta before Model as a Service by a licensee group above $20M gross revenue, and the Muse Spark Open Usage Policy", "transports": [ "http" ], "packages": [], "auth": "none", "authNotes": "Running the model needs no account or key. Getting Meta's weights does. Both Hugging Face repositories (BF16 and NVFP4) are gated with manual review and ask for full legal name, date of birth, country, affiliation and job title, and downloads then use a Hugging Face access token. The GGUF quantisations from ggml-org and Unsloth that Meta's docs point to are not gated. A local server has whatever authentication the owner adds, such as llama.cpp's `--api-key`.", "pricing": "free", "pricingNotes": "Free to download and run under the Muse Model Licence, with the owner's GPUs as the cost. A licensee whose group has more than $20M gross revenue in any 12 months must notify Meta and sign a separate agreement before offering it as Model as a Service. Hosted copies cost $1.25 input, $0.15 cached input and $4.25 output per million tokens on Fireworks, Baseten, Modal and OpenRouter (checked 2026-10-15). Meta Model API does not serve it.", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "No x402, MPP or L402. Muse Spark Open is a model the owner runs, with no payment route from Meta, and Meta Model API does not serve it (checked 2026-10-15).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-15" }, "docsUrl": "https://dev.meta.ai/docs/muse-spark-open", "llmsTxt": "https://dev.meta.ai/llms.txt", "capabilities": [ "inference.llm", "inference.open-weights", "inference.local" ], "tags": [ "model", "open-weights", "self-hosted", "local", "free", "gated", "multimodal", "openai-compatible", "llms-txt", "gpu", "new" ], "lastRelease": "2026-10-15", "graded": true, "anchor": { "graded": true, "score": 53.4, "grade": "D", "agentReady": false, "rank": 760, "ranked": true, "rankOf": 1095, "categoryRank": 16, "methodology": "0.4", "run": "2026-10-01", "scores": { "ergonomics": 69, "maintenance": 60, "payments": 50, "reliability": 42, "schema": 61, "security": 44, "transparency": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-15" }, "negative": 0, "verdict": "Muse Spark Open runs on the owner's hardware in seven runtimes, with OpenAI-compatible servers in vLLM and SGLang, standard tool calls, a 1M-token context and Meta's own serving docs. Serving the 537 GB NVFP4 checkpoint takes four B300 GPUs, Meta's download is gated by manual review, and the licence adds attribution and a revenue threshold for model resellers.", "bestFor": "A team with a four-GPU Blackwell node, or two to four Mac Studios, that wants a 1M-token multimodal coding and agent model on its own hardware with no prompts leaving it.", "strengths": [ "816B total and 42B active parameters with a 1,048,576-token context, taking text, image, video and audio input", "Day-one support in vLLM, SGLang, TensorRT-LLM, Transformers, llama.cpp, MLX and Unsloth, with commands for each in Meta's docs", "With the `muse` parsers, reasoning comes back in its own field and tool calls as a standard `tool_calls` array", "The licence grants use, fine-tuning, distillation and resale free of charge, with no user cap and no regional exclusion", "llms.txt and Markdown docs cover checkpoints, prompting, stop tokens, recommended settings and post-training" ], "weaknesses": [ "The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight", "Meta's Hugging Face repositories need manual approval with legal name and date of birth, which the Get the model page doesn't mention", "Products must display \"Built with Muse Spark Open\", and a company group with over $20M revenue needs Meta's agreement to offer it as Model as a Service", "The Usage Policy rules out military, nuclear, espionage, critical infrastructure and heavy machinery uses, and users under 18", "Released on 15 October 2026, with no changelog, deprecation policy or CI found, and the full evaluation report not yet published" ], "agentNotes": [ "Start vLLM or SGLang with `--reasoning-parser muse --tool-call-parser muse` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as raw text", "Set `reasoning_effort` to `low` or `medium` for short tasks. The default is `xhigh`, reasoning can't be turned off, and other values return an error", "Stop only on `\u003c|eot|\u003e` and end of text. Stopping on `\u003c|eom|\u003e` cuts off tool calls and final answers", "Stream long generations and leave `max_tokens` room for reasoning, or `content` can come back empty with `finish_reason` `length`", "Don't call Meta Model API for this model. Use a self-hosted server or a hosted provider's model string, such as `meta-models/muse-spark-open`" ], "metrics": { "kind": "local", "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": 53.4 } ], "editorialScores": { "ergonomics": 69, "maintenance": 60, "payments": 50, "reliability": 42, "schema": 61, "security": 44, "transparency": 50 }, "provenanceScore": 55 }, "connect": { "install": "hf download meta-models/Muse-Spark-Open-816B-A42B-NVFP4 --local-dir ./muse-spark-open-nvfp4\n\nexport VLLM_USE_RUST_FRONTEND=1\nvllm serve meta-models/Muse-Spark-Open-816B-A42B-NVFP4 \\\n --tensor-parallel-size 4 \\\n --enable-expert-parallel \\\n --max-model-len 1048576 \\\n --reasoning-parser muse \\\n --tool-call-parser muse \\\n --enable-auto-tool-choice", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\n \"model\": \"meta-models/Muse-Spark-Open-816B-A42B-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Refactor this module.