🔍 Read the full analysis: Meta Muse Ember’s AI Trade-Off: Affordable Tasks, Frequent “I Don’t Know” on ThorstenMeyerAI.com
Get business pricing on monitors, keyboards and dev gear
- Business-only prices and quantity discounts
- Tax-exempt purchasing
- Multiple users, one account, clear invoices
TL;DR
Meta released Muse Ember on November 9, 2026. Artificial Analysis gave its max-effort version a score of 36, matching Claude Haiku 5.5, while estimating a lower average cost per index task: $0.14 versus $0.41. The tests also found weak factual accuracy alongside frequent refusals to answer, a combination that may suit some supplied-information workflows but needs evaluation on real tasks.
Meta released Muse Ember on November 9, with Artificial Analysis reporting that its max-effort version scored 36 on the organization’s Intelligence Index, equal to Claude Haiku 5.5, at an estimated $0.14 per index task compared with Haiku’s $0.41. The same evaluation found a trade-off: Ember answered relatively few questions correctly on a factual-knowledge test, but often declined to answer rather than guessing.
Muse Ember is the efficient tier of Meta’s Muse model line. It is proprietary and available through Meta’s API and the company’s Muse Code coding agent. It accepts text and images and produces text. Artificial Analysis tested it at two reasoning effort settings: max and xhigh. The max version scored 36 on the index; xhigh scored 34 and had an estimated task cost of $0.12.
The reported cost per index task is the average API cost of completing one task in Artificial Analysis’s evaluation, not a price for every real-world request. The source says Ember’s listed input and output token prices match those of Haiku 5.5 and GPT-6 Luna, while its cached-input price is lower. Artificial Analysis attributes the task-cost difference to cheaper cached input and Ember using roughly a third as many output tokens as Haiku to complete the index.
Results varied by task. Ember scored 68.1% on AutomationBench-AA, matching Meta’s more expensive Muse Wonder and exceeding Haiku 5.5’s reported 35.4%. It led Haiku on Terminal-Bench 4.0 but trailed it on FrontierCode 2.0. On AA-Omniscience, Ember answered 21.1% of questions correctly and declined 64%; its reported hallucination rate was 17.4%, compared with 34.2% for Haiku 5.5. These are benchmark results reported by Artificial Analysis, not guarantees of performance in other settings.
Meta Muse Ember’s AI Trade-Off:Lower Cost, Frequent Abstentions
Artificial Analysis found Muse Ember matched Claude Haiku 5.5 on its Intelligence Index at a lower average benchmark task cost. Its factual accuracy and frequent refusals make task-specific evaluation essential.
Lower benchmark cost
The reported average task cost was lower than Haiku 5.5’s, but it does not predict a business’s production bill.
- $0.14 per index task
- Score of 36 at max effort
- $0.12 per task and score of 34 at xhigh effort
- $0.41 per index task
- Score of 36 at max effort
- Same listed input and output token prices as Ember
Performance varies by task
The matching overall index score does not mean the models perform equally across evaluations.
Strong benchmark result
Ember scored 68.1% on AutomationBench-AA, matching Muse Wonder and exceeding Haiku 5.5’s reported 35.4%.
Mixed comparison
Ember led Haiku on Terminal-Bench 4.0 but trailed it on FrontierCode 2.0.
Frequent abstentions
On AA-Omniscience, Ember answered relatively few questions correctly and often declined rather than guessing.
Where it may fit
A lower benchmark cost may make Ember worth evaluating for repeated workflows, depending on answer quality and fallback needs.
Compare answer quality, refusals, speed, and total cost using the same prompts and data as existing models.
Evaluate cases where relevant information is provided and where the model may lack the answer.
Decide whether refusals should route to a search tool, another model, or a human reviewer.
Lower Costs for Repeated AI Tasks
The reported price difference could matter to organizations running high-volume workflows such as extraction, tagging, form filling, and routine agent steps. If Ember performs adequately on a company’s own data, a lower average cost on the index could make it worth testing as a lower-cost option alongside more capable or more expensive models. The benchmark figure alone does not establish savings for a particular business: actual costs depend on prompts, context, caching, and how often a system must retry or hand work to another model.
Ember’s frequent refusals also change how its performance may fit a workflow. For tasks where a system should avoid inventing an answer, a clear refusal can be preferable to a confident but incorrect response. That behavior still needs a plan: a product may need to route unanswered questions to a search tool, a human reviewer, or another model. For tasks requiring broad factual recall without supporting information, the reported low accuracy may be a serious limitation.
