# Meta Model API vs Muse Ember

> Meta Model and Muse Ember score within a point of each other for llm inference, 52.3 and 51.8 out of 100. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember
- Markdown: https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.md (~4,050 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-09

Meta Model API and Muse Ember score within a point of each other on agent readiness, 52.3 (D) and 51.8 (D). Both do llm inference.

- Meta Model API: grade D, 52.3/100, rank #997 of 1515. Markdown https://www.anchorterminal.com/tools/meta-model-api.md · JSON https://www.anchorterminal.com/api/v1/tools/meta-model-api.json
- Muse Ember: grade D, 51.8/100, rank #1017 of 1515. Markdown https://www.anchorterminal.com/tools/muse-ember.md · JSON https://www.anchorterminal.com/api/v1/tools/muse-ember.json
- Best model APIs and inference for AI agents: https://www.anchorterminal.com/best/inference/index.md
- All 871 models comparisons: https://www.anchorterminal.com/compare/inference/index.md

## Which one, for what

### Meta Model API (D)

Good for: Long-running agent and coding work over text, images, video, audio and PDFs with a 1M-token context, where training must stay off by default.

Watch for: Muse Wonder is labelled a preview, and no deprecation policy or notice period was found

### Muse Ember (D)

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.

Watch for: No weights and no parameter count published, so it can't be run locally


## Score by category

| Category | Weight | Meta Model API | Muse Ember | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 40 | 40 | even |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 60 | 60 | even |
| Agent ergonomics | 13% (16.2 this run) | 75 | 72 | Meta Model API +3 |
| Security & auth | 14% (17.5 this run) | 64 | 64 | even |
| Payments & pricing | 10% (12.5 this run) | 20 | 20 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 46 | 46 | even |
| Transparency & trust | 7% (8.8 this run) | 53 | 53 | even |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **52.3 · D** | **51.8 · D** | |

## Facts side by side

| Fact | Meta Model API | Muse Ember |
| --- | --- | --- |
| Kind | Model API | Model API |
| Vendor | Meta | Meta |
| Hosted endpoint | `https://api.meta.ai/v1` | `https://api.meta.ai/v1` |
| Transports | HTTP |  |
| Auth | API key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| Licence | Closed service under the Meta Model API Terms of Service (last updated 9 October 2026), with Meta Platforms, Inc., or Meta Platforms Ireland Limited in the European Region, as the contracting party. | 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 |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-10-12 | 2026-11-09 |
| Terms last updated | 2026-10-02 | 2026-10-02 |
| Privacy policy last updated | couldn't be read | couldn't be read |
| Customer content may train models | yes | yes |
| Terms restrict automated access | not found in the text | not found in the text |
| Terms restrict benchmarking | yes | yes |
| Terms or service can change without notice | not found in the text | not found in the text |
| Arbitration or class-action waiver | yes | yes |

## How the scores have moved

### Meta Model API

- -0.2 on 30 October 2026: checklist
- +0.3 on 27 October 2026: checklist

### Muse Ember

No score change since it was listed.

## Verdicts

**Meta Model API.** Meta Model API serves Muse Wonder, in preview since 12 October 2026, and Muse Spark with 1M-token context, constrained JSON Schema output, automatic prompt caching and per-token prices published without a login. Standard-mode content is not used for training. There's no free allowance, status history, OpenAPI file, deprecation policy or way to force a tool call.

**Muse Ember.** A cheap, capable model for the repeated parts of agent work, at $0.10 and $0.50 per million tokens with a 1M context and Meta's figures putting it level with Claude Haiku 5.5 on fewer tokens. Launched today on an API with no readable status page, SLA or deprecation policy, and closed, with no weights.

## Before you call either

### Meta Model API

1. Use `muse-wonder-1.0` for long multi-step work and `muse-spark-1.3` for cheaper, high-volume calls. Both take the same requests
2. Keep personal or confidential data away from `-contributor` model IDs. Meta may train on that content
3. Honour Retry-After on 429 and 503, which is 60 seconds for `service_overloaded`, and check `GET https://api.meta.ai/v1/status`, which needs no key
4. Pass the key to the OpenAI SDK explicitly or set `OPENAI_API_KEY`. The SDK doesn't read `MODEL_API_KEY`
5. Leave `tool_choice` at `auto`. The docs say `none` and `required` are not supported

### Muse Ember

1. Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY
2. Set the effort explicitly, reasoning.effort on Responses or reasoning_effort on Chat Completions. It defaults to max, and none isn't accepted; minimal or low suits classification and routing
3. Keep input under 200K tokens a request, cached tokens and previous_response_id history included, or the whole request bills at the long-context rate
4. Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises
5. Use strict structured output for extraction and routing steps
6. In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide

## Questions

### Which is better for AI agents, Meta Model API or Muse Ember?

Meta Model API and Muse Ember score within a point of each other on agent readiness, 52.3 (D) and 51.8 (D).

### Do Meta Model API and Muse Ember need an API key?

Both need an API key.

### Can an agent call Meta Model API and Muse Ember without installing anything?

Yes. Meta Model API has a hosted endpoint at https://api.meta.ai/v1 and Muse Ember at https://api.meta.ai/v1.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.json, and with the fewest tokens: https://www.anchorterminal.com/compare/meta-model-api-vs-muse-ember.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "meta-model-api", "b": "muse-ember"}`. From a terminal: `anchor compare meta-model-api muse-ember`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/meta-model-api.json and https://www.anchorterminal.com/api/v1/tools/muse-ember.json

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