🔍 Read the full analysis: Meta’s Muse Ember: Affordable AI With A Tendency To Say “I Don’t Know” on ThorstenMeyerAI.com
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TL;DR
Meta released Muse Ember on November 9, 2026, as a lower-cost model for its API and Muse Code. Artificial Analysis gave its max-effort version an Intelligence Index score of 36, matching Claude Haiku 5.5, while reporting an average cost of $0.14 per index task versus $0.41 for Haiku. The evaluation also found lower factual accuracy but more frequent refusals and a lower hallucination rate on its knowledge test.
Meta released Muse Ember on November 9, positioning it as an efficient model for use through Meta’s API and Muse Code. In testing by Artificial Analysis, Muse Ember at maximum reasoning effort scored 36 on the organization’s Intelligence Index, matching Claude Haiku 5.5 while costing an average of $0.14 per index task, compared with $0.41 for Haiku.
The price difference is for completing the benchmark’s tasks, not a difference in the models’ listed per-token rates. Artificial Analysis lists Muse Ember at $0.10 per million input tokens and $0.50 per million output tokens, the same rates it reports for Haiku 5.5. It says Ember’s cached input costs $0.004 per million tokens, 60% less than Haiku’s, and that Ember used roughly a third as many output tokens as Haiku across the index. Those factors help explain the lower benchmark-task cost, though actual expenses will vary by workload.
Artificial Analysis also reports that Muse Ember produced 440 output tokens per second and completed an index task in an average of 3.2 minutes through Meta’s API. The model accepts text and images and generates text, with a reported one-million-token context window. At a higher reasoning setting, labeled xhigh, it scored 34 on the index and cost $0.12 per task; that is a separate configuration and should not be confused with the max-effort score of 36.
Performance varied by task. Ember scored 68.1% on AutomationBench-AA, matching Meta’s more expensive Muse Wonder on that evaluation and exceeding Haiku 5.5’s reported 35.4%. It beat Haiku on Terminal-Bench 4.0, 40.9% to 32.8%, but scored below it on FrontierCode 2.0, 27.2% to 34.9%. On GDP.pdf, Ember scored 22.8% against Haiku’s 20.8%; on the AA-Briefcase knowledge-work test, it trailed Haiku, with Elo ratings of 1521 and 1574, respectively.
Meta’s Muse Ember Lower cost, cautious answers
Released November 9, 2026, Muse Ember is a lower-cost model for Meta’s API and Muse Code. Benchmark results show a competitive score with trade-offs in factual accuracy and refusals.
Where Ember stands out
Performance varies by task, so a single index score does not predict every workload.
Strong benchmark result
Scored 68.1% on AutomationBench-AA, matching Muse Wonder and exceeding Haiku 5.5’s 35.4%.
Mixed task results
Beat Haiku on Terminal-Bench 4.0, but trailed it on FrontierCode 2.0.
Accuracy trade-off
On AA-Omniscience, Ember had lower factual accuracy than Haiku 5.5 and declined to answer more often.
Why the benchmark task costs differ
The reported gap reflects task-level usage, not cheaper standard token rates.
Artificial Analysis reports $0.10 per million input tokens and $0.50 per million output tokens for both Ember and Haiku 5.5.
Ember’s cached input is reported at $0.004 per million tokens, 60% less than Haiku’s.
Ember used roughly a third as many output tokens as Haiku across the index.
A cautious fit for factual work
Refusals and hallucination results may matter as much as price when answers need to be trusted.
- Repeatable, high-volume tasks with information supplied in prompts or documents
- Workflows that can route uncertain answers to trusted sources, another model, or a person
- Tasks that depend on direct answers from the model’s stored knowledge
- Real deployments where refusal behavior and total task costs have not been tested
What buyers should verify
Artificial Analysis results are specific to its evaluations, not a forecast for every business workload.
- Compare answer quality and total cost per completed task on your own prompts and documents.
- Measure factual errors and refusals on representative examples.
- Test retrieval from trusted sources and a fallback path for unanswered or uncertain requests.
- Check how reasoning settings, caching, and task complexity affect costs.
Lower Costs, With Task-Specific Trade-offs
The results suggest Muse Ember could be worth evaluating for high-volume, repeatable tasks where each request is relatively simple and the model receives the relevant information in its prompt or documents. A lower measured cost per index task could matter for businesses running extraction, tagging, form filling, routing, or repeated agent steps at scale. But the benchmark is not a forecast of what any particular company will pay: token use, cached context, reasoning settings, and task complexity all affect the bill.
