🔍 Read the full analysis: Breaking Down Muse Ember’s $0.14 Task Cost And Score Of 36 on ThorstenMeyerAI.com
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TL;DR
Artificial Analysis data dated November 9–10, 2026, gives Meta’s Muse Ember (max) an Intelligence Index score of 36 at an estimated $0.14 per task. That matches Claude Haiku 5.5’s score of 36 at $0.41 per task, but the benchmark also finds weaker factual accuracy for Muse Ember and a higher tendency to decline questions.
Meta released Muse Ember on November 9, and Artificial Analysis’s benchmark data gives its max-effort version an Intelligence Index score of 36 at an estimated $0.14 per task. That matches Claude Haiku 5.5’s score while costing less on the index, but the same tests show Muse Ember is less accurate on factual questions and more likely to decline an answer.
Artificial Analysis describes the Intelligence Index as based on nine evaluations. In its model data accessed November 10, Muse Ember (max) scores 36, the same as Claude Haiku 5.5 (max), for which the estimated cost per index task is $0.41. The cost figure is an average API cost for one task in that index, not a quoted price for every real-world request.
The comparison is not explained by lower listed token rates: the source gives Muse Ember input and output prices of $0.10 and $0.50 per million tokens, respectively, matching Haiku 5.5. Artificial Analysis attributes the lower task estimate to cheaper cached input and Muse Ember producing roughly a third as many output tokens as Haiku across the index. It reports cached input at $0.004, 60% below Haiku’s, though the source does not specify the unit alongside that cache price.
Results vary by test. Muse Ember scores 68.1% on AutomationBench-AA, level with Meta’s more expensive Muse Wonder in the cited data, and beats Haiku on Terminal-Bench 4.0. It trails Haiku on FrontierCode 2.0 and on the AA-Briefcase knowledge-work test. On AA-Omniscience, Muse Ember answers 21.1% correctly and declines 64% of questions; Artificial Analysis reports a 17.4% hallucination rate.
Muse Ember’s cost and score what the benchmark shows
Artificial Analysis reports that Muse Ember (max) matches Claude Haiku 5.5’s Intelligence Index score at a lower estimated task cost, with different results across individual tests.
Same score, different cost
Both max-effort models score 36, but the benchmark estimates a lower cost for Muse Ember on index tasks.
| Model | Index score | Cost per index task |
|---|---|---|
| Muse Ember (max) | 36 | $0.14 |
| Claude Haiku 5.5 (max) | 36 | $0.41 |
Factual answers and refusals
On AA-Omniscience, Muse Ember often declines questions and answers a smaller share correctly than Haiku 5.5.
Performance varies by task
A matching overall score does not mean the models perform alike on every workload.
- Scored 68.1% on AutomationBench-AA, level with Muse Wonder in the cited data.
- Beat Haiku 5.5 on Terminal-Bench 4.0.
- Trailed Haiku 5.5 on FrontierCode 2.0 and AA-Briefcase.
- Reported hallucination rate on AA-Omniscience was 17.4%.
What businesses should test
The index estimate is not a fixed charge or a forecast of a company’s bill.
Compare Muse Ember with alternatives using the same prompts and success criteria.
Measure task completion and how often a refusal interrupts the workflow.
Include latency, retries, and fallback model calls alongside API cost.
A Lower-Cost Option With Trade-Offs
The result matters to organizations weighing the cost of high-volume model use. If their own work resembles the index tasks, the reported $0.14 average could make Muse Ember worth testing for repeated steps such as extraction, tagging, form filling, or workflow automation. The benchmark estimate does not establish what an organization will pay: actual costs depend on prompts, context length, cache use, and how many model steps a workflow requires.
Its refusal pattern may also affect where it fits. A model that often says it lacks an answer may be preferable for some tasks where unsupported factual responses carry risk, but refusals can interrupt work that requires a direct answer. The reported figures are test results, not evidence that Muse Ember will be safer or more accurate across business settings. Teams would need to measure both task completion and refusal handling on their own data.
