🔍 Read the full analysis: Can Muse Ember’s Score Of 36 Offset Its Frequent “I Don’t Know” Answers? on ThorstenMeyerAI.com
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TL;DR
Meta released Muse Ember on November 9, 2026. Artificial Analysis reported a score of 36 for its max-effort version, matching Claude Haiku 5.5, while its average index-task cost was $0.14 versus $0.41. The benchmark also found low factual accuracy alongside frequent refusals and a lower hallucination rate; results may not predict performance on a particular company’s workload.
Meta released Muse Ember on November 9, and Artificial Analysis’s evaluation found its max-effort version scored 36 on the Intelligence Index, matching Claude Haiku 5.5 while costing an average $0.14 per index task, compared with $0.41 for Haiku. The same evaluation found Muse Ember answered just 21.1% of questions correctly on a separate factual-knowledge test, but declined 64% of them, a pattern associated with a lower reported hallucination rate.
Artificial Analysis describes the Intelligence Index as a set of nine evaluations. Its reported cost per task is the average API cost of completing one task in that index, not a guaranteed price for a customer’s own use. The figures put Muse Ember (max) at the same index score as Claude Haiku 5.5 (max), with the former’s reported task cost about one-third of the latter’s. Muse Ember’s xhigh setting scored 34 and cost $0.12 per index task.
The models’ listed token prices are not the whole explanation for that task-cost difference. Artificial Analysis attributes it partly to Muse Ember’s cached input price of $0.004 per million tokens, which it says is 60% below Haiku’s, and partly to Muse Ember using roughly a third as many output tokens as Haiku in the index. The report lists Muse Ember at $0.10 per million input tokens and $0.50 per million output tokens. These are reported rates and benchmark usage figures, not a forecast of an individual deployment’s bill.
The results vary by task. Muse Ember scored 68.1% on AutomationBench-AA, matching Meta’s more expensive Muse Wonder and exceeding Haiku 5.5’s 35.4% in the reported comparison. It also outscored Haiku on Terminal-Bench 4.0, but trailed it on FrontierCode 2.0 and the AA-Briefcase knowledge-work test. Artificial Analysis reported a 46 score for Muse Ember in Meta’s Muse Code on its Coding Agent Index; that comparison involves models running in different coding-agent products.
Can Muse Ember’s Score of 36 Offset Frequent “I Don’t Know” Answers?
Meta’s Muse Ember matched Claude Haiku 5.5 on the Intelligence Index at a lower reported benchmark cost. Its frequent refusals and low factual-test accuracy make real-workflow testing essential.
Comparable score, lower benchmark cost
The index covers nine evaluations; reported task costs describe benchmark use, not a customer’s guaranteed bill.
| Model and setting | Index score | Cost per index task |
|---|---|---|
| Muse Ember (max) | 36 | $0.14 |
| Claude Haiku 5.5 (max) | 36 | $0.41 |
| Muse Ember (xhigh) | 34 | $0.12 |
Answer behavior matters
On the factual-knowledge test, refusals were common alongside limited correct answers.
- 21.1% of questions answered correctly
- 64% of questions declined
- 17.4% reported hallucination rate
- Refusals may help avoid unsupported answers
- They may interrupt workflows that require a response
- Results apply to this benchmark, not every use
Performance varies by task
The headline index score does not establish a model-wide advantage.
Strong benchmark result
Muse Ember scored 68.1% on AutomationBench-AA, matching Muse Wonder and exceeding Haiku 5.5’s 35.4% in the reported comparison.
Mixed comparisons
It outscored Haiku on Terminal-Bench 4.0 but trailed on FrontierCode 2.0 and the AA-Briefcase knowledge-work test.
Benchmark costs are not bills
Prompts, context, retries, tools, and output needs can change what a company pays.
Test before choosing
Compare models on representative work and decide how the system should respond when Muse Ember declines.
Test representative prompts and relevant documents against alternatives.
Compare accuracy, refusal frequency, latency, and total cost.
