Head to head · LLM inference · October 2026 research run
Muse Ember vs OpenAI API
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.
Best model APIs and inference for AI agents · All 871 models comparisons
Which one, for what
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
Best 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 in its favour
- Agent-ready, a grade of BB or better
Watch for
Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Maintenance & community OpenAI API +41
Muse Ember 46
Model reading. Released 9 November 2026 (30). No deprecation policy or notice period found, per the platform reading (0 of 12). Dated launches on the API, the latest Muse Wonder on 12 October and Muse Ember on 9 November (8 of 8). No Model API changelog; a help centre with tickets (8 of 15). No SDK of Meta's own (0 of 15, 0 of 10). 30 + 0 + 8 + 8 + 0 = 46.
OpenAI API 87
Model reading. GPT-6 Rosalind, a life-sciences model for approved organisations, launched on 30 October, after GPT-6.1 Terra on 26 October and the Decisions API GA on 22 October (30). Published minimum notice of 6 months for GA models, 3 months for specialised variants, as little as 2 weeks for previews. On 14 October GPT-4o (gpt-4o-2024-08-06, gpt-4o-2024-11-20), GPT-4o mini, GPT-4.1 and GPT-4.1 mini were given a shutdown date of 14 January 2027, 92 days, with no safety or compliance reason stated. These are GA models, so the page's own 6-month minimum wasn't kept, as with gpt-5.4-cyber's 20 days in September. 8 of 12, the award for a minimum of two to three months in practice. Five shutdown dates in the 90 days to 1 November, the Assistants API on 26 August (announced in August 2025), gpt-5.2-chat-latest and gpt-5.3-chat-latest on 10 August (announced 8 May), gpt-3.5-turbo-instruct, babbage-002, davinci-002 and gpt-3.5-turbo-1106 on 28 September (announced 26 September 2025, missed in the 27 October count), gpt-5.4-cyber on 1 October and the legacy snapshots on 23 October (announced 22 April). Sora 2 and the Videos API (24 September) are scored on the openai-sora listing. Four of the five kept the published notice, so 4 of 8 as before, as amazon-bedrock has for four dates after full notice. Dated changelog several times a month and a help centre (15 of 15). openai-python had 215 open issues per the 1 October check, replies not sampled (5 of 10). Current official SDKs, openai 3.42.0 on PyPI (31 October) and npm openai 7.47.0 (15). Supported runtimes stated, Python 3.10 or later and Node 22 or later, the npm package's engines re-read on 1 November (10).
Schema & documentation OpenAI API +40
Muse Ember 60
Model reading. No OpenAPI file; the Markdown reference is the contract, per the meta-model-api listing (0). llms.txt and Markdown copies of every page (10). The Muse Ember page and choose-a-model page say what to hand it and what not to (hard single-shot coding, knowledge-heavy questions, visual coding), with model ID, inputs, context, max output and effort range (18 of 20). JSON Schema tools and output, reasoning effort an enum, per the platform reading (12 of 15). Curl, Python and TypeScript examples and the platform's error guide (15). A versioned model ID and a dated launch post, with no API changelog (5 of 15). 0 + 10 + 18 + 12 + 15 + 5 = 60.
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 gpt-6.1-terra (25). llms.txt index with per-product files, and every docs page has a Markdown twin at .md (10). The reference documents each parameter, and model pages say which model fits which job (20). Structured outputs with strict: true on schemas and function tools (15). Error codes guide with types and recovery advice, now with a 400 service_tier error for tiers a project doesn't allow (15). Dated model snapshots and a dated changelog, last entry 30 October (15).
Transparency & trust OpenAI API +37
Muse Ember 53
Editorial half. Closed model under the Model API terms, with clear terms and law; no weights and no parameter count, unlike Muse Spark Open and Muse Glimmer (15). Standard content isn't used for training, retention periods not stated outside zero retention (18 of 30). No deprecation policy or dated notices (0). A subprocessor list linked but not read, no data locations (8 of 20). 15 + 18 + 0 + 8 = 41.
OpenAI API 90
Closed service with clear terms, SDKs Apache-2.0 (15). The data-controls guide, re-read on 1 November, gives retention periods per endpoint, the training default and the ZDR route. The DPA, effective 1 January 2026, commits to return or deletion of customer data on termination, 30 days to object to a new subprocessor, and SCCs with the UK Addendum for transfers, and nothing in it conflicts with the guide, per the 4 October check, not re-read on 1 November (30). Deprecations page with announcement and shutdown dates, and GET /v1/models returns each model's shutdown_date (20). The subprocessor list, updated 9 July 2026, names its third-party subprocessors and OpenAI affiliates with the processing countries for each and a form to get notice of new ones, and data residency regions are listed, per the 4 October check, not re-read on 1 November (20).
