Head to head · LLM inference · October 2026 research run

Eden AI vs Muse Ember

Eden AI scores 59.9 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Muse Ember leads on reliability. Both do llm inference.

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

Which one, for what

Eden AI C

Best for Teams that want one key and bill for many LLMs plus OCR, speech, translation and web tools, with EU-only processing.

Ahead on

  • Schema & documentation, 81 against 60
  • Security & auth, 82 against 64
  • Payments & pricing, 30 against 20
  • Maintenance & community, 58 against 46

Watch for

No public status page, incident history or SLA 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.

Ahead on

  • Reliability, 40 against 33

Watch for

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

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.

Schema & documentation Eden AI +21

Eden AI 81

Router, scored on the API lines. OpenAPI 3.1 document for v3 (version 3.0.0, 46 paths) at api.edenai.run/v3/docs/openapi.json, plus a management API spec (25). llms.txt on the site and in the docs, llms-full.txt and Markdown pages (10). llms.txt states when to use Eden AI and when not to, and the docs separate chat models from expert models (17). Requests follow the OpenAI shape, and expert-model tools on the MCP server carry JSON Schema inputs (12). Examples throughout, but the spec documents only 200, 201, 202, 302 and 422, while llms.txt names 401 and 451 for region refusals (10). Versioned /v3 path, but no public changelog was found (7). 25+10+17+12+10+7 = 81.

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.

Security & auth Eden AI +18

Eden AI 82

Router, read on the model lines. Bearer keys with per-key budgets, expiry and attribution, a key management API, and deny-by-default guardrails that limit models per member, role or key (25). The data retention page says request and response content isn't stored and data isn't used to train models by default (18). Content logging is opt-in, async results are deleted after 7 days, and an organisation can enforce Zero Data Retention and region rules; the EU endpoint refuses non-EU models with 451 (13). Cost and provider per response, and a monitoring dashboard by key (12). The security page says Eden AI is SOC 2 and ISO 27001 certified and links a trust centre; /.well-known/security.txt returns 404 and no disclosure policy was found (14). 25+18+13+12+14 = 82.

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.

Maintenance & community Eden AI +12

Eden AI 58

Closed service, read on the model lines. The docs repository changed on 5 and 6 November 2026, including retiring Nova Reel and Veo from video generation, and the terms were updated 22 October (30). No public changelog, so dated API changes in the last 90 days come only from docs commits (12 of 20). Chat support within 48 working hours, no public changelog (8). No SDK of its own, and the MCP server isn't in the official MCP registry (5). Nothing to judge package health by beyond the docs repository (3). 30+12+8+5+3 = 58.

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.

Payments & pricing Eden AI +10

Eden AI 30

No x402, MPP or L402 (0). Provider prices with no markup plus a 5.5% platform fee, published per model in the keyless catalogue (20). llms.txt says some models are billed $0 for tests, but whether a new account can call them without buying credits isn't stated (10). A person signs up in a browser and buys credits through Stripe (0). 0+20+10+0 = 30.

Muse Ember 20

Published rubric. No payment protocol (0). Per-token prices for both tiers published without a login, read 9 November (20). No free API allowance found; billing needs a card, per the platform reading (0). Browser sign-up (0). 0 + 20 + 0 + 0 = 20.

Reliability Muse Ember +7

Eden AI 33

Router, read on the hosted lines. No status page is linked from the site or docs, and status.edenai.co didn't resolve for us on 6 November 2026 (0). No incident history to read (5). The rate-limits page gives 10 requests a second per account, shared across keys, while the plans page gives 7 a second, raisable to 15, so the figure is documented but inconsistent (10). Fallback retries the next model or provider on a 429 or error inside one request, but no client-facing 429 or Retry-After guidance was found (8). No SLA published; 'Dedicated support & SLA' is listed only for the custom Advanced plan, and the pricing page's '99.99%' uptime is a claim without a status page behind it (0). The v3 API is GA (10). 0+5+10+8+0+10 = 33.

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.

Score by category

CategoryWeight this runEden AIMuse EmberEdge
Reliability16%203340Muse Ember +7
Performance10%pendingpendingpendingnot scored in this run
Schema & documentation13%16.28160Eden AI +21
Agent ergonomics13%16.27572Eden AI +3
Security & auth14%17.58264Eden AI +18
Payments & pricing10%12.53020Eden AI +10
Task success10%pendingpendingpendingnot scored in this run
Maintenance & community7%8.85846Eden AI +12
Transparency & trust7%8.85553Eden AI +2
Negative events≤1500
TotalC 59.9/100D 51.8/100

Facts side by side

FactEden AIMuse Ember
KindModel routerModel API
VendorEden AI SASMeta
Hosted endpointhttps://mcp.edenai.run/mcphttps://api.meta.ai/v1
TransportsHTTP, Streamable HTTP
AuthAPI keyAPI key
PricingPay per usePay per use
x402nono
LicencenoneClosed 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 release2026-11-052026-11-09
Terms last updated2026-10-222026-10-02
Privacy policy last updated2026-10-22couldn't be read
Customer content may train modelsnot found in the textyes
Terms restrict automated accessnot found in the textnot found in the text
Terms restrict benchmarkingnot found in the textyes
Terms or service can change without noticenot found in the textnot found in the text
Arbitration or class-action waivernot found in the textyes

Verdicts

Eden AI

Eden AI gives one OpenAI-compatible key to about 1,000 models and expert models such as OCR, speech and translation, with an EU-only endpoint, per-key guardrails and no stored content by default. No status page, SLA or changelog was found, and its own pages disagree on the default rate limit.

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

Eden AI

  1. Read GET https://api.edenai.run/v3/models at run time for model ids, prices and regions instead of hard-coding them
  2. Use https://api.eu.edenai.run/v3 when data must stay in the EU, and pick another model on a 451 region_not_allowed
  3. Set max_tokens to 500 or more for reasoning models, or the answer can come back empty
  4. Pass a fallbacks list so a provider 429 or error retries inside one request
  5. Poll check_job for MCP tools marked long-running instead of calling them again

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, Eden AI or Muse Ember?

Eden AI scores 59.9 (C) on agent readiness against Muse Ember's 51.8 (D), and leads in 6 of 7 scored categories. Muse Ember leads on reliability.

Do Eden AI and Muse Ember need an API key?

Both need an API key.

Can an agent call Eden AI and Muse Ember without installing anything?

Yes. Eden AI has a hosted endpoint at https://mcp.edenai.run/mcp and Muse Ember at https://api.meta.ai/v1.

Other comparisons with Eden AI or Muse Ember

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