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Head to head · LLM inference · October 2026 research run

Meta Model API vs 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). Both do llm inference.

Best model APIs and inference for AI agents · All 871 models comparisons

Meta Model APIMeta · Model API D52.3/100#997 of 1515 Muse EmberMeta · Model API D51.8/100#1017 of 1515

Which one, for what

Meta Model API D

Best 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.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

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

Muse Ember D

Best 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.

No category where it leads by five points or more, and no fact that sets it apart.

Watch for

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

Score by category

CategoryWeight this runMeta Model APIMuse EmberEdge
Reliability16%204040even
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.26060even
Agent ergonomics13%16.27572Meta Model API +3
Security & auth14%17.56464even
Payments & pricing10%12.52020even
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.84646even
Transparency & trust7%8.85353even
Negative events≤1500
TotalD 52.3/100D 51.8/100

Facts side by side

FactMeta Model APIMuse Ember
KindModel APIModel API
VendorMetaMeta
Hosted endpointhttps://api.meta.ai/v1https://api.meta.ai/v1
TransportsHTTP
AuthAPI keyAPI key
PricingPay per usePay per use
x402nono
LicenceClosed 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 documentednono
llms.txtyesyes
Last release2026-10-122026-11-09
Terms last updated2026-10-022026-10-02
Privacy policy last updatedcouldn't be readcouldn't be read
Customer content may train modelsyesyes
Terms restrict automated accessnot found in the textnot found in the text
Terms restrict benchmarkingyesyes
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waiveryesyes

How the scores have moved

Meta Model API

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.

Other comparisons with Meta Model API or Muse Ember

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