Security & auth OpenAI API +36
Muse Ember 64
Platform readings are the meta-model-api listing's of 4 November. Team keys, named, revocable, no scopes (20 of 30). Standard mode only, so content isn't used to train Meta models under the terms, with safety use reserved (17 of 20). Retention 'as needed' with zero retention through sales (10 of 15). Usage dashboard by model, no per-call log (5 of 15). No security.txt on dev.meta.ai, a bug bounty and a security commitments page, no named certification (12 of 20). 20 + 17 + 10 + 5 + 12 = 64.
OpenAI API 100
Model reading. Project keys with All, Restricted (per-endpoint None, Read or Write) and Read Only permissions, service accounts, and mutual TLS with X.509 workload identity GA since 26 August, per the 1 October check. The changelog adds project key expiry dates on 10 September, key-creation controls on 15 September and, on 21 October, organisation and project settings that limit which service tiers a project may use (30). API data not used for training unless the customer opts in, since 1 March 2023 (20). Abuse-monitoring logs kept up to 30 days, and Zero Data Retention or Modified Abuse Monitoring by approval. The data-controls guide re-read on 1 November lists Chat Completions, Responses, Decisions and ten other endpoints as ZDR-eligible; /v1/agents, Conversations, Files and vector stores aren't (15). Usage and Costs filter by API key since 4 August, with Admin APIs to match, and audit logs for enterprise (15). SOC 2 Type 2, ISO 27001, 27017, 27018, 27701 and 42001 per the 1 October check, not re-read on 1 November, a Bugcrowd bug bounty and a coordinated disclosure policy, both linked from a signed security.txt. That security.txt had no Expires field on 27 October, which RFC 9116 requires, so the disclosure policy carries this line. It wasn't re-read on 1 November (20).
Reliability OpenAI API +35
Muse Ember 40
Model reading, for the surface it's called on, Meta Model API. No status page with readable history, per the meta-model-api listing's 4 November check (0), so no readable incident history (5). Rate limits published per team and per model, 3,000 RPM and 4,000,000 TPM for muse-ember-1.0, read 9 November (15). 429 and 503 with Retry-After, backoff guidance and rate-limit headers, per the meta-model-api listing (15). No SLA found (0). Muse Ember carries no preview label, but the API opened as a public preview in July and no general-availability notice was found, so half, as for the platform (5). 0 + 5 + 15 + 15 + 0 + 5 = 40.
OpenAI API 75
Status page at status.openai.com with component history (20). Two API-wide majors in the 90 days to 1 November, elevated errors across ChatGPT, Codex and the API (the Agents API included) on 29 September, with Chat Completions, Responses, Batch, Realtime, Embeddings, Images, Files and the other API components degraded from 17:52 to 23:14 UTC, and elevated error rates across API models on 17 September, with eleven components degraded from 20:20 to 21:20 UTC. Two majors give 5 of 30. The 25 July incident is now outside the window. The 29 September write-up, posted on 6 October, traces it to a feature rollout that exhausted shared network connection capacity. Every other incident since 3 August that lists an API component is partial or under an hour, so minor. Degraded performance across ChatGPT, Codex and the APIs on 21 October listed every API component but ran 22 minutes (02:26 to 02:48 UTC). Single-model incidents were GPT-5.6 Sol errors on 11 September (09:08 to 11:21 UTC), gpt-5.6-luna errors on 21 October (70 minutes) and GPT-6.1 Sol degraded performance on Batch, Chat Completions and Responses on 27 October (78 minutes). Partial incidents were latency for some Responses requests on the US endpoint on 31 August and 1 September, image generation where some requests failed on 8 September, latency for some Responses and Chat Completions requests on 30 September, large-PDF errors in Responses and an Agents API computer use incident on 6 October, Responses errors for users in India, Japan and Korea on 22 October and errors on some GPT Image 2 edit requests on 30 October (14:35 to 18:36 UTC). Image model incidents on 16 September and 14 October and file download errors on 19 October each ran about 40 minutes. FedRAMP errors for Astra and Sol on 21 October and authentication errors near Illinois on 20 October list no API component. Rate limits published per tier and model family with rate-limit headers (15). 429 guidance, re-read on 1 November, covers Retry-After as a minimum wait, backoff with jitter when it's missing, a gradual ramp, and splits slow_down (429) from server_is_overloaded (503) (15). Scale Tier carries a 99.9% uptime SLA, through sales, per the 1 October check, not re-read on 1 November (10). Responses and Chat Completions are GA (10). 20 + 5 + 15 + 15 + 10 + 10 = 75.
