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TL;DR
Artificial Analysis data dated November 9 and 10, 2026, puts Meta’s Muse Ember (max) at 36 on its Intelligence Index, matching Claude Haiku 5.5 while costing $0.14 per index task versus $0.41. The data also shows Ember answers fewer factual questions correctly but declines more often and has a lower measured hallucination rate. Results come from benchmarks, so performance and costs on a reader’s own tasks may differ.
Meta released Muse Ember on November 9, 2026, and Artificial Analysis data rates its max setting at 36 on the Intelligence Index—the same score as Claude Haiku 5.5—at an average cost of $0.14 per index task, compared with $0.41 for Haiku. The benchmark comparison also finds that Ember often declines difficult factual questions rather than guessing, a behavior that lowers its measured hallucination rate but leaves it with lower factual accuracy.
Artificial Analysis describes the Intelligence Index as a test covering nine evaluations. Its model pages list Muse Ember (max) at 36 and $0.14 per task; Muse Ember (xhigh) scores 34 at $0.12. These task costs are averages for running one task in the index, not a flat fee for every use. The figures should not be read as a guarantee of what a particular application will cost or how well it will perform.
Meta’s listed token prices are $0.10 per million input tokens and $0.50 per million output tokens. Artificial Analysis says Ember’s cached input price is $0.004 per million tokens, 60% below Haiku 5.5’s. The analyst attributes Ember’s lower index-task cost partly to cheaper cached input and partly to its use of roughly a third as many output tokens as Haiku to complete the index.
The benchmark results vary by task, as shown in Muse Wonder’s benchmark comparisons. Ember scored 68.1% on AutomationBench-AA, matching Meta’s more expensive Muse Wonder in the reported data, and beat Haiku on Terminal-Bench 4.0. It scored below Haiku on FrontierCode 2.0 and on the AA-Briefcase knowledge-work test. Artificial Analysis also reports an index score of 46 for Ember in Meta’s Muse Code tool, compared with 37 for Haiku in Claude Code and 31 for GPT-6 Luna in Codex. Those are results from separate benchmark setups, not a universal ranking of coding performance.
Muse Ember’s cost and its “I don’t know”
Artificial Analysis reports that Muse Ember (max) matches Claude Haiku 5.5’s Intelligence Index score at a lower average benchmark-task cost. It also declines more factual questions and has lower measured accuracy on that test.
How the comparison stacks up
The index covers nine evaluations; its average task costs are not fixed prices for every use.
| Model and setting | Index score | Cost per task |
|---|---|---|
| Muse Ember (max) | 36 | $0.14 |
| Claude Haiku 5.5 | 36 | $0.41 |
| Muse Ember (xhigh) | 34 | $0.12 |
Why it may answer “I don’t know”
On AA-Omniscience, the model can decline difficult factual questions rather than guess.
What the trade-off means
Lower benchmark cost can be useful, but frequent refusals may leave work unfinished.
- High-volume, repeated workflows
- Tasks where cautious answers help
- Teams able to test their own workloads
- Direct answers may be essential
- Refusals may require human or model fallback
- Actual costs depend on usage and caching
Before choosing a model
Benchmark results do not establish performance or costs for a specific deployment.
Include cases where supplied documents do and do not contain the answer.
Compare accuracy, refusals, latency, token use and fallback costs.
Where Lower Task Costs Could Matter
The price difference may matter most for organizations running high-volume, repeated workflows, such as sorting, extracting information, filling forms or taking routine steps in an agent process. If a workload resembles the benchmark and produces similar usage, the lower average task cost could make Ember worth testing against more expensive alternatives. Actual costs will depend on prompts, context length, caching, output volume and the task itself.
Its refusal pattern matters too. Artificial Analysis reports that Ember answers 21.1% of AA-Omniscience questions correctly, while declining 64%; its measured hallucination rate on that test is 17.4%. Haiku’s reported hallucination rate is 34.2%. This does not establish that Ember is more reliable across all settings: a model that frequently declines may be unsuitable when users need a direct answer, while a cautious response can help reduce unsupported claims in some workflows.
For businesses, the practical question is not whether a model is cheapest in a benchmark, but whether it can complete a specific job accurately at an acceptable cost. Teams considering it would need to test their own data, account for refusals, and decide when to send unanswered requests to a human or another system.
