🔍 Read the full analysis: Is Muse Ember Worth 14 Cents Per Task? Its Score Is 36, With A Catch on ThorstenMeyerAI.com
Prime made for students and young adults
- Fast, free delivery for dorm and study essentials
- Prime Video and Amazon Music included
- Member-only deals
TL;DR
Meta released Muse Ember on November 9, 2026. Artificial Analysis measured its Intelligence Index score at 36 and an average cost of $0.14 per index task, compared with $0.41 for Claude Haiku 5.5 at the same score. The trade-off is weaker factual accuracy, offset by more frequent refusals and a lower reported hallucination rate in the test.
Meta released Muse Ember on November 9, and Artificial Analysis measured the model at an Intelligence Index score of 36 for an average of $0.14 per index task. Claude Haiku 5.5 scored the same 36 but cost $0.41 per task in the comparison, while Muse Ember performed worse on a factual-knowledge test and declined more questions.
Artificial Analysis describes Muse Ember as the efficient tier of Meta’s Muse model line. The proprietary model accepts text and images and generates text; Meta offers it through its API and its Muse Code coding agent. The figures in the report concern the model at its “max” reasoning effort setting. At “xhigh,” it scored 34 and had an estimated index-task cost of $0.12.
The reported per-task price is not the same as a token price. Meta’s listed rates are $0.10 per million input tokens and $0.50 per million output tokens, matching the cited rates for Haiku 5.5 and GPT-6 Luna. Artificial Analysis attributes Ember’s lower benchmark-task cost partly to cached input priced at $0.004, which it says is 60% cheaper than Haiku’s, and partly to Ember using roughly a third as many output tokens as Haiku to complete the index.
The results vary by task. Ember scored 68.1% on AutomationBench-AA, matching the result attributed to Meta’s more expensive Muse Wonder and exceeding Haiku 5.5’s 35.4%. It also led Haiku on Terminal-Bench 4.0 and the GDP.pdf test, but trailed on FrontierCode 2.0 and the AA-Briefcase knowledge-work test. In Meta’s Muse Code, it scored 46 on the Artificial Analysis Coding Agent Index, compared with 37 for Haiku in Claude Code and 31 for GPT-6 Luna in Codex.
Is Muse Ember Worth 14 Cents per Task? A score of 36, with a catch
Meta’s Muse Ember matches Claude Haiku 5.5 on one benchmark index at a lower average task cost. The catch: weaker factual accuracy and frequent refusals in a factual-knowledge test.
Same index score, lower task cost
These figures compare average costs on Artificial Analysis’s benchmark tasks—not a fixed price for every request.
| Model | Index score | Cost per task |
|---|---|---|
| Muse Ember (max) | 36 | $0.14 |
| Claude Haiku 5.5 (max) | 36 | $0.41 |
| Muse Wonder (max) | 44 | $6.95 |
| Claude Fable 5.2 | 65 | $26.54 |
Task results are uneven
Ember performed well on some automation and coding evaluations, but its results varied by task.
Strong on AutomationBench-AA
It scored 68.1%, matching Muse Wonder and exceeding Haiku 5.5’s 35.4%.
Higher coding-agent score
In Muse Code, Ember scored 46, versus 37 for Haiku in Claude Code and 31 for GPT-6 Luna in Codex.
Factual accuracy is a concern
On AA-Omniscience, Ember answered 21.1% correctly and declined 64% of questions.
What the factual test shows
In AA-Omniscience, models could decline questions. The reported outcomes are benchmark results, not guarantees of behavior elsewhere.
Where a trial may fit
The lower benchmark-task cost may matter most for repeatable workflows where teams can check output quality and handle refusals.
Test extraction, tagging, form filling, or other workflows using the same inputs as your current model.
Compare completion rates, errors, refusals, response times, and actual API costs.
Route refusals or uncertain outputs to a human or a second model when the workflow needs a complete answer.
Lower Costs, Uneven Performance
The comparison suggests Muse Ember could be worth testing for high-volume, repeatable tasks where the model receives the relevant information, such as extraction, tagging, form filling or steps in an automated workflow. If a workload resembles the benchmark and uses comparable settings, the index figures point to substantially lower API cost than Haiku 5.5 at the same overall score.
