Meta’s Muse Ember matches Haiku 5.5’s index score at a third of the cost per task
Written by AI
In an Artificial Analysis evaluation, Muse Ember, which Meta built for routine agent work, matched the index score of Claude Haiku 5.5 at its highest reasoning effort at a third of the cost per task. For prompts of up to 200,000 tokens, 1 million input tokens cost $0.10 and 1 million output tokens $0.50.
Meta Superintelligence Labs released Muse Ember on 9 November. The company said it built the model for tool calls, coding subtasks, request routing, data extraction and processing long documents.
Meta proposes a division of labour in which the larger Muse Wonder plans and Ember handles a large volume of repetitive steps. Ember processes text, images, audio and video. It has a context window of 1 million tokens.
Independent evaluation: same index, different strengths
Artificial Analysis (AA) builds its index from nine benchmarks, according to a chart it published. AA also calculates how much a single test task costs at a model’s list prices. The table compares Ember with Anthropic’s Claude Haiku 5.5 and Meta’s larger models.
| Model (reasoning effort) | Index | Cost per task |
|---|---|---|
| Muse Ember (max) | 36 | $0.14 |
| Muse Ember (xhigh) | 34 | $0.12 |
| Claude Haiku 5.5 (max) | 36 | $0.41 |
| Muse Spark 1.3 (max) | 37 | $2.39 |
| Muse Wonder (max) | 44 | $6.95 |
Source: Artificial Analysis, Intelligence Index v5. Spark 1.3 figures from Meta’s announcement, which cites AA.
The same index score does not mean the same capabilities: Ember and Haiku do well in different component tests. AA said Ember’s token prices are the same as those of Haiku 5.5 and OpenAI’s GPT-6 Luna. TechCrunch disagrees, reporting that Ember is considerably cheaper than both on input token prices.
In AA’s coding evaluations, Ember beat Haiku on Terminal-Bench 4.0 (40.9 per cent vs 32.8 per cent) but lost on FrontierCode 2.0 (27.2 per cent vs 34.9 per cent). Meta’s table gives Haiku’s Terminal-Bench score as 39.2 per cent. Meta tested each model in its best published harness, Haiku in Claude Code. A harness is a program that controls an agent, giving the model tools and running its work steps.
On AA’s coding agent index, Ember scored 46 in Meta’s Muse Code coding agent, Haiku 37 in Claude Code and Luna 31 in Codex.
The sources give different figures for AutomationBench-AA, which measures business process automation. Meta’s announcement, which lists AA as its source, gives Ember 37.8 per cent under strict pass@1 scoring: a task counts as solved only on the basis of the first attempt. AA’s own announcement puts the score at 68.1 per cent, the same as Muse Wonder. No explanation is given for the difference.
AA identifies knowledge as Ember’s weakness. On AA-Omniscience, Ember answered 21.1 per cent of questions correctly, against 34.7 per cent for Haiku. AA said this was mainly because Ember declined to answer 64 per cent of the questions. As a result, its hallucination rate stayed at 17.4 per cent, half Haiku’s figure.
In Meta’s own evaluations, Ember led Haiku and Luna on benchmarks such as Agents’ Last Exam and OSWorld 2.1.
Price and availability
Ember is available as muse-ember-1.0 in the Meta Model API and in Muse Code. For prompts of up to 200,000 tokens, 1 million input tokens cost $0.10, cached input $0.004 and 1 million output tokens $0.50. Prices triple for longer prompts. Meta said the model is also available on Google Cloud and Oracle Cloud.
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