Agent ergonomics OpenAI API +26
Muse Ember 72
Model reading. Tool calls, parallel calls, computer use and search grounding, with tool_choice limited to auto per the platform reading (15 of 20). Strict structured output (15). Automatic prompt caching, cached input at 4% of the input price (15). A 1,048,576-token context, but requests over 200K input tokens bill at three times the rate for the whole request (12 of 15). No batch endpoint (0). No SDK of Meta's own; OpenAI and Anthropic SDKs against compatible endpoints (0). OpenAI-style errors with fixes and retryable codes (15). 15 + 15 + 15 + 12 + 0 + 0 + 15 = 72.
OpenAI API 98
Model reading of the checklist (tool use, structured output, caching, context, batch, SDKs, errors). Tool use with parallel calls and forced tool choice, 18 of 20 because GPT-6 Astra, GPT-6.1 Sol and GPT-6.1 Terra call tools only through the Responses API (18). Strict structured outputs (15). Automatic prompt caching with cached input at 0.1x on Astra and Luna ($0.01 per million on Luna) and 0.05x on GPT-6.1 Sol ($0.10) and GPT-6.1 Terra ($0.05), and a 12-hour cache lifetime in the Responses API since 20 October (15). 1.05M context on Astra, GPT-6.1 Sol, GPT-6.1 Terra and Luna (15). Batch at half price (10). Official SDKs in Python, JavaScript and more (10). Documented error codes with retry guidance (15).
Score by category
| Category | Weight this run | Muse Ember | OpenAI API | Edge |
|---|---|---|---|---|
| Reliability | 16%20 | 40 | 75 | OpenAI API +35 |
| Performance | 10%pending | pending | pending | not scored in this run |
| Schema & documentation | 13%16.2 | 60 | 100 | OpenAI API +40 |
| Agent ergonomics | 13%16.2 | 72 | 98 | OpenAI API +26 |
| Security & auth | 14%17.5 | 64 | 100 | OpenAI API +36 |
| Payments & pricing | 10%12.5 | 20 | 30 | OpenAI API +10 |
| Task success | 10%pending | pending | pending | not scored in this run |
| Maintenance & community | 7%8.8 | 46 | 87 | OpenAI API +41 |
| Transparency & trust | 7%8.8 | 53 | 90 | OpenAI API +37 |
| Negative events | ≤15 | 0 | 0 | |
| Total | D 51.8/100 | A 83.9/100 |
Facts side by side
| Fact | Muse Ember | OpenAI API |
|---|---|---|
| Kind | Model API | Model API |
| Vendor | Meta | OpenAI |
| Hosted endpoint | https://api.meta.ai/v1 | https://api.openai.com/v1 |
| Transports | HTTP | |
| Auth | API key | API key |
| Pricing | Pay per use | Pay per use |
| x402 | no | no |
| 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) |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| Last release | 2026-11-09 | 2026-10-30 |
| Terms last updated | 2026-10-02 | couldn't be read |
| Privacy policy last updated | couldn't be read | 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) |
How the scores have moved
Muse Ember
No score change since it was listed.
Verdicts
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.
OpenAI API
Official OpenAPI document and an llms.txt index. Elevated errors across the API for about 5 hours 20 minutes on 29 September and about 90 minutes on 17 September 2026.
Before you call either
Muse Ember
- Call https://api.meta.ai/v1/responses (or /chat/completions or /messages) with model muse-ember-1.0 and a bearer MODEL_API_KEY
- 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
- 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
- Use it for sub-agents and bounded repeated tasks, and a larger Muse model for the plan, as Meta advises
- Use strict structured output for extraction and routing steps
- In Claude Code, map the haiku alias and sub-agent model to muse-ember-1.0, per Meta's guide
OpenAI API
- Build on the Responses API. Astra calls tools only there
- Use
gpt-6-lunafor routing and extraction,gpt-6.1-terrafor screens, images and computer use at half Sol's price, andgpt-6.1-solfor most work. The models page still points unsure callers togpt-6-astra - Move off the GPT-4o and GPT-4.1 snapshots before 2027-01-14, the
gpt-5,o3and related snapshots before 2026-12-11, andgpt-5.1,gpt-5.3-codexandgpt-5.4-nanobefore 2027-04-01. Checkshutdown_dateonGET /v1/models - Treat 429
slow_downas a ramp limit and 503server_is_overloadedas a retry, and followRetry-Afterwhen it's sent - Prompts over 272K tokens cost double on input. Trim before you pay for it
Questions
Which is better for AI agents, Muse Ember or OpenAI API?
