🔍 Read the full analysis: Meta’s Muse Ember Scores 36 For $0.14 A Task. The Catch: It Often Says “I Don’t Know” on ThorstenMeyerAI.com
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TL;DR
Meta released Muse Ember on November 9, 2026. Artificial Analysis scored its max-effort version 36, matching Claude Haiku 5.5, while estimating a lower average cost per index task: $0.14 versus $0.41. The same tests found low factual accuracy but a high rate of declining to answer, leaving its fit dependent on whether a task rewards caution or expects reliable recall.
Meta released Muse Ember on November 9, with the model scoring 36 on Artificial Analysis’s Intelligence Index, the same score as Claude Haiku 5.5. Artificial Analysis estimates an average cost of $0.14 per index task for Muse Ember at max effort, compared with $0.41 for Haiku, while also finding that Ember answers fewer questions on a factual knowledge test and often declines when uncertain.
Artificial Analysis says its Intelligence Index combines nine evaluations. On that index, Muse Ember (max) costs an average of $0.14 per task; its xhigh reasoning setting scores 34 at $0.12 per task. These are benchmark estimates, not a quoted price for every real-world request. Meta lists token prices of $0.10 per million input tokens and $0.50 per million output tokens. Artificial Analysis attributes Ember’s lower task cost relative to Haiku partly to a cached-input price it reports as 60% lower and to Ember using roughly a third as many output tokens to complete the index.
The model accepts text and images and returns text. Meta offers it through its API and Muse Code, the company’s coding agent. Artificial Analysis measured output speed at 440 tokens per second for max effort and reported a one-million-token context window. Performance varied by test: Ember scored 68.1% on AutomationBench-AA, level with Meta’s more expensive Muse Wonder, and beat Haiku on Terminal-Bench 4.0. It trailed Haiku on FrontierCode 2.0 and on the AA-Briefcase knowledge-work test.
The clearest limitation appeared on AA-Omniscience, a factual question test that allows models to abstain. Artificial Analysis reported 21.1% accuracy for Ember, with the model declining 64% of questions and a 17.4% hallucination rate. In the same comparison, Haiku answered 34.7% correctly and had a 34.2% hallucination rate. These results describe this test set; they do not establish how either model will perform on every factual task.
Meta’s Muse Ember: Lower Cost With a Catch
Artificial Analysis found Muse Ember matched Claude Haiku 5.5 on its Intelligence Index at a lower estimated task cost. But Ember often abstained on factual questions, limiting where that saving may translate into useful work.
The cost comparison
Both models scored 36, but the benchmark estimated a lower average task cost for Ember.
| Model | Index score | Estimated task cost |
|---|---|---|
| Muse Ember (max) | 36 | $0.14 |
| Claude Haiku 5.5 | 36 | $0.41 |
Caution versus recall
On a factual test that allows abstentions, Ember declined many questions; Haiku answered more correctly.
- 21.1% accuracy
- 64% of questions declined
- 17.4% hallucination rate
- 34.7% accuracy
- More questions answered
- 34.2% hallucination rate
Where it may fit
The trade-off depends on whether a workflow values low cost and cautious answers or complete factual recall.
Repeatable, supplied-data tasks
Teams could test extraction, classification or form filling grounded in provided documents.
Workflows requiring completeness
Frequent abstentions may require human review, search or another model, adding cost and delay.
What businesses should test
Benchmark estimates are not guaranteed bills or proof of performance on customer workloads.
Compare Ember with the current model using the same prompts and source materials.
Measure correct answers, abstentions, errors, latency and cost after fallback.
See whether Ember signals uncertainty reliably and whether the workflow can handle it.
Lower Costs, More Abstentions
For organizations running high-volume, repeatable tasks, the reported cost gap could make Ember worth testing for extraction, classification, form filling or agent steps that rely on supplied documents rather than broad stored knowledge. The price comparison is based on Artificial Analysis’s benchmark tasks, so actual costs will depend on prompts, context length, caching, output and usage patterns.
Its frequent refusals create a separate operational trade-off. A model that says it does not know may reduce unsupported answers, but it can also interrupt workflows that need a complete response. Businesses would need to decide whether to route abstentions to a person, a search system or another model, and account for the cost and delay of that fallback.
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How Ember Compares on Tests
Muse Ember is described in the source as the efficient tier of Meta’s Muse line. Artificial Analysis compared its max-effort configuration with models at similar scores or prices. Claude Haiku 5.5 also scored 36, at a reported $0.41 per index task. Meta’s Muse Wonder scored 44 at $6.95 per task, while Claude Fable 5.2 scored 65 at $26.54 per task in the cited data. Those figures illustrate differences within this benchmark, not a universal measure of value or capability.
Other results were mixed rather than uniformly favorable. Ember scored 22.8% on the GDP.pdf document test, compared with Haiku’s 20.8%, but its AA-Briefcase score was lower. On coding, it scored 40.9% on Terminal-Bench 4.0 against Haiku’s 32.8%, then 27.2% on FrontierCode 2.0 against Haiku’s 34.9%. Artificial Analysis also reported a score of 46 for Ember in Meta’s Muse Code on its Coding Agent Index, compared with 37 for Haiku in Claude Code and 31 for GPT-6 Luna in Codex. These results come from different evaluations and product settings.
Benchmark Results and Real Use
The reported figures come from Artificial Analysis’s tests, not a disclosed independent trial of customer workloads. The source does not provide enough detail here to determine how performance changes across industries, prompt designs, languages, or different mixes of cached and uncached input. It also does not establish whether the benchmark cost estimates will match users’ bills.
It remains unclear how often Ember’s refusals would disrupt practical workflows, or whether a retrieval system and fallback model would preserve the stated cost advantage. The supplied material also does not include a response from Meta addressing the benchmark results. Claims about lower hallucination rates should be read as specific to the cited test, not a guarantee that the model will avoid errors elsewhere.
Testing Ember in Workflows
The next useful measure is performance on customer-specific tasks, including the rate of correct answers, abstentions, errors, latency and total cost after any fallback is added. Teams considering Ember can compare it with their existing model using the same prompts and source materials, while checking whether the model reliably signals uncertainty.
Meta’s API and Muse Code provide routes for trying the model, according to the source. No further release or benchmark date is specified in the material. Until more real-world data is available, the index supports a case for evaluation in cost-sensitive workflows, not a general conclusion that Ember is the best choice.
Key Questions
What is Meta Muse Ember?
Muse Ember is a model in Meta’s Muse line, released November 9, 2026. The source says it accepts text and images, produces text, and is available through Meta’s API and Muse Code.
How does Muse Ember compare with Claude Haiku 5.5?
Artificial Analysis gave both max-effort models an Intelligence Index score of 36. It estimated $0.14 per index task for Ember and $0.41 for Haiku, while reporting stronger factual accuracy for Haiku on AA-Omniscience.
Why does Muse Ember often say it does not know?
On AA-Omniscience, Artificial Analysis reported that Ember declined 64% of questions. The test allows abstentions, and the source describes this as part of a pattern in which Ember guesses less when it lacks an answer.
Is the $0.14 cost guaranteed for every task?
No. The $0.14 figure is an average cost estimate for one task in Artificial Analysis’s Intelligence Index at max effort. Actual costs can vary with the request, input and output tokens, caching and usage.
What remains unknown about the model?
The supplied data does not show how Ember performs across real customer workloads or whether its cost advantage persists when refusals require human review or another model. The benchmark findings should not be treated as a guarantee of performance outside those tests.
Source: ThorstenMeyerAI.com
