🔍 Read the full analysis: Meta Muse Ember’s Low Task Cost Comes With An Uncertainty Trade-Off on ThorstenMeyerAI.com
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TL;DR
Meta released Muse Ember on November 9, 2026. Artificial Analysis measured a score of 36 for its max-reasoning version at $0.14 per index task, matching Claude Haiku 5.5’s score at $0.41; its tests also found lower factual accuracy, frequent refusals and a lower hallucination rate. The results come from benchmark tests, not a guarantee of performance on a company’s own work.
Meta released Muse Ember on November 9, and Artificial Analysis’s tests show its max-reasoning version matched Claude Haiku 5.5 at 36 on the Intelligence Index while costing $0.14 per index task, compared with $0.41 for Haiku. The cost advantage comes with a qualification: Muse Ember answered fewer questions correctly on a factual-knowledge test, while declining more often and posting a lower measured hallucination rate.
Artificial Analysis says its Intelligence Index combines nine evaluations. Muse Ember (max) and Claude Haiku 5.5 (max) each scored 36, but the average API cost for one index task was $0.14 for Muse Ember and $0.41 for Haiku. These are benchmark costs, not a forecast of what any particular deployment will spend; actual costs depend on the prompts, model use and workload.
The service lists Muse Ember at $0.10 per million input tokens and $0.50 per million output tokens. Artificial Analysis attributes its lower overall index-task cost, despite token prices matching Haiku’s, to cheaper cached input and roughly one-third as many output tokens during the index tests. Its model page reports 440 output tokens per second and a 1-million-token context window. Those figures are test and product specifications, not a guarantee of speed for every user.
Results varied by task. Muse Ember scored 68.1% on AutomationBench-AA, matching Meta’s more expensive Muse Wonder in the reported results, 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 reported a Coding Agent Index score of 46 for Muse Ember in Meta’s Muse Code, compared with 37 for Haiku in Claude Code and 31 for GPT-6 Luna in Codex. These comparisons involve different products and test environments, so they do not establish that one model is better for all coding work.
Low Task Cost, Uncertain Fit
Meta’s Muse Ember matched Claude Haiku 5.5 on Artificial Analysis’s Intelligence Index for less benchmark cost. Lower factual accuracy and frequent refusals make real-world fit uncertain.
Where the trade-off shows
The benchmark combines nine evaluations, but performance varies by task.
Lower index-task spend
The reported average is lower than Haiku 5.5’s, but it is not a forecast for a company’s workload.
Fewer correct answers
Muse Ember scored 21.1% accuracy on AA-Omniscience and declined many questions.
Lower measured rate
Its 17.4% rate was below Haiku 5.5’s 34.2% on that test.
Results depend on the task
Coding evaluations were mixed, so no single result settles which model is best for a team.
- Matched Muse Wonder on AutomationBench-AA at 68.1%.
- Beat Haiku on Terminal-Bench 4.0.
- Higher Coding Agent Index score in Muse Code than Haiku in Claude Code.
- Scored below Haiku on FrontierCode 2.0.
- Tests used different products and environments.
- Benchmark results do not guarantee performance on company work.
Check before switching
Use representative tasks to see whether lower benchmark cost holds up when quality and fallback needs count.
Compare Muse Ember with the current model on representative company work.
Track accuracy, refusals, latency and total cost.
Check how reliably a human or another system can take over after a refusal.
Lower Costs for Repeated AI Tasks
The reported price difference may matter to teams that run large volumes of routine model calls, such as tagging, extraction, form completion or individual steps in an agent workflow. If a company’s own tasks resemble the benchmark and its quality requirements are met, a lower cost per task could reduce spending or make more frequent model use affordable. Artificial Analysis’s figure is an average for its index, however, and should not be treated as a direct estimate for those business uses.
The accuracy and refusal results also affect how the model may fit into a workflow. On a knowledge test, an answer that is withheld can be safer than a confident but incorrect response, but it can also interrupt automation or require a human or another model to take over. The practical value of lower cost with more refusals depends on whether a task supplies the necessary facts and has a reliable path for handling unanswered requests.
