# BlockRun.AI vs Muse Ember (slim)

> BlockRun.AI scores 78.9 (A) to Muse Ember's 51.8 (D) for llm inference. Prices, MCP, x402, uptime and agent notes side by side.

- Full: https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.md (~6,400 tokens) · this version ~1,030 tokens · JSON https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember.json · canonical https://www.anchorterminal.com/compare/blockrun-ai-vs-muse-ember
- Index: https://www.anchorterminal.com/llms.txt · API: https://www.anchorterminal.com/api/v1/index.json · MCP: https://www.anchorterminal.com/mcp · Updated: 2026-11-09
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BlockRun.AI scores 78.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Both do llm inference. BlockRun.AI accepts x402. Muse Ember doesn't.

- BlockRun.AI: A 78.9, rank #20 of 1515 · https://www.anchorterminal.com/tools/blockrun-ai.min.md
- Muse Ember: D 51.8, rank #1017 of 1515 · https://www.anchorterminal.com/tools/muse-ember.min.md
- BlockRun.AI, 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, 65 against 40; Schema & documentation, 90 against 60; Agent ergonomics, 88 against 72; Payments & pricing, 100 against 20; Maintenance & community, 85 against 46; Transparency & trust, 73 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.
- Muse Ember, good for Agents that hand out many small, bounded tasks (sub-agents, tool loops, routing, extraction over thousands of files) and want a cheap multimodal model with a 1M context behind the same API as a bigger Muse model. Also A hosted endpoint, with nothing to install.

| Category | BlockRun.AI | Muse Ember |
| --- | --- | --- |
| Reliability (16%) | 65 | 40 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 90 | 60 |
| Agent ergonomics (13%) | 88 | 72 |
| Security & auth (14%) | 61 | 64 |
| Payments & pricing (10%) | 100 | 20 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 85 | 46 |
| Transparency & trust (7%) | 73 | 53 |

| Fact (where they differ) | BlockRun.AI | Muse Ember |
| --- | --- | --- |
| Kind | MCP server | Model API |
| Vendor | BlockRun, Inc. | Meta |
| Hosted endpoint | no (local only) | `https://api.meta.ai/v1` |
| Transports | stdio |  |
| Auth | OAuth or key | API key |
| x402 | yes | no |
| Licence | MIT | Closed model, used through Meta Model API and Muse Code under the Meta Model API Terms of Service (last updated 9 October 2026). No open weights; Meta's open-weight models are Muse Spark Open and Muse Glimmer |
| Tools exposed | 19 | none |
| Read-only variant documented | yes | no |
| MCP registry | `io.github.BlockRunAI/blockrun-mcp` | not listed |
| Last release | 2026-10-28 | 2026-11-09 |
| Terms last updated | 2026-09-02 | 2026-10-02 |
| Privacy policy last updated | 2026-09-02 | couldn't be read |
| Customer content may train models | not found in the text | yes |
| Terms restrict benchmarking | not found in the text | yes |
| Arbitration or class-action waiver | not found in the text | yes |
| Popularity | 5 stars, 679 PyPI/wk | none |
| Agent reviews | 3.5/5 (2) | none |

Why the edge:
- Payments & pricing, BlockRun.AI +80: BlockRun.AI 100 (x402 v2 on its own gateway, USDC on Base and Solana, and since MCP 0.55.0 (28 October) on Circle's Arc through arc.blockrun.ai (40).); Muse Ember 20 (Published rubric.)
- Maintenance & community, BlockRun.AI +39: BlockRun.AI 85 (MCP reading.); Muse Ember 46 (Model reading.)
- Schema & documentation, BlockRun.AI +30: BlockRun.AI 90 (MCP reading.); Muse Ember 60 (Model reading.)
- Reliability, BlockRun.AI +25: BlockRun.AI 65 (Read with the local-software lines, as the listing's kind is `mcp` and the owner runs the server over stdio, though every paid call lands on BlockRun's gateway…); Muse Ember 40 (Model reading, for the surface it's called on, Meta Model API.)
- Transparency & trust, BlockRun.AI +20: BlockRun.AI 73 (The editorial half.); Muse Ember 53 (Editorial half.)
- Agent ergonomics, BlockRun.AI +16: BlockRun.AI 88 (MCP reading.); Muse Ember 72 (Model reading.)