The score comparison is useful but narrow. Artificial Analysis’s index aggregates nine evaluations; matching another model’s overall score does not mean the two perform equally on every task. The reported differences between coding tests and knowledge tests underline why buyers should examine task-level results rather than relying on a single index number.
Popular right now
Samsung 48 4K OLED Smart TV (2026) with WireLogic 8ft Ultra High Speed HDMI Cable
Apple AirPods Pro (2nd Generation) Wireless Earbuds with USB‑C Charging, Up to 2X More Active Noise Cancelling, Transparency Mode, Adaptive
Bose QuietComfort Earbuds (2nd Gen) – True Wireless Bluetooth Earbuds with Active Noise Cancellation
As an affiliate, we earn on qualifying purchases.
What the Benchmark Measures
Artificial Analysis describes its Intelligence Index as a measure drawn from nine evaluations. Its reported cost per task is an average of API costs on that index. The figures therefore compare model performance and cost under the evaluation’s conditions; they do not constitute an independent audit of every use case or a forecast of a customer’s bill.
The source reports that Ember produces 440 output tokens per second and completes an index task in an average of 3.2 minutes on Meta’s API at max effort. It also lists a one-million-token context window. Those measurements are specific to the reported setup and are not a promise that every request will run at the same speed or fit within the same cost.
In the cited comparison, Muse Wonder scored 44 with an estimated $6.95 per index task, while Claude Fable 5.2 at its highest effort scored 65 at $26.54 per task. The source also reports that Ember reached a score of 46 on Artificial Analysis’s Coding Agent Index when used in Meta’s Muse Code. These comparisons use different models and evaluations; they should not be read as proof that Ember is generally better or worse for all coding work.
Limits of the Reported Results
The available source gives benchmark results and pricing comparisons, but does not establish how Muse Ember performs across independent customer workloads. It also does not provide details such as confidence intervals or enough methodology here to assess how much the scores might vary across repeated runs. The index task cost should not be treated as a universal per-request charge.
It remains unclear how often Ember’s refusals would interrupt a given workflow, how well fallback systems could resolve them, and whether the reported cost advantage would hold after retries or human review. The source recommends testing on a business’s own tasks, but provides no results from such deployments. The model’s performance may also differ by reasoning effort setting: the reported xhigh score was lower than the max score, while its average task cost was also lower.
Testing Ember in Real Workflows
The next useful evidence will come from task-specific evaluations by organizations considering the model. Those tests can compare answer quality, refusal rates, speed, and total cost against existing models using the same prompts and data. For workflows that depend on factual knowledge, testing should include cases where the model lacks the answer as well as cases where relevant information is supplied.
Meta’s release makes Muse Ember available through its API and Muse Code, according to the source. No further release milestone or independent deployment study is specified in the material provided. Until such evidence is available, the reported index scores describe the benchmark comparison, while real-world reliability and savings remain open questions.
Key Questions
What is Meta Muse Ember?
Muse Ember is the efficient tier of Meta’s proprietary Muse model line. The source says it accepts text and images and is available through Meta’s API and Muse Code.
How did Muse Ember compare with Claude Haiku 5.5?
Artificial Analysis reported a score of 36 for both models on its Intelligence Index at max effort. It estimated an average index task cost of $0.14 for Ember and $0.41 for Haiku 5.5; those figures describe the evaluation, not every use of either model.
Why does Muse Ember often say it does not know?
In Artificial Analysis’s AA-Omniscience test, Ember declined 64% of questions and answered 21.1% correctly. The reported results suggest it often abstains when it cannot answer, but do not explain the model’s internal decision process.
Is Muse Ember better for coding?
The reported results are mixed. Ember beat Haiku 5.5 on Terminal-Bench 4.0 but scored lower on FrontierCode 2.0. Its reported Coding Agent Index score in Muse Code was 46, but these benchmark results do not establish which model will perform better on a particular coding project.
Does the lower benchmark cost mean businesses will save money?
Not necessarily. The $0.14 figure is an average cost per task on Artificial Analysis’s index. Costs in production depend on a business’s prompts, context, caching, retries, and any review or fallback required when Ember declines.
Source: ThorstenMeyerAI.com
