🔍 Read the full analysis: A Look At Muse Ember’s 36 Score, Low Task Cost, And Uncertain Answers on ThorstenMeyerAI.com
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TL;DR
Meta released Muse Ember on November 9, 2026. Artificial Analysis gave its max-reasoning version a score of 36, the same as Claude Haiku 5.5, while estimating a lower cost per benchmark task: $0.14 versus $0.41. The evaluation also found low factual accuracy alongside frequent refusals and a lower hallucination rate than several compared models.
Meta released Muse Ember on November 9, and an Artificial Analysis evaluation gave its max-reasoning version an Intelligence Index score of 36, matching Claude Haiku 5.5. Artificial Analysis estimates that a task in its index costs $0.14 with Muse Ember, compared with $0.41 for Haiku 5.5, but the results also point to a trade-off: Ember answered only 21.1% of questions correctly on a factual-knowledge test and often declined to answer.
Muse Ember is the efficient tier in Meta’s Muse model line. It is a proprietary model available through Meta’s API and its Muse Code coding agent, accepts text and images, and produces text. Artificial Analysis tested two reasoning settings. At max effort, Ember scored 36 at an estimated $0.14 per index task; at xhigh effort, it scored 34 at $0.12. The site says the task-cost figure is the average API cost of completing one task in its index, not a price for every possible user request.
The reported token prices are $0.10 per million input tokens and $0.50 per million output tokens. Artificial Analysis says Ember’s cached input costs $0.004, 60% less than Haiku 5.5’s, and that Ember used roughly a third as many output tokens as Haiku 5.5 across the index. Its max setting generated output at 440 tokens per second and took 3.2 minutes per index task on Meta’s API, according to the evaluation. The model has a reported one-million-token context window.
Performance varied by task. Ember scored 68.1% on AutomationBench-AA, matching Meta’s more expensive Muse Wonder in the reported results and exceeding Haiku 5.5’s 35.4%. On coding evaluations it scored 40.9% on Terminal-Bench 4.0, ahead of Haiku’s 32.8%, but scored 27.2% on FrontierCode 2.0, below Haiku’s 34.9%. In the Artificial Analysis Coding Agent Index, Ember in Muse Code scored 46, compared with 37 for Haiku in Claude Code and 31 for GPT-6 Luna in Codex.
Muse Ember’s score and cost come with trade-offs
Meta released Muse Ember on November 9, 2026. Its benchmark score matched Claude Haiku 5.5 at a lower estimated task cost, while factual answers and refusals raise practical questions.
Cost and score in context
Artificial Analysis estimated average API cost for tasks in its index; costs may differ for real workloads.
| Model | Index score | Cost per task |
|---|---|---|
| Muse Ember | 36 | $0.14 |
| Claude Haiku 5.5 | 36 | $0.41 |
| Muse Wonder | 44 | $6.95 |
Results vary by task
The benchmark picture depends on what a team needs the model to do.
- AutomationBench-AA: 68.1%, matching Muse Wonder and exceeding Haiku 5.5 at 35.4%.
- Terminal-Bench 4.0: 40.9%, ahead of Haiku 5.5 at 32.8%.
- FrontierCode 2.0: 27.2%, below Haiku 5.5 at 34.9%.
- AA-Omniscience: 21.1% correct, with 64% of questions declined.
Plan for unanswered requests
Frequent refusals may limit unsupported answers, but they can leave users without a response.
Compare Ember with alternatives using the same prompts and success criteria.
Measure answer quality, API cost, refusals, fallback use, and human review needs.
Ground requests in relevant information and prepare a fallback when Ember declines.
Lower Costs, Uneven Task Results
The comparison makes Muse Ember relevant to organizations considering high-volume, repeatable AI tasks, where small differences in per-task cost can matter if performance is adequate for the actual workload. Artificial Analysis’s estimate suggests a lower cost than Haiku 5.5 on its index, but the result should not be treated as a guaranteed saving across different prompts, tools, or production systems. The source itself advises testing models on the tasks they will actually perform.
