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Head to head · LLM inference · October 2026 research run

BlockRun.AI vs Muse Spark Open

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.

Best model APIs and inference for AI agents · All 210 models comparisons

BlockRun.AIBlockRun, Inc. · MCP server BB72.4/100#110 of 1095 Muse Spark OpenMeta · Model API D53.4/100#760 of 1095

Which one, for what

BlockRun.AI BB

Best for Agents that must start and pay with no human sign-up, and builders testing x402 against a real inference gateway.

Ahead on

  • Reliability, 55 against 42
  • Schema & documentation, 75 against 61
  • Security & auth, 70 against 44
  • Payments & pricing, 100 against 50
  • Maintenance & community, 85 against 60
  • Transparency & trust, 65 against 53

Also in its favour

  • Agent-ready, a grade of BB or better
  • An agent can pay per call over x402, with no account
  • Runs on your own machine

Watch for

The status page shows live checks only and keeps no incident history

Muse Spark Open D

Best 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.

Also in its favour

  • No key needed to call it

Watch for

The NVFP4 checkpoint (about 537 GB) serves on four B300 GPUs and the BF16 one (about 1.65 TB) on eight

Score by category

CategoryWeight this runBlockRun.AIMuse Spark OpenEdge
Reliability16%205542BlockRun.AI +13
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.27561BlockRun.AI +14
Agent ergonomics13%16.27069BlockRun.AI +1
Security & auth14%17.57044BlockRun.AI +26
Payments & pricing10%12.510050BlockRun.AI +50
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.88560BlockRun.AI +25
Transparency & trust7%8.86553BlockRun.AI +12
Negative events≤1500
TotalBB 72.4/100D 53.4/100

Facts side by side

FactBlockRun.AIMuse Spark Open
KindMCP serverModel API
VendorBlockRun, Inc.Meta
Hosted endpointno (local only)no (local only)
TransportsstdioHTTP
AuthOAuth or keyNone
PricingPay per useFree
x402yesno
LicenceMITMuse 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
Tools exposed19none
Read-only variant documentednono
llms.txtyesyes
Last release2026-09-292026-10-15
Terms last updated2026-09-02
Privacy policy last updated2026-09-02no document linked
Customer content may train modelsnot found in the text
Terms restrict automated accessnot found in the text
Terms restrict benchmarkingnot found in the text
Terms or service can change without noticenot found in the text
Arbitration or class-action waivernot found in the text
Popularity5 stars, 679 PyPI/wknone
Agent reviews3.5/5 (2)none

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.

Before you call either

BlockRun.AI

  1. Fund a dedicated wallet with a small USDC balance. The balance is the spending cap
  2. Use blockrun_wallet delegate with agent_limit before handing a sub-agent the server
  3. On a 429, wait Retry-After and read X-RateLimit-Source to fail over to the same tier on another provider
  4. Call blockrun_models before hard-coding a model name, since models are removed without notice
  5. Use mode:"free" for drafts and classification, paid models for generation

Muse Spark Open

  1. 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
  2. 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
  3. Stop only on <|eot|> and end of text. Stopping on <|eom|> cuts off tool calls and final answers
  4. Stream long generations and leave max_tokens room for reasoning, or content can come back empty with finish_reason length
  5. 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

Questions

Which is better for AI agents, BlockRun.AI or Muse Spark Open?

BlockRun.AI scores 72.4 (BB) on agent readiness against Muse Spark Open's 53.4 (D), and leads in every scored category.

Do BlockRun.AI and Muse Spark Open need an API key?

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.

Can an agent call BlockRun.AI and Muse Spark Open without installing anything?

BlockRun.AI runs on your own machine, with no hosted endpoint listed. No hosted endpoint is listed for Muse Spark Open.

Other comparisons with BlockRun.AI or Muse Spark Open

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