The score also illustrates why a single index number is an incomplete buying guide. Matching Haiku’s overall score does not mean the models perform alike across coding, documents, factual questions, and agent workflows. Buyers need to compare performance on the tasks they intend to run, alongside the full costs of prompting, retries, and fallback models.
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How the Benchmark Compares Models
Muse Ember is presented in the source as the efficient tier of Meta’s Muse model line. It is a proprietary model available through Meta’s API and Muse Code, accepts text and images, and produces text. Artificial Analysis tested it at two reasoning settings: max and xhigh. The max version scored 36 at $0.14 per index task; xhigh scored 34 at $0.12. The source does not explain why the higher effort setting has a lower score in this test.
Artificial Analysis also reports 440 output tokens per second and 3.2 minutes per index task for Muse Ember (max) on Meta’s API, plus a one-million-token context window. These are reported benchmark and service measurements, not guarantees for every user or workload. The source compares Muse Ember’s task estimate with models at similar scores or token prices; for example, it lists Muse Wonder at score 44 and $6.95 per task, and GPT-6 Luna at score 28 and $0.15 per task.
For factual knowledge, the source reports that Haiku 5.5 answered 34.7% of AA-Omniscience questions correctly, compared with Muse Ember’s 21.1%. It gives Haiku a 34.2% hallucination rate and GPT-6 Luna a 79.3% rate in the same data. These are Artificial Analysis benchmark findings; they should not be read as universal rates for all prompts or deployments.
Questions Beyond the Index Data
The figures available here are attributed to Artificial Analysis and dated November 9–10, 2026. The source does not provide the full evaluation methodology, individual task prompts, confidence intervals, or enough detail to independently reproduce the cost estimates. The reported $0.14 is an index-specific average, not a universal per-request charge or a guarantee of savings.
It is also unclear how the benchmark results will translate to production use, including how often refusals occur on business prompts, whether the reported knowledge gap persists when documents or retrieval results are supplied, and how performance changes with different settings. The source identifies the model as proprietary and available through Meta’s API and Muse Code, but does not give further detail here about access limits, service availability, or future pricing.
Testing Results on Real Workloads
The next practical step for prospective users is to test Muse Ember against their own tasks and compare it with alternatives using the same prompts and success criteria. That evaluation should track quality, refusal rates, latency, and total API cost, including repeated steps and fallback calls where a refusal or error requires another model.
Further Artificial Analysis updates, Meta documentation, and independent tests could clarify how the model performs across workloads and whether its pricing or benchmark results change. Until then, the strongest supported conclusion is limited: Muse Ember matched Haiku 5.5’s reported index score at a lower estimated cost on that index, while showing distinct weaknesses and strengths across its component tests.
Key Questions
What score did Muse Ember receive?
Artificial Analysis reports a score of 36 for Muse Ember (max) on its Intelligence Index, which covers nine evaluations.
What does the $0.14 task cost mean?
It is Artificial Analysis’s estimated average API cost for one task in its index. It is not a fixed charge for every prompt or a forecast of a particular company’s bill.
How does Muse Ember compare with Claude Haiku 5.5?
The cited data gives both max-effort models a score of 36, with estimated index-task costs of $0.14 for Muse Ember and $0.41 for Haiku 5.5. Their results differ across individual tests, including factual questions and coding.
Does Muse Ember answer factual questions reliably?
In Artificial Analysis’s AA-Omniscience test, it answered 21.1% correctly and declined 64% of questions. Those results describe that benchmark and do not establish performance on every factual task.
What should businesses evaluate before using it?
Businesses should test the model on representative tasks and measure output quality, refusals, speed, and total cost, including any follow-up or fallback model calls. The index results alone do not show how it will perform on a specific workload.
Source: ThorstenMeyerAI.com
