🔍 Read the full analysis: Meta’s Muse Ember: Is Its 36 Score Worth The Frequent Uncertainty? on ThorstenMeyerAI.com
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TL;DR
Meta released Muse Ember on November 9, 2026. Artificial Analysis gives its max-effort version an Intelligence Index score of 36, matching Claude Haiku 5.5, while estimating $0.14 per index task versus $0.41 for Haiku. The same testing finds weak factual accuracy but a lower hallucination rate, in part because Ember often declines to answer.
Meta released Muse Ember on November 9, and independent AI evaluator Artificial Analysis rates its max-effort version at 36 on the Intelligence Index, the same score as Claude Haiku 5.5. Artificial Analysis estimates Ember costs $0.14 per index task, compared with $0.41 for Haiku, while its tests also point to a trade-off: Ember performs poorly on difficult factual questions but often refuses to guess.
Artificial Analysis says the Intelligence Index combines nine evaluations. Its model pages list Muse Ember (max) at a score of 36 and $0.14 per task; the xhigh reasoning setting scores 34 at $0.12. The task-cost figure is an average API cost for completing one task in that index, not a guaranteed price for a customer’s workload. These are evaluator findings, not results independently verified here.
The evaluator reports input pricing of $0.10 per million tokens and output pricing of $0.50 per million, matching the listed rates for Haiku 5.5 and GPT-6 Luna. Cached input is listed at $0.004 per million tokens, which Artificial Analysis describes as 60% below Haiku’s rate. It attributes Ember’s lower index-task cost partly to that cheaper cache and to Ember using roughly a third as many output tokens as Haiku across the index.
Performance varies by test. Artificial Analysis reports Ember at 68.1% on AutomationBench-AA, level with Meta’s more expensive Muse Wonder in that evaluation, and ahead of Haiku’s 35.4%. Ember scores higher than Haiku on Terminal-Bench 4.0 but lower on FrontierCode 2.0. The evaluator also reports an advantage for Ember inside Meta’s Muse Code tool on its Coding Agent Index; those agent results involve different tools as well as different models, so they are not a direct model-only comparison.
Meta’s Muse Ember Cost vs. uncertainty
Artificial Analysis gives Ember and Claude Haiku 5.5 the same Intelligence Index score. Ember costs less per benchmark task, but often declines difficult factual questions.
The benchmark trade-off
The index combines nine evaluations; equal overall scores can hide different strengths.
- Lower estimated cost per index task
- Higher than Haiku on Terminal-Bench 4.0
- 68.1% on AutomationBench-AA, ahead of Haiku
- Weak performance on difficult factual questions
- Lower than Haiku on FrontierCode 2.0
- Frequent refusals can interrupt workflows
Read the factual results carefully
On AA-Omniscience, Artificial Analysis reports accuracy, refusals and hallucinations together.
| Model | Accuracy | Hallucination rate |
|---|---|---|
| Muse Ember | 21.1% | 17.4% |
| Claude Haiku 5.5 | 34.7% | 34.2% |
Where lower costs may help
High-volume, repeated calls could benefit if Ember completes the task reliably.
Routine processing
Document extraction, routing and tagging may suit lower-cost calls when quality meets the team’s needs.
Knowledge-heavy work
Trusted documents, retrieval or human review may help, but the supplied findings do not test those deployments.
Coding workflows
Results vary by test, and coding-agent comparisons include different tools as well as different models.
A practical evaluation
Compare Ember with alternatives on representative tasks before choosing a role for it.
Use examples that reflect the work your team needs done.
Measure accuracy, refusals, latency and cost per completed task.
Count human review and other models needed when Ember declines or falls short.
Where Lower Costs Could Help
The reported price difference could matter for organizations that run high volumes of repeated model calls, such as document extraction, routing, tagging and simple agent steps. At the evaluator’s stated index-task costs, Ember’s task is about one-third the price of Haiku’s. Actual savings will depend on prompt length, caching, output volume, model settings and the customer’s own application; benchmark costs do not establish savings in production.
Ember’s refusal pattern may also suit tasks where an unsupported answer carries a cost. Artificial Analysis reports that Ember declines 64% of AA-Omniscience questions, with 21.1% accuracy and a 17.4% hallucination rate. A cautious model can be useful when paired with supplied documents, retrieval or a human review path. But frequent refusals may interrupt workflows that require an answer, even when confidence is low, so teams would need to measure both error rates and completion rates.