\"}],\n \"reasoning_effort\": \"xhigh\"\n }'" }, "letme": { "capability": "https://letme.dev/inference.llm", "tool": "https://letme.dev/muse-spark-open" }, "sameCompany": [ "meta-model-api", "muse-code" ], "area": "models", "provenance": { "legalEntity": "Meta Platforms, Inc.", "domain": "meta.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B/blob/main/LICENSE.md", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-15", "notes": [ "The licence names Meta Platforms, Inc. as copyright holder, and defines Meta as Meta Platforms Ireland Limited for licensees in the European region and Meta Platforms, Inc. elsewhere. The model card names the same two as model provider.", "The licence is the document that governs use of the weights, so it is recorded as the terms. LICENSE.md matches the text shown on the gated access form word for word. The Usage Policy is its Exhibit A. The separate USE_POLICY.md that Meta's docs link answered 401 without access and wasn't read.", "No privacy policy governs the model, because the owner runs it and no input reaches Meta. The access form says its details are handled under the Meta Privacy Policy on www.facebook.com, whose robots.txt asks readers like ours not to fetch it, so it wasn't read.", "The domain is meta.ai, where Meta's developer docs (dev.meta.ai) and research post (research.meta.ai) sit, the same as the meta-model-api and muse-code listings. RDAP gives a registration date of 16 December 2017.", "No hosted endpoint from Meta, so no status page. No changelog for the model was found, and the Hugging Face commit history needs access to read.", "dev.meta.ai/.well-known/security.txt and research.meta.ai/.well-known/security.txt returned 404 on 15 October 2026.", "WebFetch read the research post. It refused the Hugging Face model card as outside its allowed set, so the card, LICENSE.md, the repository and collection metadata, the discussions, the evaluation methodology PDF, Meta's docs pages and the providers' model pages were read with curl." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/muse-spark-open.json", "live": { "slug": "muse-spark-open", "pages": [ { "url": "https://huggingface.co/meta-models/Muse-Spark-Open-816B-A42B/blob/main/LICENSE.md", "kind": "terms", "status": 200, "checkedAt": "2026-10-15T21:58:24.678553754Z", "changedAt": "2026-10-15T21:58:24.678553754Z", "fingerprint": "7ffd7a072112" } ], "updatedAt": "2026-10-15T21:58:24.678553754Z" } }, "facts": [ { "a": "MCP server", "b": "Model API", "name": "Kind" }, { "a": "BlockRun, Inc.", "b": "Meta", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "stdio", "b": "HTTP", "name": "Transports" }, { "a": "OAuth or key", "b": "None", "name": "Auth" }, { "a": "Pay per use", "b": "Free", "name": "Pricing" }, { "a": "yes", "b": "no", "name": "x402" }, { "a": "MIT", "b": "Muse Model Licence (source-available weights, not an OSI licence), free of charge with an attribution notice, a separate agreement with Meta before Model as a Service by a licensee group above $20M gross revenue, and the Muse Spark Open Usage Policy", "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": "2026-10-15", "name": "Last release" }, { "a": "2026-09-02", "b": "", "name": "Terms last updated" }, { "a": "2026-09-02", "b": "no document linked", "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 Muse Spark Open's 53.4 (D), and leads in every scored category.", "question": "Which is better for AI agents, BlockRun.AI or Muse Spark Open?" }, { "answer": "BlockRun.AI takes an API key or an OAuth sign-in. Muse Spark Open needs no key. An agent can also pay BlockRun.AI per call over x402, with no account.", "question": "Do BlockRun.AI and Muse Spark Open need an API key?" }, { "answer": "BlockRun.AI runs on your own machine, with no hosted endpoint listed. No hosted endpoint is listed for Muse Spark Open.", "question": "Can an agent call BlockRun.AI and Muse Spark Open without installing anything?" } ], "goodFor": [ { "aheadOn": [ "Reliability, 55 against 42", "Schema \u0026 documentation, 75 against 61", "Security \u0026 auth, 70 against 44", "Payments \u0026 pricing, 100 against 50", "Maintenance \u0026 community, 85 against 60", "Transparency \u0026 trust, 65 against 