🔍 Read the full analysis: Is Meta’s Muse Ember A Bargain At $0.14 Per Task? The Score Is 36 on ThorstenMeyerAI.com
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TL;DR
Meta released Muse Ember on November 9, 2026. Artificial Analysis reports that its max-effort version scores 36 on the Intelligence Index at an average cost of $0.14 per index task, compared with $0.41 for Claude Haiku 5.5 at the same score. The benchmark also shows weaker factual accuracy but more frequent refusals and a lower hallucination rate; results may differ on real-world workloads.
Meta released Muse Ember on November 9, and Artificial Analysis’s evaluation gives its max-effort version a score of 36 on the firm’s Intelligence Index. The benchmark estimates an average cost of $0.14 per index task, compared with $0.41 for Claude Haiku 5.5 at the same score, though the test results also point to weaker factual knowledge and different performance across tasks.
Artificial Analysis describes Muse Ember as the efficient tier in Meta’s Muse model line. It is a proprietary model available through Meta’s API and Muse Code, the company’s coding agent. It accepts text and images as input and produces text. The benchmark lists a one-million-token context window, an output speed of 440 tokens per second, and an average of 3.2 minutes to complete an index task for the max-effort version.
The reported $0.14 figure is an average API cost for one task in Artificial Analysis’s index, not a quoted price for every user request. The model’s listed rates are $0.10 per million input tokens and $0.50 per million output tokens, while cached input is listed at $0.004. Artificial Analysis attributes the lower task cost relative to Haiku partly to cheaper cached input and to Muse Ember using roughly a third as many output tokens as Haiku in the index.
The benchmark comparison also varies by test. Muse Ember scored 68.1% on AutomationBench-AA, matching Meta’s more expensive Muse Wonder on that test and exceeding Haiku 5.5’s 35.4%. It scored higher than Haiku on Terminal-Bench 4.0, but lower on FrontierCode 2.0. In Meta’s Muse Code tool, it received a 46 on Artificial Analysis’s Coding Agent Index; those figures describe different model-and-tool setups and should not be treated as a direct comparison of models alone.
Muse Ember: lower benchmark cost with trade-offs
Artificial Analysis reports the same Intelligence Index score as Claude Haiku 5.5 at a lower average task cost. Results may differ on real-world workloads.
Cost advantage, benchmark only
The reported task cost reflects the index’s token use and pricing assumptions; individual request costs vary.
- 36 at max effort
- $0.14 per index task
- xhigh scores 34 at $0.12
- 36 at max effort
- $0.41 per index task
- Different results across tests
Performance depends on the task
Muse Ember matched Muse Wonder on AutomationBench-AA and exceeded Haiku 5.5 there, while results differed on other coding tests.
68.1% score
Matched Muse Wonder and exceeded Haiku 5.5’s 35.4% on this test.
Mixed comparisons
Higher than Haiku on Terminal-Bench 4.0, lower on FrontierCode 2.0.
46 index score
This measures a model-and-tool setup, not a direct model-only comparison.
Factual work needs review
The knowledge benchmark shows frequent declines alongside low correct-answer accuracy.
What developers should measure
Test representative tasks before estimating production savings or choosing a deployment path.
Compare answer quality with alternatives on your data and prompts.
Measure token use, caching, settings, and total API spend.
Decide when to use retrieval, human review, or a fallback model.
Lower Benchmark Costs, Different Trade-Offs
The comparison gives developers a reason to test Muse Ember for high-volume, repeatable tasks, where a lower cost per benchmark task could matter if similar savings hold on their own workloads. Examples might include tagging, extraction, and routine workflow steps, but the index does not establish how much a business would save in production.
Its behavior on factual questions also matters for deployment choices. Artificial Analysis reports that Muse Ember answered 21.1% of AA-Omniscience questions correctly and declined 64% of them. Its measured hallucination rate was 17.4%, below Haiku 5.5’s 34.2% in the same data. A system that declines uncertain questions may suit some applications, but it may also require a human review process or a fallback model. These benchmark results do not establish that it will be safer or more reliable in every setting.
Products Worth Considering
Apple MacBook Pro 16-inch (2021), M1 Pro, 10-core CPU, 16-core GPU, 16GB RAM, 512GB SSD, Silver
Apple MacBook Pro 16.2-inch with M5 Pro, 18-core CPU, 20-core GPU, 48GB Memory, 2TB SSD, Space Black
Apple MacBook Pro 14-inch (2021) – M1 Pro Chip, 10-Core CPU, 16-Core GPU, 16GB RAM, 1TB SSD, Space Gray
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How the Index Compares Models
Artificial Analysis’s Intelligence Index aggregates results from nine evaluations. The source compares Muse Ember at two reasoning-effort settings: max scores 36 at $0.14 per index task, while xhigh scores 34 at $0.12. The quoted comparison with Claude Haiku 5.5 is specifically between the max-effort versions, both of which score 36; the source lists Haiku’s task cost as $0.41.
Other models in the source illustrate how benchmark score and cost can diverge: GPT-6 Luna scores 28 at $0.15 per task, and Meta’s Muse Wonder scores 44 at $6.95. Artificial Analysis also reports a score of 65 and a $26.54 task cost for Claude Fable 5.2 at its highest effort setting. These are benchmark figures reported by Artificial Analysis, not a guarantee of equivalent performance or costs across different applications.
Limits of the Benchmark Results
The source figures do not establish how Muse Ember will perform on a particular company’s data, prompts, or workflow. Artificial Analysis’s index is not a substitute for testing on a user’s own tasks, and the reported per-task cost reflects the index’s token use and pricing assumptions. Actual costs can vary with input length, output length, caching, model settings, and API use.
The supplied material also does not provide independent verification of Meta’s stated product availability or detailed API terms. It reports benchmark comparisons, but does not specify confidence intervals or enough testing detail to establish whether small differences on individual evaluations are statistically meaningful. The results should be read as a snapshot from November 9–10, 2026, rather than a lasting ranking.
Test Workloads and API Costs
The immediate next step for developers considering Muse Ember is to compare it with alternatives on their own representative tasks, measuring answer quality, refusal rates, latency, and total API costs. Teams using it for factual work can also test how often it declines and decide whether to route those requests to a retrieval system, a human reviewer, or another model.
Further updates may clarify how the model performs as Meta’s API and Muse Code usage expands. The available source does not give a schedule for new benchmark results or product changes. For now, the confirmed development is the release and the reported evaluation; production outcomes remain to be measured by users.
Key Questions
What score did Muse Ember receive?
Artificial Analysis reports a score of 36 for Muse Ember at max reasoning effort on its Intelligence Index, which combines nine evaluations. The xhigh setting scored 34 in the source data.
Does $0.14 mean every Muse Ember request costs that amount?
No. $0.14 is the reported average API cost per task in Artificial Analysis’s index. The cost of an individual request depends on factors such as token use, caching, and settings.
How does Muse Ember compare with Claude Haiku 5.5?
In the cited max-effort comparison, both models scored 36, while Artificial Analysis reports task costs of $0.14 for Muse Ember and $0.41 for Haiku 5.5. Their results differ across individual evaluations, including factual knowledge and coding tests.
What is the main limitation reported for Muse Ember?
Artificial Analysis found low accuracy on its AA-Omniscience knowledge test: Muse Ember answered 21.1% of questions correctly and declined 64%. The benchmark also reports a 17.4% hallucination rate, but these figures do not predict performance on every use case.
Source: ThorstenMeyerAI.com