But a single index score cannot establish which model is best for a company’s own work. The benchmark’s average cost reflects its own tasks and token use, not a guaranteed price for every request. Ember’s results also show that performance depends on the task: it did well on the cited automation and Terminal-Bench tests, but its factual-knowledge performance was limited. Buyers should evaluate their own prompts, error costs, latency needs and fallback requirements before switching models.
Ember’s frequent refusals may be useful where unsupported answers carry risk, but refusals can also interrupt work that needs a complete answer. A practical deployment may need a review or fallback process for questions it declines, rather than treating lower hallucination figures as a guarantee of correctness.
Popular right now
Ninja CREAMi Scoop & Swirl 13-in-1 Ice Cream and Soft Serve Maker, NC701
Ninja Crispi Microwave and Air Fryer with FusionCrisp, Silver, MW101
Samsung 43-Inch M70H Series 4K Mini LED Smart TV (2026)
As an affiliate, we earn on qualifying purchases.
How the Benchmark Compares
The Artificial Analysis Intelligence Index combines results from nine evaluations. Its score is a benchmark measure, not a direct measure of quality across all business or coding tasks. The source reports Muse Ember (max) at 36 and $0.14 per index task, Haiku 5.5 (max) at 36 and $0.41, and Muse Wonder (max) at 44 and $6.95. It also lists Claude Fable 5.2 at its highest effort level with a score of 65 and a cost of $26.54 per task. These comparisons show different cost-and-score points in this particular index; they do not establish what a specific deployment will cost.
The key caveat is visible in AA-Omniscience, which tests factual questions and allows models to decline. Artificial Analysis reported that Ember answered 21.1% correctly and declined 64% of questions, with a 17.4% hallucination rate. Haiku 5.5 answered 34.7% correctly and had a 34.2% hallucination rate in the same reported comparison. GPT-6 Luna’s reported hallucination rate was 79.3%. These are results from that evaluation, not proof that a model will behave identically in other settings.
Limits of the Cost Comparison
The reported figures do not show how Muse Ember will perform or price out on a reader’s particular workload. Artificial Analysis gives average API costs for its index tasks, but the supplied material does not provide a full breakdown of cost variation across individual tasks or production deployments. Token usage, caching, prompt length and model settings can affect actual bills.
The source also does not report an independent evaluation of the model beyond the cited Artificial Analysis tests, nor does it establish whether the results generalize to all versions or future model updates. The comparison is dated November 9-10, 2026. The reported factual accuracy and hallucination rates should be read as benchmark outcomes, not guarantees that Ember will always abstain or that other models will answer incorrectly.
Test Before Routing Work
The next useful step for businesses is to test Muse Ember on representative tasks before assigning it high-volume work. Teams can compare completion rates, incorrect answers, refusals, response times and actual API costs against their current model, using the same inputs and success criteria.
For information-heavy tasks, deployments may pair Ember with supplied documents or retrieved database results, then route refusals or uncertain outputs to a human or a second model. The source material does not announce a further benchmark milestone or a scheduled product update, so Meta’s next changes and any later independent evaluations remain unconfirmed.
Key Questions
What did Muse Ember score?
Artificial Analysis reported an Intelligence Index score of 36 for Muse Ember at max reasoning effort. At xhigh effort, it reported a score of 34.
Does Muse Ember cost 14 cents for every request?
No. The $0.14 figure is the average API cost per task in Artificial Analysis’s index, not a fixed charge for every user request. Actual cost depends on the task, token use, caching and settings.
How does its price compare with Claude Haiku 5.5?
In the cited index comparison, both models scored 36, while Muse Ember (max) cost $0.14 per task and Haiku 5.5 (max) cost $0.41. The source says Ember’s lower cached-input price and lower output-token use help explain the difference.
What is the main drawback in the reported tests?
Its weakest result was factual knowledge: on AA-Omniscience, it answered 21.1% correctly. It declined 64% of questions, and Artificial Analysis reported a 17.4% hallucination rate in that test.
Who should consider testing Muse Ember?
Teams with high-volume workflows that provide the model with relevant source information may find it worth testing. The benchmark does not establish suitability for every workload, so buyers should measure quality, refusals and actual costs on their own tasks.
Source: ThorstenMeyerAI.com