53" ], "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": null, "also": [ "No key needed to call it" ], "goodFor": "A team with a four-GPU Blackwell node, or two to four Mac Studios, that wants a 1M-token multimodal coding and agent model on its own hardware with no prompts leaving it.", "slug": "muse-spark-open", "watchFor": "The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight" } ], "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-muse-spark-open.json", "title": "Claude API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/anthropic-api-vs-muse-spark-open" }, { "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-muse-spark-open.json", "title": "Antseed vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/antseed-vs-muse-spark-open" }, { "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-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-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-routerplus.json", "title": "BlockRun.AI vs RouterPlus", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-routerplus" }, { "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-muse-spark-open.json", "title": "Cloudflare AI Gateway vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-spark-open.json", "title": "Cloudflare Workers AI vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-spark-open.json", "title": "Cohere Chat API (Command models) vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/cohere-chat-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-spark-open.json", "title": "DeepInfra vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/deepinfra-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-spark-open.json", "title": "DeepSeek API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/deepseek-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open.json", "title": "Gemini Developer API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/groq-vs-muse-spark-open.json", "title": "GroqCloud vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/groq-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-spark-open.json", "title": "Meta Model API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/meta-model-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-spark-open.json", "title": "Mistral AI API vs Muse Spark Open", "url": "https://www.anchorterminal.com/compare/mistral-api-vs-muse-spark-open" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-novita-ai.json", "title": "Muse Spark Open vs Novita AI", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-novita-ai" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openai-api.json", "title": "Muse Spark Open vs OpenAI API", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openai-api" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openrouter.json", "title": "Muse Spark Open vs OpenRouter", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-openrouter" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-prism-inference.json", "title": "Muse Spark Open vs Prism Inference", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-prism-inference" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-routerplus.json", "title": "Muse Spark Open vs RouterPlus", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-routerplus" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova.json", "title": "Muse Spark Open vs SambaCloud", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow.json", "title": "Muse Spark Open vs SiliconFlow", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow" }, { "json": "https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api.json", "title": "Muse Spark Open vs StepFun API", "url": "https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api" } ], "scores": [ { "blockrun-ai": 55, "by": 13, "edge": "blockrun-ai", "key": "reliability", "muse-spark-open": 42, "name": "Reliability", "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "blockrun-ai": 75, "by": 14, "edge": "blockrun-ai", "key": "schema", "muse-spark-open": 61, "name": "Schema \u0026 documentation", "weight": 13 }, { "blockrun-ai": 70, "by": 1, "edge": "blockrun-ai", "key": "ergonomics", "muse-spark-open": 69, "name": "Agent ergonomics", "weight": 13 }, { "blockrun-ai": 70, "by": 26, "edge": "blockrun-ai", "key": "security", "muse-spark-open": 44, "name": "Security \u0026 auth", "weight": 14 }, { "blockrun-ai": 100, "by": 50, "edge": "blockrun-ai", "key": "payments", "muse-spark-open": 50, "name": "Payments \u0026 pricing", "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "blockrun-ai": 85, "by": 25, "edge": "blockrun-ai", "key": "maintenance", "muse-spark-open": 60, "name": "Maintenance \u0026 community", "weight": 7 }, { "blockrun-ai": 65, "by": 12, "edge": "blockrun-ai", "key": "transparency", "muse-spark-open": 53, "name": "Transparency \u0026 trust", "weight": 7 } ], "summary": "BlockRun.AI scores 72.4 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in every scored category. Both do llm inference. BlockRun.AI accepts x402. Muse Spark Open 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.", "muse-spark-open": "Muse Spark Open runs on the owner's hardware in seven runtimes, with OpenAI-compatible servers in vLLM and SGLang, standard tool calls, a 1M-token context and Meta's own serving docs. Serving the 537 GB NVFP4 checkpoint takes four B300 GPUs, Meta's download is gated by manual review, and the licence adds attribution and a revenue threshold for model resellers." