Meta Releases Muse Ember: Matches Claude Haiku on Intelligence Index at One-Third Cost
Meta has launched Muse Ember, its smallest AI model, which scores 36 on the Artificial Analysis Intelligence Index—parity with Claude Haiku 5.5—at an average cost of $0.14 per benchmark task, roughly one-third of Haiku's price. Designed for high-volume agentic tasks, it supports a 1M token context window and aims to reduce operational costs through a 'model division of labor' strategy.
Meta released Muse Ember, its smallest AI model, on November 10, 2026. According to third-party evaluator Artificial Analysis, Ember scored 36 points on the Intelligence Index, placing it practically on par with Anthropic's Claude Haiku 5.5. However, its average cost per benchmark task is only $0.14, approximately one-third of Haiku's $0.41.
The model is specifically designed for frequently repeated agentic tasks such as document processing, tool invocation, and minor programming work. Meta proposes a 'division of labor' architecture: powerful models like Muse Wonder handle complex planning and task decomposition, while the cost-efficient Ember executes the resulting steps. This allows developers to lower the operating costs of their AI agents without relying on expensive frontier models for every action.
Technically, Muse Ember features a context window of around 1 million tokens and accepts text, image, audio, video, and PDF inputs, though it outputs only text. While it excels in automated workflows (scoring 68.1% on AutomationBench-AA) and terminal operations (40.9% on Terminal-Bench 4.0), it performs poorly in factual knowledge tests (21.1% on AA-Omniscience vs. 34.7% for Haiku). This lower accuracy correlates with a higher refusal rate but results in a significantly lower hallucination rate of 17.4%.
Muse Ember is currently available via the Meta Model API, Muse Code, and OpenRouter. For enterprises deploying large-scale AI agents, this release offers a clear cost-benefit trade-off, particularly for structured tasks where execution speed and cost are prioritized over broad factual recall.