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Meta released Muse Ember on November 9, 2026. Artificial Analysis measured its max-effort model at 36 on its Intelligence Index and $0.14 per index task, compared with Claude Haiku 5.5’s score of 36 at $0.41. The benchmark also found lower factual accuracy but a higher rate of declining to answer and a lower hallucination rate.
Meta released Muse Ember on November 9, and benchmark firm Artificial Analysis reports that its max-effort version scores 36 on the Intelligence Index for an average of $0.14 per index task. Claude Haiku 5.5 scores the same, according to the firm, but costs $0.41 per task; the results put a lower-cost option in view for businesses running repeated AI workflows, while raising questions about the model’s factual knowledge.
Artificial Analysis says Muse Ember is Meta’s efficient-tier proprietary model, available through Meta’s API and the Muse Code coding agent. It accepts text and images and produces text. The firm evaluated it at two reasoning effort levels: max scored 36 on the Intelligence Index at $0.14 per task, while xhigh scored 34 at $0.12. The index combines nine evaluations, and the reported task cost is an average API cost for completing one index task.
The model’s listed prices are $0.10 per million input tokens and $0.50 per million output tokens, the same rates Artificial Analysis lists for Claude Haiku 5.5 and GPT-6 Luna. Cached input is priced at $0.004 per million tokens, which the firm says is 60% below Haiku’s rate. Artificial Analysis attributes Muse Ember’s lower benchmark task cost to cheaper cached input and to its use of roughly a third as many output tokens as Haiku 5.5 on the index.
Performance varied by evaluation. Muse Ember scored 68.1% on AutomationBench-AA, level with Meta’s more expensive Muse Wonder in the reported results. It beat Haiku 5.5 on Terminal-Bench 4.0, 40.9% to 32.8%, but scored lower on FrontierCode 2.0, 27.2% to 34.9%. In the Muse Code tool, it scored 46 on Artificial Analysis’ Coding Agent Index; the comparison scores were 37 for Haiku 5.5 in Claude Code and 31 for GPT-6 Luna in Codex.
Muse Ember prices AI uncertainty at $0.14 per task
Meta’s model matched Claude Haiku 5.5 on Artificial Analysis’ Intelligence Index at a lower average benchmark-task cost. Its factual accuracy was lower, while its abstention and hallucination results point to a different reliability trade-off.
The cost advantage—and its limits
Artificial Analysis’ task cost reflects its benchmark, not a guaranteed price for every company workload.
- Lower benchmark cost may matter for repeated, high-volume tasks.
- Cheaper cached input and fewer output tokens contributed to the result.
- Actual costs depend on a company’s tasks and token use.
- Human review and corrections can affect total operating cost.
Factual answers involve a trade-off
On the AA-Omniscience test, Muse Ember answered fewer questions correctly but declined more often and had a lower measured hallucination rate than the cited comparisons.
| AA-Omniscience result | Muse Ember | Haiku 5.5 |
|---|---|---|
| Accuracy | 21.1% | 34.7% |
| Declined questions | 64% | Not reported here |
| Hallucination rate | 17.4% | 34.2% |
Performance varies by task
Muse Ember beat Haiku 5.5 on one reported coding benchmark and scored lower on another.
Test it on your own workflows
Artificial Analysis advises measuring performance on a company’s own tasks; benchmark results alone do not establish real-world savings or suitability.
Include the workflows where a lower-cost model might be used.
Track accuracy, refusals, response time and token use.
Include the cost of checking and correcting outputs alongside API charges.
Lower Costs for Repeated AI Tasks
The reported price difference may matter most for companies that run high volumes of repeated tasks, such as routing, extraction, tagging, form filling and agent steps. On Artificial Analysis’ benchmark, Muse Ember’s task cost is about one-third of Haiku 5.5’s. That is a comparison of costs on this index; the firm’s results do not establish that companies will see the same savings on their own workloads.
The benchmark points to a trade-off between cost and what the model can reliably answer. Artificial Analysis reports that Muse Ember gets fewer questions right on its knowledge test than several compared models, but also declines more often and has a lower measured hallucination rate. For tasks where an unsupported factual answer could cause problems, a model’s willingness to abstain may be useful. For work that depends on broad factual recall, the lower accuracy could be a limitation.
Those results make task-specific evaluation important to interpreting the price. A low average cost does not show whether a model can complete a particular business process accurately, or how often people must review and correct its output. Artificial Analysis itself advises measuring performance on a company’s own tasks.
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How the Benchmark Compares Models
Artificial Analysis’ Intelligence Index combines results from nine evaluations. Its cost-per-task measure uses the API cost of completing a task in that index, so the figure reflects both token prices and how many tokens a model uses. It is not a flat charge for every request, nor a forecast of an organization’s total operating costs.
The comparisons reported November 9 and 10 put Muse Ember’s score of 36 alongside Haiku 5.5 at 36. Muse Wonder scored 44 at $6.95 per index task, while Claude Fable 5.2 at its highest effort level scored 65 at $26.54. These figures describe different benchmark scores and costs; they do not by themselves establish which model is best for a specific use.
Artificial Analysis also tested Muse Ember on AA-Omniscience, a factual question benchmark that allows models to decline. It reports 21.1% accuracy for Muse Ember, 34.7% for Haiku 5.5 and 50.5% for Gemini 3.8 Flash at high effort. Muse Ember declined 64% of questions, and its reported hallucination rate was 17.4%, compared with 34.2% for Haiku 5.5 and 79.3% for GPT-6 Luna. The figures reflect that test’s setup and results.
Open Questions on Real-World Use
The available figures come from Artificial Analysis’ evaluations, not from a broad report of customer outcomes. It is not clear how Muse Ember will perform across different business workloads, how often users will need to check its answers, or whether the benchmark cost difference will translate into comparable savings in production.
The source material does not provide customer adoption figures, independent assessments beyond the cited benchmarks, or details on how Meta’s API pricing may vary by usage. It also does not establish why the model declines so often on AA-Omniscience, or whether the same pattern holds for questions and workflows outside that test. The benchmark results are a snapshot dated November 9 and 10, 2026.
Testing Against Business Workloads
For teams considering Muse Ember, the next step is to compare it with existing models on their own representative tasks. That evaluation can track accuracy, refusals, response time, token use and the cost of human review alongside API charges. The source material does not identify a later benchmark release or a specific product milestone.
Artificial Analysis may add results as it evaluates more models, and Meta’s API and Muse Code remain the stated ways to access Muse Ember. Further customer data or updated benchmark measurements could clarify how often the lower task cost holds outside the index and where the model’s knowledge limits matter most.
Key Questions
What did Meta release?
Meta released Muse Ember on November 9, 2026. The proprietary model is available through Meta’s API and in Muse Code, according to the source material.
How does Muse Ember’s benchmark cost compare with Haiku 5.5?
Artificial Analysis reports an average cost of $0.14 per index task for Muse Ember at max effort and $0.41 for Haiku 5.5 at max effort. Both scored 36 on the firm’s Intelligence Index.
Does Muse Ember answer factual questions accurately?
On Artificial Analysis’ AA-Omniscience test, Muse Ember answered 21.1% correctly and declined 64% of questions. The firm reported a 17.4% hallucination rate on that test; results may differ on other tasks.
Is Muse Ember better at coding than Haiku 5.5?
The reported results are mixed. Muse Ember scored higher on Terminal-Bench 4.0 but lower on FrontierCode 2.0. In Muse Code, it scored 46 on Artificial Analysis’ Coding Agent Index, compared with 37 for Haiku 5.5 in Claude Code.
Source: Thorsten Meyer AI