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open", "json": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open.md", "slim": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open.min.md" }, "markdown": "BlockRun.AI scores 72.4 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in every scored category. Both do llm inference. BlockRun.AI accepts x402. Muse Spark Open doesn't.\n\n- BlockRun.AI: grade BB, 72.4/100, rank #110 of 1095. Markdown https://www.anchorterminal.com/tools/blockrun-ai.md · JSON https://www.anchorterminal.com/api/v1/tools/blockrun-ai.json\n- Muse Spark Open: grade D, 53.4/100, rank #760 of 1095. Markdown https://www.anchorterminal.com/tools/muse-spark-open.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-spark-open.json\n- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md\n- All 210 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 42\n- Schema \u0026 documentation, 75 against 61\n- Security \u0026 auth, 70 against 44\n- Payments \u0026 pricing, 100 against 50\n- Maintenance \u0026 community, 85 against 60\n- Transparency \u0026 trust, 65 against 53\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### Muse Spark Open (D)\n\nGood for: A team with a four-GPU Blackwell node, or two to four Mac Studios, that wants a 1M-token multimodal coding and agent model on its own hardware with no prompts leaving it.\n\nAlso in its favour:\n- No key needed to call it\n\nWatch for: The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight\n\n\n## Score by category\n\n| Category | Weight | BlockRun.AI | Muse Spark Open | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 55 | 42 | BlockRun.AI +13 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 75 | 61 | BlockRun.AI +14 |\n| Agent ergonomics | 13% (16.2 this run) | 70 | 69 | BlockRun.AI +1 |\n| Security \u0026 auth | 14% (17.5 this run) | 70 | 44 | BlockRun.AI +26 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 100 | 50 | BlockRun.AI +50 |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 85 | 60 | BlockRun.AI +25 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 65 | 53 | BlockRun.AI +12 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **72.4 · BB** | **53.4 · D** | |\n\n## Facts side by side\n\n| Fact | BlockRun.AI | Muse Spark Open |\n| --- | --- | --- |\n| Kind | MCP server | Model API |\n| Vendor | BlockRun, Inc. | Meta |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | stdio | HTTP |\n| Auth | OAuth or key | None |\n| Pricing | Pay per use | Free |\n| x402 | yes | no |\n| Licence | MIT | Muse Model Licence (source-available weights, not an OSI licence), free of charge with an attribution notice, a separate agreement with Meta before Model as a Service by a licensee group above $20M gross revenue, and the Muse Spark Open Usage Policy |\n| Tools exposed | 19 | none |\n| Read-only variant documented | no | no |\n| llms.txt | yes | yes |\n| Last release | 2026-09-29 | 2026-10-15 |\n| Terms last updated | 2026-09-02 | |\n| Privacy policy last updated | 2026-09-02 | no document linked |\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**Muse Spark Open.** Muse Spark Open runs on the owner's hardware in seven runtimes, with OpenAI-compatible servers in vLLM and SGLang, standard tool calls, a 1M-token context and Meta's own serving docs. Serving the 537 GB NVFP4 checkpoint takes four B300 GPUs, Meta's download is gated by manual review, and the licence adds attribution and a revenue threshold for model resellers.\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### Muse Spark Open\n\n1. Start vLLM or SGLang with `--reasoning-parser muse --tool-call-parser muse` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as raw text\n2. Set `reasoning_effort` to `low` or `medium` for short tasks. The default is `xhigh`, reasoning can't be turned off, and other values return an error\n3. Stop only on `\u003c|eot|\u003e` and end of text. Stopping on `\u003c|eom|\u003e` cuts off tool calls and final answers\n4. Stream long generations and leave `max_tokens` room for reasoning, or `content` can come back empty with `finish_reason` `length`\n5. Don't call Meta Model API for this model. Use a self-hosted server or a hosted provider's model string, such as `meta-models/muse-spark-open`\n\n## Questions\n\n### Which is better for AI agents, BlockRun.AI or Muse Spark Open?