Set a route for human review, added context, or another system.
Lower Costs, With Task-Specific Trade-Offs
The results put benchmark cost and answer behavior alongside raw capability as factors in choosing a model. For businesses running high-volume extraction, tagging, form filling, or agent steps, lower cost per benchmark task may merit testing. But the index does not establish that Muse Ember will cost one-third as much for any particular company’s workflow: prompts, context length, retries, tools, and output needs can change costs.
Its frequent refusals may also be useful or disruptive, depending on the job. In a system that can route uncertain requests to a person or another model, declining may reduce unsupported answers. In a workflow that expects a response every time, refusals can create extra work or interrupt automation. The relevant question is not simply whether the model says “I don’t know,” but whether its accuracy, refusal behavior, and cost fit the consequences of the specific task.
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How the Benchmark Compares Models
Muse Ember is the efficient tier of Meta’s Muse model line, available through Meta’s API and in its Muse Code coding agent. The source describes it as a proprietary model that accepts text and images and generates text, with a context window of up to one million tokens. Artificial Analysis tested two reasoning-effort settings, max and xhigh; the headline score of 36 refers to max.
The comparison is not a claim that Muse Ember is the strongest model overall. Artificial Analysis reported Muse Wonder at 44 for $6.95 per index task, while Claude Fable 5.2 at its highest effort scored 65 for $26.54 per task. Those figures illustrate a different price-and-score range, not a direct measure of how well each model would perform on every real-world use. The source’s benchmark data was accessed on November 9 and 10, 2026.
What the Scores Do Not Establish
The supplied material reports benchmark results but does not provide the full test prompts, sample sizes, uncertainty ranges, or independent replication details. It is not clear how closely the index’s task mix matches any particular company’s workload, or how results may change with different prompts, tools, or model updates. The reported 21.1% accuracy and 64% refusal rate apply to AA-Omniscience, not to every factual question or normal use of the model.
The figures also do not settle whether a refusal is preferable to a wrong answer in a given application. A lower benchmark hallucination rate does not establish that errors will be absent, and a model’s relative performance may differ across domains. Actual customer costs, availability, service terms, and performance at scale are not detailed in the supplied source. Those points should be checked against current provider documentation and tested directly.
Testing in Real Workflows
The next useful step for prospective users is to test Muse Ember on representative tasks, comparing it with alternatives on accuracy, refusal frequency, latency, and total cost rather than relying on an index score alone. For knowledge-heavy work, teams can supply the relevant documents or retrieved information and check whether the model handles that grounded material more reliably. They can also define what happens when it declines: request human review, retry with additional context, or route the task to another system.
Meta may publish further product or model details, and Artificial Analysis may update its evaluations as models and benchmark data change. The supplied report does not specify a future benchmark date or a scheduled Meta announcement. Until more information is available, the reported figures are a comparison point rather than a promise of performance in production.
Key Questions
What did Muse Ember score?
Artificial Analysis reported an Intelligence Index score of 36 for Muse Ember at max reasoning effort. Its xhigh setting scored 34. The index covers nine evaluations, according to the source.
How did its reported cost compare with Claude Haiku 5.5?
For one task in Artificial Analysis’s index, the source reported an average API cost of $0.14 for Muse Ember (max) and $0.41 for Claude Haiku 5.5 (max). Actual costs depend on the workload and usage, so these benchmark figures are not a universal price comparison.
Why does Muse Ember often answer “I don’t know”?
On the AA-Omniscience test, Artificial Analysis reported that Muse Ember declined 64% of questions and answered 21.1% correctly. The supplied material does not explain the model’s internal decision process, so it does not establish why it declines particular questions.
Does a lower hallucination rate mean Muse Ember does not make things up?
No. The reported 17.4% hallucination rate is a result from one benchmark, not proof that the model will avoid unsupported answers in all situations. Users should verify outputs when errors could have meaningful consequences.
Source: ThorstenMeyerAI.com