Its pattern on Artificial Analysis’s knowledge test also changes how buyers may weigh it. The test found low factual accuracy alongside frequent refusals, with a lower hallucination rate than several comparisons. That may suit systems designed to retrieve trusted information and route uncertain responses to another model or a person. It may be a poor fit when users need a direct answer from the model’s stored knowledge. The refusal behavior is a benchmark finding, not a guarantee that the model will abstain correctly in every real-world setting.
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How Ember Compares on the Index
Artificial Analysis describes its Intelligence Index as a measure across nine evaluations. Its November 9–10, 2026, model data put Muse Ember (max) and Claude Haiku 5.5 (max) at 36, but their reported average costs per index task differed: $0.14 for Ember and $0.41 for Haiku. The organization also reported that GPT-6 Luna scored 28 at $0.15 per task, while Meta’s larger Muse Wonder scored 44 at $6.95. These figures compare performance and cost within this specific index; they do not establish a universal ranking for all uses.
On the AA-Omniscience factual-question evaluation, Artificial Analysis reported 21.1% accuracy for Ember, compared with 34.7% for Haiku 5.5 and 50.5% for Gemini 3.8 Flash at high effort. Ember declined to answer 64% of the questions, and its reported hallucination rate was 17.4%; Haiku’s was 34.2%, while GPT-6 Luna’s was 79.3% in the same data. The figures describe results on this particular test and should not be treated as rates for every conversation or deployment.
Meta’s release places Ember in the efficient tier of its Muse model line. The source material describes it as proprietary and available through Meta’s API and the company’s Muse Code coding agent. Artificial Analysis gave Ember a Coding Agent Index score of 46 in Muse Code, compared with 37 for Haiku 5.5 in Claude Code and 31 for GPT-6 Luna in Codex. Because the systems and agent environments differ, those scores are not a direct test of the models in identical conditions.
Benchmark Results Are Not Workloads
The figures cited here come from Artificial Analysis evaluations, and the available material does not provide independent confirmation of Meta’s pricing, speed, or performance claims beyond that testing. Results can shift with model versions, prompts, tools, and evaluation conditions. It is also unclear how well the benchmark results predict performance on specific business datasets or whether the reported costs will hold for customers’ real traffic and caching patterns.
The source material gives no detailed breakdown of how refusals perform across different kinds of questions, how often Ember declines when it could answer, or how often it answers when it should abstain. A lower hallucination rate on AA-Omniscience does not establish that every refusal is appropriate or that the model is reliable for high-stakes factual work. The practical impact of the model’s refusal behavior, and the availability and terms of access for different users, are not fully described.
Test Ember Against Your Own Tasks
The immediate next step for prospective users is to test Muse Ember on their own prompts, documents, and agent workflows, comparing both answer quality and total cost per completed task with alternatives. Teams considering it for factual work can measure refusal rates and factual errors on representative examples, then set up retrieval from trusted sources and a fallback path for unanswered or uncertain requests.
Meta’s API and Muse Code are the access routes named in the source material. No further release date, rollout schedule, or additional product announcement is specified there. The model’s position will depend on whether its cost and benchmark strengths carry over to real deployments, and on how Meta’s model and pricing change over time.
Key Questions
What is Meta Muse Ember?
Muse Ember is a proprietary Meta model released November 9, 2026. The source material says it is available through Meta’s API and Muse Code, accepts text and images, and generates text.
How does Ember’s benchmark score compare with Claude Haiku 5.5?
Artificial Analysis gave both models a 36 Intelligence Index score at maximum reasoning effort. It reported average index-task costs of $0.14 for Ember and $0.41 for Haiku; those costs are benchmark averages, not guaranteed prices for other workloads.
Why did Artificial Analysis report a lower cost per task for Ember?
The organization points to cheaper cached input and lower output-token use across its index. It reports the same standard input and output token prices for Ember and Haiku 5.5, so the task-cost comparison does not mean Ember’s listed token rates are lower.
Does Muse Ember hallucinate less than competing models?
On Artificial Analysis’s AA-Omniscience test, Ember had a reported hallucination rate of 17.4%, lower than the listed rates for Haiku 5.5 and GPT-6 Luna. The test also found that Ember declined 64% of questions; these results do not establish error rates across all uses.
What kinds of tasks may suit Muse Ember?
The reported results point to possible use in high-volume workflows, including automation tasks, especially when the system supplies relevant source information. Its weaker factual-test score and frequent refusals mean buyers should test it on their own work and plan how to handle unanswered questions.
Source: ThorstenMeyerAI.com