\n\nBlockRun.AI scores 72.4 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in every scored category.\n\n### Do BlockRun.AI and Muse Spark Open need an API key?\n\nBlockRun.AI takes an API key or an OAuth sign-in. Muse Spark Open needs no key. An agent can also pay BlockRun.AI per call over x402, with no account.\n\n### Can an agent call BlockRun.AI and Muse Spark Open without installing anything?\n\nBlockRun.AI runs on your own machine, with no hosted endpoint listed. No hosted endpoint is listed for Muse Spark Open.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open.json, and with the fewest tokens: https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"blockrun-ai\", \"b\": \"muse-spark-open\"}`. From a terminal: `anchor compare blockrun-ai muse-spark-open`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/blockrun-ai.json and https://www.anchorterminal.com/api/v1/tools/muse-spark-open.json\n\n## Other comparisons with BlockRun.AI or Muse Spark Open\n\n- [Claude API vs BlockRun.AI](https://www.anchorterminal.com/compare/anthropic-api-vs-blockrun-ai.md)\n- [Claude API vs Muse Spark Open](https://www.anchorterminal.com/compare/anthropic-api-vs-muse-spark-open.md)\n- [Antseed vs BlockRun.AI](https://www.anchorterminal.com/compare/antseed-vs-blockrun-ai.md)\n- [Antseed vs Muse Spark Open](https://www.anchorterminal.com/compare/antseed-vs-muse-spark-open.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 Meta Model API](https://www.anchorterminal.com/compare/blockrun-ai-vs-meta-model-api.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 RouterPlus](https://www.anchorterminal.com/compare/blockrun-ai-vs-routerplus.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 Muse Spark Open](https://www.anchorterminal.com/compare/cloudflare-ai-gateway-vs-muse-spark-open.md)\n- [Cloudflare Workers AI vs Muse Spark Open](https://www.anchorterminal.com/compare/cloudflare-workers-ai-vs-muse-spark-open.md)\n- [Cohere Chat API (Command models) vs Muse Spark Open](https://www.anchorterminal.com/compare/cohere-chat-vs-muse-spark-open.md)\n- [DeepInfra vs Muse Spark Open](https://www.anchorterminal.com/compare/deepinfra-vs-muse-spark-open.md)\n- [DeepSeek API vs Muse Spark Open](https://www.anchorterminal.com/compare/deepseek-api-vs-muse-spark-open.md)\n- [Gemini Developer API vs Muse Spark Open](https://www.anchorterminal.com/compare/gemini-api-vs-muse-spark-open.md)\n- [GroqCloud vs Muse Spark Open](https://www.anchorterminal.com/compare/groq-vs-muse-spark-open.md)\n- [Meta Model API vs Muse Spark Open](https://www.anchorterminal.com/compare/meta-model-api-vs-muse-spark-open.md)\n- [Mistral AI API vs Muse Spark Open](https://www.anchorterminal.com/compare/mistral-api-vs-muse-spark-open.md)\n- [Muse Spark Open vs Novita AI](https://www.anchorterminal.com/compare/muse-spark-open-vs-novita-ai.md)\n- [Muse Spark Open vs OpenAI API](https://www.anchorterminal.com/compare/muse-spark-open-vs-openai-api.md)\n- [Muse Spark Open vs OpenRouter](https://www.anchorterminal.com/compare/muse-spark-open-vs-openrouter.md)\n- [Muse Spark Open vs Prism Inference](https://www.anchorterminal.com/compare/muse-spark-open-vs-prism-inference.md)\n- [Muse Spark Open vs RouterPlus](https://www.anchorterminal.com/compare/muse-spark-open-vs-routerplus.md)\n- [Muse Spark Open vs SambaCloud](https://www.anchorterminal.com/compare/muse-spark-open-vs-sambanova.md)\n- [Muse Spark Open vs SiliconFlow](https://www.anchorterminal.com/compare/muse-spark-open-vs-siliconflow.md)\n- [Muse Spark Open vs StepFun API](https://www.anchorterminal.com/compare/muse-spark-open-vs-stepfun-api.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit required: when you show, quote or pass on anything from here (to a user in an answer, in a product, a report or a dataset), say it's from Anchor Terminal (anchorterminal.com) and link the page or API address you took it from. Reviews also credit their author.", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-15", "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", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "BlockRun.AI vs Muse Spark Open", "url": "" } ], "description": "BlockRun.AI scores 72.4 (BB) to Muse Spark Open's 53.4 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "BlockRun.AI BB 72.4", "Muse Spark Open D 53.4", "scores" ], "h1": "BlockRun.AI vs Muse Spark Open", "image": "https://www.anchorterminal.com/assets/og/compare-blockrun-ai-vs-muse-spark-open.png", "path": "/compare/blockrun-ai-vs-muse-spark-open", "published": "2026-10-01", "section": "tools", "title": "BlockRun.AI vs Muse Spark Open for AI agents (2026) | Anchor Terminal", "toc": null, "updated": "2026-10-15", "url": "https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-spark-open" }, "tokens": { "markdown": 2950, "slim": 780 }, "version": 1 }