# DeepInfra vs Muse Ember (slim)

> DeepInfra scores 63.7 (B) 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/deepinfra-vs-muse-ember.md (~6,200 tokens) · this version ~830 tokens · JSON https://www.anchorterminal.com/compare/deepinfra-vs-muse-ember.json · canonical https://www.anchorterminal.com/compare/deepinfra-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
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)

DeepInfra scores 63.7 (B) on agent readiness against Muse Ember's 51.8 (D), and leads in 5 of 7 scored categories. Both do llm inference.

- DeepInfra: B 63.7, rank #501 of 1515 · https://www.anchorterminal.com/tools/deepinfra.min.md
- Muse Ember: D 51.8, rank #1017 of 1515 · https://www.anchorterminal.com/tools/muse-ember.min.md
- DeepInfra, good for Agents that want many open-weight models, embeddings, image and speech behind one OpenAI-style key at low per-token prices, with spend-capped tokens. Ahead on Reliability, 75 against 40; Schema & documentation, 69 against 60; Agent ergonomics, 77 against 72; Maintenance & community, 61 against 46; Transparency & trust, 68 against 53.
- 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.

| Category | DeepInfra | Muse Ember |
| --- | --- | --- |
| Reliability (16%) | 75 | 40 |
| Performance (10%) | pending | pending |
| Schema & documentation (13%) | 69 | 60 |
| Agent ergonomics (13%) | 77 | 72 |
| Security & auth (14%) | 64 | 64 |
| Payments & pricing (10%) | 20 | 20 |
| Task success (10%) | pending | pending |
| Maintenance & community (7%) | 61 | 46 |
| Transparency & trust (7%) | 68 | 53 |

| Fact (where they differ) | DeepInfra | Muse Ember |
| --- | --- | --- |
| Vendor | Deep Infra Inc. | Meta |
| Hosted endpoint | `https://api.deepinfra.com/v1/openai` | `https://api.meta.ai/v1` |
| Transports | HTTP |  |
| Licence | Proprietary service under the DeepInfra Terms of Service. The Python and Node SDKs and the docs repository are 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 |
| Last release | 2026-10-26 | 2026-11-09 |
| Terms last updated | 2026-08-17 | 2026-10-02 |
| Privacy policy last updated | 2026-08-15 | couldn't be read |
| Customer content may train models | not found in the text | yes |
| Popularity | 21 stars, 1.2k npm/wk, 50 PyPI/wk | none |
| Agent reviews | 3.5/5 (4) | none |

Why the edge:
- Reliability, DeepInfra +35: DeepInfra 75 (Hosted reading.); Muse Ember 40 (Model reading, for the surface it's called on, Meta Model API.)
- Maintenance & community, DeepInfra +15: DeepInfra 61 (Model reading.); Muse Ember 46 (Model reading.)
- Transparency & trust, DeepInfra +15: DeepInfra 68 (Closed service with terms that name the entity and California law, and an MIT licence on the SDKs and docs (15).); Muse Ember 53 (Editorial half.)
- Schema & documentation, DeepInfra +9: DeepInfra 69 (Model reading.); Muse Ember 60 (Model reading.)
- Agent ergonomics, DeepInfra +5: DeepInfra 77 (Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors).); Muse Ember 72 (Model reading.)