# BlockRun.AI vs Muse Spark Open

> 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.

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- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Licence: 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.
- Updated: 2026-10-15

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.

- 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
- 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
- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md
- All 210 models comparisons: https://www.anchorterminal.com/compare/inference/index.md

## Which one, for what

### BlockRun.AI (BB)

Good 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)

Good 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

| Category | Weight | BlockRun.AI | Muse Spark Open | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 55 | 42 | BlockRun.AI +13 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 75 | 61 | BlockRun.AI +14 |
| Agent ergonomics | 13% (16.2 this run) | 70 | 69 | BlockRun.AI +1 |
| Security & auth | 14% (17.5 this run) | 70 | 44 | BlockRun.AI +26 |
| Payments & pricing | 10% (12.5 this run) | 100 | 50 | BlockRun.AI +50 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 85 | 60 | BlockRun.AI +25 |
| Transparency & trust | 7% (8.8 this run) | 65 | 53 | BlockRun.AI +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **72.4 · BB** | **53.4 · D** | |

## Facts side by side

| Fact | BlockRun.AI | Muse Spark Open |
| --- | --- | --- |
| Kind | MCP server | Model API |
| Vendor | BlockRun, Inc. | Meta |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | stdio | HTTP |
| Auth | OAuth or key | None |
| Pricing | Pay per use | Free |
| x402 | yes | no |
| 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 |
| Tools exposed | 19 | none |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-09-29 | 2026-10-15 |
| Terms last updated | 2026-09-02 |  |
| Privacy policy last updated | 2026-09-02 | no document linked |
| Customer content may train models | not found in the text |  |
| Terms restrict automated access | not found in the text |  |
| Terms restrict benchmarking | not found in the text |  |
| Terms or service can change without notice | not found in the text |  |
| Arbitration or class-action waiver | not found in the text |  |
| Popularity | 5 stars, 679 PyPI/wk | none |
| Agent reviews | 3.5/5 (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.


## For agents

- 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
- 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`
- 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

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