The findings do not make Ember a general replacement for stronger models. A lower benchmark cost matters only if the model can complete the relevant task to an acceptable standard. Buyers comparing options will need to weigh price against factual reliability, coding results, refusal frequency and the cost of fallback systems.
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Meta’s Efficient Muse Tier
Muse Ember is the efficient tier of Meta’s Muse model line. It is proprietary, accepts text and images, and produces text, according to the supplied source. Meta offers it through its API and in Muse Code, its coding agent. The Artificial Analysis figures cited here were published or accessed on November 9 and 10, 2026; model benchmarks and prices can change, so the date matters when comparing them.
The evaluator’s comparison illustrates a wide range of price and score combinations. Muse Wonder is listed at 44 points and $6.95 per index task, while Claude Fable 5.2 at its highest effort is listed at 65 points and $26.54. These comparisons show how the cost of benchmark performance can vary, but they do not establish that the models are interchangeable or that a higher score is worth a given price to every user.
Artificial Analysis characterizes Ember’s factual-knowledge result as its main weakness. On the same AA-Omniscience test, it reports Haiku 5.5 at 34.7% accuracy and Gemini 3.8 Flash (high) at 50.5%. Differences in refusal behavior affect how such scores should be read: a model that declines many questions can have fewer wrong answers while also providing fewer answers overall.
Limits of the Benchmark Evidence
The reported results do not establish how Muse Ember will perform on any particular company’s data or workflow. The source provides Artificial Analysis scores and prices, but no independent replication, customer deployment figures or measured business savings. It is also unclear how frequently Meta may change the model, API rates or benchmark configurations.
The meaning of the factual test depends partly on what counts as a useful refusal. The cited figures give accuracy, refusal and hallucination rates, but do not explain how every unanswered question is treated in downstream applications. A low hallucination rate is not the same as high factual coverage, and the results do not show whether Ember’s refusals are appropriate in a specific setting.
Comparisons across coding-agent tools also require care: Muse Code, Claude Code and Codex are distinct environments. The supplied material reports scores for each pairing but does not isolate how much of the difference comes from the model, tool design or evaluation setup. The benchmark’s performance and price data should be treated as a dated snapshot rather than a guarantee.
Test Ember on Real Workloads
The next practical step for prospective users is to compare Ember with alternatives on their own representative tasks. That means tracking cost per completed task alongside accuracy, refusals, latency and the frequency of human or model fallback. For knowledge-heavy work, supplying trusted documents or retrieved information may address some limitations, but the source does not report results from such deployments.
Meta’s API availability and Muse Code provide routes for users to evaluate the model, according to the supplied source. No customer rollout timetable, independent production study or further Meta benchmark announcement is included in the material. Readers should check current pricing and model documentation before making purchasing decisions, since the cited figures are dated November 2026.
Key Questions
What did Meta release?
Meta released Muse Ember on November 9, 2026. The source describes it as a proprietary, efficient-tier model available through Meta’s API and Muse Code.
What does Muse Ember’s score of 36 mean?
Artificial Analysis assigns the max-effort version a score of 36 on its Intelligence Index, which combines nine evaluations. Claude Haiku 5.5 receives the same score in the cited comparison.
Is Muse Ember cheaper than Claude Haiku 5.5?
Artificial Analysis estimates $0.14 per index task for Ember and $0.41 for Haiku 5.5. Those are benchmark-task averages, not guaranteed costs for an individual company’s workload.
Why does the source say Muse Ember hallucinates less?
In Artificial Analysis’s AA-Omniscience results, Ember declines 64% of questions and has a reported hallucination rate of 17.4%. The same test reports 34.2% for Haiku 5.5. Ember’s low factual accuracy and high refusal rate mean the lower hallucination figure should not be read as broad factual expertise.
Is Muse Ember suitable for coding?
The results are mixed. Artificial Analysis reports Ember ahead of Haiku 5.5 on Terminal-Bench 4.0 but behind on FrontierCode 2.0. Its coding-agent score in Muse Code also compares different tool environments, so users should test their own coding tasks.
Source: ThorstenMeyerAI.com