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How Ember Compares on the Index
Muse Ember is presented as the efficient tier of Meta’s Muse model line. The source says it is a proprietary model available through Meta’s API and Muse Code, accepts text and images, and generates text. Artificial Analysis tested it at max and xhigh reasoning effort. At max, its reported output speed was 440 tokens per second, and one index task took 3.2 minutes; at xhigh, those figures were 423 tokens per second and 2.8 minutes. The source also lists a one-million-token context window.
At the same max setting, Artificial Analysis lists Muse Wonder at 44 points and $6.95 per index task, while GPT-6 Luna scores 28 at $0.15. Claude Fable 5.2 scores 65 at its highest effort level and costs $26.54 per task in the reported comparison. These figures show a range of benchmark scores and costs, but they do not establish that models are interchangeable or that the index captures every factor relevant to choosing one.
The reported factual-question results help explain the article’s “I don’t know” framing. On AA-Omniscience, a test of difficult factual questions that lets models decline, Ember’s 21.1% accuracy is below Haiku 5.5’s 34.7% and Gemini 3.8 Flash (high)’s 50.5%. Ember’s 64% refusal rate accompanies a 17.4% hallucination rate; Haiku’s is 34.2%, while GPT-6 Luna’s is 79.3% in the same reported data. These rates describe that evaluation, not all responses in real-world use.
“The index is not your workload.”
— Thorsten Meyer, ThorstenMeyerAI.com
What the Benchmarks Cannot Show
The figures supplied are benchmark results and listed prices; they do not show how Muse Ember performs across every customer workload. The source does not provide independent, real-world cost comparisons for specific deployments, nor does it establish that the same task mix or caching behavior will apply to other users. It is also not clear from the material how the reported test results will change as models or benchmark versions are updated.
The source describes frequent refusals and lower measured hallucination as a trade-off, but does not report how often users would accept those refusals or how a fallback system would affect total cost. Its recommendation to route unanswered requests elsewhere is an operational suggestion, not a measured outcome. Readers should also distinguish a low hallucination rate on AA-Omniscience from a general guarantee that a model will avoid incorrect answers.
Test Workloads Before Choosing
The next practical step for teams evaluating Muse Ember is to test it on representative tasks, including cases where the supplied documents contain the answer and cases where they do not. A useful comparison would track accuracy, refusal frequency, latency, token use and the cost of any fallback model or human review—not just the headline benchmark score.
Artificial Analysis’s published model pages and future benchmark updates may provide further comparisons, while Meta’s API and Muse Code offer routes to try the model. The supplied source does not identify a further announcement date or a promised change to pricing. Until more deployment evidence is available, the confirmed comparison remains a benchmark result: Ember matched Haiku 5.5’s index score at a lower reported cost per task, alongside weaker factual accuracy and more frequent refusals.
Key Questions
How much does Muse Ember cost per benchmark task?
Artificial Analysis reports an average of $0.14 per index task for Muse Ember at max effort. Its xhigh setting is listed at $0.12. These are benchmark task costs, not a fixed charge for every request or a prediction of a particular customer’s bill.
How does Muse Ember’s score compare with Claude Haiku 5.5?
In Artificial Analysis’s reported comparison, both Muse Ember (max) and Claude Haiku 5.5 (max) score 36 on the Intelligence Index. Their reported costs per index task are $0.14 and $0.41, respectively.
Why does Muse Ember say “I don’t know”?
On the AA-Omniscience evaluation, the model can decline difficult factual questions. Artificial Analysis reports that Muse Ember declined 64% of questions in that test. The result describes benchmark behavior and does not mean it will refuse the same share of questions in every use.
Does the lower hallucination rate mean Muse Ember is more accurate?
Not by itself. In the reported AA-Omniscience results, Ember had a lower hallucination rate than Haiku 5.5, but its factual accuracy was also lower: 21.1% versus 34.7%. Accuracy, refusals and hallucinations measure different aspects of performance.
Is Muse Ember the best choice for coding?
The supplied results are mixed: Ember beat Haiku on Terminal-Bench 4.0 but scored below it on FrontierCode 2.0. Artificial Analysis also reported a higher Coding Agent Index score for Ember in Muse Code than for Haiku in Claude Code, but results from different tools and tests do not establish a universal winner. Teams should test their own coding tasks.
Source: ThorstenMeyerAI.com