OpenAI API scores 83.9 (A) on agent readiness against Muse Ember's 51.8 (D), and leads in every scored category.
Do Muse Ember and OpenAI API need an API key?
Both need an API key.
Can an agent call Muse Ember and OpenAI API without installing anything?
Yes. Muse Ember has a hosted endpoint at https://api.meta.ai/v1 and OpenAI API at https://api.openai.com/v1.
Other comparisons with Muse Ember or OpenAI API
- Amazon Bedrock vs Muse Ember
- Amazon Bedrock vs OpenAI API
- Claude API vs Muse Ember
- Claude API vs OpenAI API
- Antseed vs Muse Ember
- Antseed vs OpenAI API
- Azure OpenAI in Foundry Models vs Muse Ember
- Azure OpenAI in Foundry Models vs OpenAI API
- BlockRun.AI vs Muse Ember
- BlockRun.AI vs OpenAI API
- Cerebras Inference vs Muse Ember
- Cerebras Inference vs OpenAI API
- Cloudflare AI Gateway vs Muse Ember
- Cloudflare AI Gateway vs OpenAI API
- Cloudflare Workers AI vs Muse Ember
- Cloudflare Workers AI vs OpenAI API
- Cohere Chat API (Command models) vs Muse Ember
- Cohere Chat API (Command models) vs OpenAI API
- DeepInfra vs Muse Ember
- DeepInfra vs OpenAI API
- DeepSeek API vs Muse Ember
- DeepSeek API vs OpenAI API
- Eden AI vs Muse Ember
- Eden AI vs OpenAI API
- Glimpse vs Muse Ember
- Glimpse vs OpenAI API
- Fireworks AI Inference vs Muse Ember
- Fireworks AI Inference vs OpenAI API
- Gemini Developer API vs Muse Ember
- Gemini Developer API vs OpenAI API
- GroqCloud vs Muse Ember
- GroqCloud vs OpenAI API
- Hugging Face Inference Providers vs Muse Ember
- Hugging Face Inference Providers vs OpenAI API
- Inkling 2 Small vs Muse Ember
- Inkling 2 Small vs OpenAI API
- Jais 3 vs Muse Ember
- Jais 3 vs OpenAI API
- Meta Model API vs Muse Ember
- Meta Model API vs OpenAI API
- MiniMax API vs Muse Ember
- MiniMax API vs OpenAI API
- Mistral AI API vs Muse Ember
- Mistral AI API vs OpenAI API
- Muse Ember vs Muse Spark Open
- Muse Ember vs Nemotron 3.5 Super VL
- Muse Ember vs Novita AI
- Muse Ember vs OpenRouter
- Muse Ember vs Prism Inference
- Muse Ember vs Router.com (Ramp Router)
- Muse Ember vs Beam 1.0
- Muse Ember vs RouterPlus
- Muse Ember vs SambaCloud
- Muse Ember vs SiliconFlow
- Muse Ember vs Simplismart
- Muse Ember vs StepFun API
- Muse Ember vs SWE-2 Flash
- Muse Ember vs Tencent Cloud TokenHub
- Muse Ember vs Together AI Inference
- Muse Ember vs Vercel AI Gateway
- Muse Ember vs Vertex AI (Gemini Enterprise Agent Platform)
- Muse Ember vs xAI API (Grok)
- Muse Spark Open vs OpenAI API
- Nemotron 3.5 Super VL vs OpenAI API
- Novita AI vs OpenAI API
- OpenAI API vs OpenRouter
- OpenAI API vs Prism Inference
- OpenAI API vs Router.com (Ramp Router)
- OpenAI API vs Beam 1.0
- OpenAI API vs RouterPlus
- OpenAI API vs SambaCloud
- OpenAI API vs SiliconFlow
- OpenAI API vs Simplismart
- OpenAI API vs StepFun API
- OpenAI API vs SWE-2 Flash
- OpenAI API vs Tencent Cloud TokenHub
- OpenAI API vs Together AI Inference
- OpenAI API vs Vercel AI Gateway
- OpenAI API vs Vertex AI (Gemini Enterprise Agent Platform)
- OpenAI API vs xAI API (Grok)
Machine-readable
- This page as Markdown
/compare/muse-ember-vs-openai-api.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/muse-ember.json·/api/v1/tools/openai-api.json - From a terminal
anchor compare muse-ember openai-api(the CLI) - Over MCP
compare_tools {"a": "muse-ember", "b": "openai-api"}at/mcp, no key