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Artificial Analysis reports that Meta’s Muse Ember (max) scored 36 on its Intelligence Index, matching Claude Haiku 5.5 (max), at $0.14 per index task versus $0.41. The same evaluation found lower factual accuracy but more refusals and a lower hallucination rate for Muse Ember; results may not predict performance on a company’s own tasks.
Meta released Muse Ember on November 9, and testing by Artificial Analysis found its max-effort version scored 36 on the Intelligence Index, matching Claude Haiku 5.5 while costing $0.14 per index task compared with $0.41. The benchmark also found that Muse Ember answered fewer difficult factual questions correctly but declined more often, with a lower measured hallucination rate.
Artificial Analysis describes Muse Ember as the efficient tier of Meta’s Muse model line. The proprietary model accepts text and images and produces text. Meta offers it through its API and Muse Code, its coding agent. The benchmark lists two reasoning settings: max scored 36 and xhigh scored 34. The xhigh setting cost $0.12 per index task and completed the index in 2.8 minutes, compared with $0.14 and 3.2 minutes for max.
The task-cost comparison is distinct from per-token pricing. Artificial Analysis lists Muse Ember’s input at $0.10 per million tokens and output at $0.50 per million, the same rates cited for Haiku 5.5. It attributes the lower benchmark-task cost to cheaper cached input and fewer output tokens: cached input costs $0.004, and Muse Ember used roughly one-third as many output tokens as Haiku 5.5 across the index, according to the source. These figures reflect the benchmark’s workload and pricing assumptions.
Results varied by task. Muse Ember scored 68.1% on AutomationBench-AA, matching Meta’s more expensive Muse Wonder in that test and exceeding Haiku 5.5’s 35.4%. It scored 22.8% on GDP.pdf, slightly above Haiku’s 20.8%, but trailed Haiku on the AA-Briefcase knowledge-work test. On coding evaluations, it scored higher on Terminal-Bench 4.0 and lower on FrontierCode 2.0. In the Coding Agent Index, Artificial Analysis reported a score of 46 for Muse Code running Ember, compared with 37 for Haiku in Claude Code and 31 for GPT-6 Luna in Codex.
Does Muse Ember’s $0.14 Price Offset Its Frequent Uncertainty?
Artificial Analysis found Muse Ember matched Claude Haiku 5.5 on its Intelligence Index at a lower benchmark-task cost. Its factual accuracy was lower, so the results need checking against real work.
Lower cost, with a caveat
The index-task comparison reflects its benchmark workload and pricing assumptions, not guaranteed savings for every business.
- High-volume routine calls such as tagging and extraction
- Tasks using known documents or structured inputs
- Open-ended factual questions
- Work that needs reliable answers without supplied sources
Performance varies by task
A tied overall score does not mean the models behave alike across evaluations.
AutomationBench-AA
Ember scored 68.1%, above Haiku 5.5’s 35.4% in this test.
Coding evaluations
Ember scored higher on Terminal-Bench 4.0 and lower on FrontierCode 2.0.
Factual answers
AA-Omniscience accuracy was lower than Haiku’s, alongside more refusals and a lower measured hallucination rate.
Check your own workload
Benchmark results are an initial comparison, not a forecast for a specific deployment.
Use real prompts and inputs from the work you plan to automate.
Measure accuracy, refusals, review effort, and latency against your current model.
Include retries, output, context, and caching in your comparison.
Lower Costs for Repeated Tasks
The price difference could matter to organizations that run high volumes of routine model calls, such as routing, extraction, tagging, form filling, or repeated steps in an agent workflow. On Artificial Analysis’s index, the reported $0.14 per task is about one-third of Haiku 5.5’s $0.41. The source says cheaper cached input and lower output-token use help account for the gap, even though listed per-token rates match.
That comparison does not establish that every business would save the same amount. Artificial Analysis’s index is a particular set of evaluations, and costs depend on prompts, context length, output, caching, and the model’s performance on the job. A cheaper model can also require more review or retries if it performs poorly on a particular task. The benchmark findings make Muse Ember a candidate for evaluation on routine workloads, while the model’s factual-answering weakness may limit its suitability where a reliable answer is required without supplied source material.
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A Benchmark Match With Trade-Offs
The Intelligence Index combines nine evaluations, according to the source, and its overall score summarizes performance across that set. A tied score does not mean two models behave alike on every task. Artificial Analysis’s subtest results show substantial variation: Muse Ember did well on AutomationBench-AA and Terminal-Bench 4.0, but lagged Haiku on FrontierCode 2.0 and AA-Briefcase.
The cost comparison also changes at the high end. The source reports Muse Wonder at score 44 and $6.95 per index task. It lists Claude Fable 5.2 at its highest effort level at score 65 and $26.54 per task. Those are benchmark comparisons from Artificial Analysis’s model data accessed November 10, 2026; they do not establish a general price for all user workloads or a universal quality ranking.
Factual Reliability Needs Testing
Artificial Analysis’s AA-Omniscience evaluation found 21.1% accuracy for Muse Ember, compared with 34.7% for Haiku 5.5 and 50.5% for Gemini 3.8 Flash (high). It also recorded a 64% refusal rate and a 17.4% hallucination rate for Ember; Haiku’s measured hallucination rate was 34.2%. These are results on that test, not guarantees about how often either model will answer correctly or abstain in other settings.
The source does not provide enough detail here to determine how representative the nine index evaluations are of a particular company’s work, or how task costs would change with different prompts and usage patterns. It also does not establish whether the same quality and cost relationships hold across all supported applications. The supplied material ends during its business recommendations, so it provides no further implementation guidance or independent user results.
Test Against Real Workloads
Organizations considering Muse Ember can compare it with their current model using representative tasks, measuring accuracy, refusal rate, review effort, latency, and total cost. Work that depends on known documents or structured inputs may be a better fit than open-ended factual questions, but that needs to be checked against the organization’s own data and requirements.
Artificial Analysis’s published benchmark results provide an initial comparison, not a forecast for a specific deployment. Further clarity will depend on performance in real workloads and on how Meta’s API pricing and model behavior apply to each use case.
Key Questions
What did Muse Ember score on the Intelligence Index?
Artificial Analysis reported a score of 36 for Muse Ember at max reasoning effort. Its xhigh setting scored 34.
How does its benchmark-task cost compare with Haiku 5.5?
Artificial Analysis listed $0.14 per index task for Muse Ember (max) and $0.41 for Claude Haiku 5.5 (max). This is an average for the index workload, not a guaranteed cost for other tasks.
Does Muse Ember hallucinate less than Haiku 5.5?
In Artificial Analysis’s AA-Omniscience test, Muse Ember’s measured hallucination rate was 17.4%, while Haiku 5.5’s was 34.2%. The result applies to that evaluation and does not guarantee performance elsewhere.
What is Muse Ember’s main reported weakness?
Its factual accuracy on AA-Omniscience was 21.1%, below the compared Haiku 5.5 and Gemini 3.8 Flash results. Muse Ember also declined 64% of questions in that test.
Where can users access Muse Ember?
The source says Meta offers the proprietary model through its API and Muse Code. It accepts text and images and generates text.
Source: Thorsten Meyer AI