Ember’s results also suggest a possible fit for workflows where a system provides the model with the needed information, such as document extraction or responses grounded in retrieved records. Its weaker factual-knowledge score may be a limitation when users expect answers from the model’s stored knowledge. Frequent refusals may reduce unsupported answers, but they also mean a workflow needs a way to handle unanswered requests. Price, answer coverage, and reliability all matter; the benchmark score alone does not settle the choice.
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How the Benchmark Compares Models
The Intelligence Index is an Artificial Analysis measure based on nine evaluations, according to the supplied report. Its score is one summary of performance, while the component tests show where models differ. The reported scores and costs are snapshots from Artificial Analysis model pages accessed November 9 and 10, 2026; they are not independent measurements of every real-world deployment.
Among the comparisons cited, Claude Haiku 5.5 also scored 36 and cost $0.41 per index task at max effort. Meta’s Muse Wonder scored 44 at $6.95 per task. Artificial Analysis reported that Claude Fable 5.2 at its highest effort scored 65 and cost $26.54 per task. These figures illustrate the cost-performance spread in that evaluation, but different models may suit different workloads and settings.
Limits of the Reported Results
The source material reports benchmark results but does not provide enough information here to establish how well they predict performance across individual business workloads. The $0.14 figure is an estimated average for tasks in Artificial Analysis’s index; actual costs can depend on prompt length, caching, reasoning settings, and how an application uses the model. The reported scores also do not establish that the model will be more accurate or less costly in a particular deployment.
On AA-Omniscience, Ember answered 21.1% correctly and declined 64% of questions, with a reported hallucination rate of 17.4%. The source compares that rate with 34.2% for Haiku 5.5 and 79.3% for GPT-6 Luna. Those numbers describe the test as reported and do not show how often the models would give unsupported answers in other settings. The supplied material does not detail the test’s full methodology, uncertainty ranges, or results from independent replication.
Testing Ember in Real Workflows
The next practical step for prospective users is to test Muse Ember on representative tasks and compare it with alternatives using the same prompts, success criteria, and cost accounting. Teams should track not only answer quality and API expense, but also refusal rates, fallback frequency, and human review needs. The supplied report recommends providing Ember with relevant source material and having a fallback for cases where it declines to answer.
Meta’s API and Muse Code are the stated access routes. The source material gives no later release timetable, independent deployment results, or announced changes to the model. Further evidence about real-world performance and costs will depend on product use and subsequent testing.
Key Questions
What did Artificial Analysis report about Muse Ember?
Artificial Analysis rated Muse Ember at 36 on its Intelligence Index at max reasoning effort, the same score it reported for Claude Haiku 5.5. It estimated a cost of $0.14 per index task for Ember and $0.41 for Haiku 5.5.
Does the $0.14 figure represent the cost of every Muse Ember request?
No. The report defines it as the average API cost of one task in Artificial Analysis’s index. Costs for a different workload may vary with input and output length, caching, reasoning effort, and other factors.
How did Muse Ember perform on factual questions?
On the reported AA-Omniscience test, Ember answered 21.1% correctly and declined 64% of questions. Artificial Analysis reported a 17.4% hallucination rate for the test, lower than the comparison figures cited for Haiku 5.5 and GPT-6 Luna.
Is Muse Ember better than Claude Haiku 5.5?
The reported results do not support a single overall winner. The models tied at 36 on the Intelligence Index, while Ember had a lower estimated index-task cost. Ember led on some listed workflow and coding tests but trailed Haiku on FrontierCode 2.0 and the AA-Briefcase knowledge-work test.
Where can users access Muse Ember?
The source says Muse Ember is available through Meta’s API and the Muse Code coding agent. It does not provide further details about account access, regional availability, or service terms.
Source: ThorstenMeyerAI.com
