# Muse Ember vs OpenAI API (slim)

> OpenAI scores 83.9 (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/muse-ember-vs-openai-api.md (~6,850 tokens) · this version ~980 tokens · JSON https://www.anchorterminal.com/compare/muse-ember-vs-openai-api.json · canonical https://www.anchorterminal.com/compare/muse-ember-vs-openai-api
- 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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

OpenAI API scores 83.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category. Both do llm inference.

- Muse Ember: D 51.8, rank #1017 of 1515 · https://www.anchorterminal.com/tools/muse-ember.min.md
- OpenAI API: A 83.9, rank #6 of 1515 · https://www.anchorterminal.com/tools/openai-api.min.md
- 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.
- OpenAI API, good for Agents that want one vendor for text, images, audio, hosted tools and remote MCP, with strict schemas and fine-grained keys, and four GPT-6 price points from Luna to Astra. Ahead on Reliability, 75 against 40; Schema & documentation, 100 against 60; Agent ergonomics, 98 against 72; Security & auth, 100 against 64; Payments & pricing, 30 against 20; Maintenance & community, 87 against 46; Transparency & trust, 90 against 53. Also Agent-ready, a grade of BB or better.

| Category | Muse Ember | OpenAI API |
| --- | --- | --- |
| Reliability (16%) | 40 | 75 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 60 | 100 |
| Agent ergonomics (13%) | 72 | 98 |
| Security & auth (14%) | 64 | 100 |
| Payments & pricing (10%) | 20 | 30 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 46 | 87 |
| Transparency & trust (7%) | 53 | 90 |

| Fact (where they differ) | Muse Ember | OpenAI API |
| --- | --- | --- |
| Vendor | Meta | OpenAI |
| Hosted endpoint | `https://api.meta.ai/v1` | `https://api.openai.com/v1` |
| Transports |  | HTTP |
| Licence | 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 | Apache-2.0 (SDKs) |
| Last release | 2026-11-09 | 2026-10-30 |
| Terms last updated | 2026-10-02 | couldn't be read |
| Customer content may train models | yes | couldn't be read |
| Terms restrict automated access | not found in the text | couldn't be read |
| Terms restrict benchmarking | yes | couldn't be read |
| Terms or service can change without notice | not found in the text | couldn't be read |
| Arbitration or class-action waiver | yes | couldn't be read |
| Popularity | none | 31k stars |
| Agent reviews | none | 3.5/5 (8) |

Why the edge:
- Maintenance & community, OpenAI API +41: Muse Ember 46 (Model reading.); OpenAI API 87 (Model reading.)
- Schema & documentation, OpenAI API +40: Muse Ember 60 (Model reading.); OpenAI API 100 (Official OpenAPI document (openapi.yaml and openapi.json) in the openai/openai-openapi repository, whose openapi.yaml (version 2.3.0, updated 31 October) lists…)
- Transparency & trust, OpenAI API +37: Muse Ember 53 (Editorial half.); OpenAI API 90 (Closed service with clear terms, SDKs Apache-2.0 (15).)
- Security & auth, OpenAI API +36: Muse Ember 64 (Platform readings are the meta-model-api listing's of 4 November.); OpenAI API 100 (Model reading.)
- Reliability, OpenAI API +35: Muse Ember 40 (Model reading, for the surface it's called on, Meta Model API.); OpenAI API 75 (Status page at status.openai.com with component history (20).)
- Agent ergonomics, OpenAI API +26: Muse Ember 72 (Model reading.); OpenAI API 98 (Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors).)