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How Muse Ember Compares
Muse Ember is described in the source material as the efficient tier of Meta’s Muse model line, available through Meta’s API and the Muse Code coding agent. It accepts text and images and produces text. Artificial Analysis tested two reasoning settings: max and xhigh. The max version scored 36 on the Intelligence Index at $0.14 per task; xhigh scored 34 at $0.12. The lower xhigh cost accompanies a lower index score, so the settings are not interchangeable.
Artificial Analysis’s comparisons put the model’s price and score in perspective. Muse Wonder scored 44 at $6.95 per index task in the reported data. Claude Fable 5.2 at its highest effort level scored 65 at $26.54 per task. Those figures show that higher scores in this set came at substantially higher benchmark cost; they do not prove the more expensive models are necessary or preferable for every application.
The factual-knowledge test illustrates a different dimension from the overall index. Artificial Analysis reported 21.1% accuracy for Muse Ember on AA-Omniscience and a 64% refusal rate. It reported a 17.4% hallucination rate for Ember, versus 34.2% for Haiku 5.5 and 79.3% for GPT-6 Luna. The measures describe behavior on that test and should not be read as universal rates across topics or real-world use.
Benchmark Results Have Limits
The published results do not show how Muse Ember performs across every customer workload, nor do they provide an independent, head-to-head measure of total cost for a specific business process. The $0.14 figure is an index-task average; the source does not establish that a company will see the same cost, speed or quality on its own prompts and data.
It is also unclear from the supplied material how the evaluations handle different kinds of refusals, whether performance varies substantially by subject, or how often a real workflow would need a fallback after an unanswered question. The figures are attributed to Artificial Analysis, and the available material does not include the full test protocols or enough detail to independently assess every comparison. More production experience and task-specific testing would be needed to judge reliability beyond these benchmarks.
Test Workloads Before Switching
Organizations considering Muse Ember can compare it with their current model using representative prompts, measuring accuracy, refusal frequency, latency and total cost rather than relying on the index score alone. For tasks based on supplied documents or retrieved records, teams can check whether the model uses that material correctly and route refusals to a human or another system. The source material recommends testing on a company’s own work; it does not provide a date for additional independent results or a further Meta release.
Further information about real-world performance, including how often users need a fallback and whether the benchmark cost advantage holds at scale, remains to be established. Until then, the reported data support a narrower conclusion: Muse Ember offers a lower cost on Artificial Analysis’s index at a score matching Haiku 5.5, while its factual-knowledge and refusal profile may make it a better fit for some tasks than others.
Key Questions
What did Meta announce?
Meta released Muse Ember on November 9, 2026. The source describes it as an efficient-tier model available through Meta’s API and Muse Code.
How does Muse Ember’s benchmark cost compare with Haiku 5.5?
Artificial Analysis reported an average cost of $0.14 per Intelligence Index task for Muse Ember (max), against $0.41 for Claude Haiku 5.5 (max). Both scored 36 on the index. The costs refer to that benchmark, not every real-world task.
Does Muse Ember hallucinate less?
Artificial Analysis measured a 17.4% hallucination rate for Muse Ember on AA-Omniscience, lower than the reported 34.2% for Haiku 5.5. The same test found Muse Ember answered 21.1% correctly and declined 64% of questions, so the result should be understood alongside its frequent refusals and lower factual accuracy.
Is Muse Ember better for coding?
The reported results are mixed. Muse Ember scored higher than Haiku on Terminal-Bench 4.0 and had a higher Coding Agent Index score in its respective tool, but it scored lower on FrontierCode 2.0. These tests do not establish that it is the better choice for every coding task.
What should companies test before adopting it?
Teams should evaluate Muse Ember on their own representative tasks, including answer quality, refusal handling, latency and total cost. Workflows should also specify what happens when the model declines to answer.
Source: ThorstenMeyerAI.com
