How to Use Meta Muse Ember for High-Volume AI Agent Tasks
Meta Muse Ember matches Claude Haiku at lower cost for agent work. Guide to setup, pricing, and pairing with larger models for efficient workflows.
Meta released Muse Ember on November 9, 2026. It is the smallest model in the Muse family and targets the repetitive steps inside AI agents: tool calls, coding subtasks, routing, extraction, and long-document parsing. It matches Claude Haiku 5.5 on the Artificial Analysis Intelligence Index at about one-third the cost per task.
This guide shows what Ember is built for, how to access it, and when to pair it with larger models like Muse Wonder. All details come from Meta’s official announcement and developer documentation.
What Muse Ember does well
Ember is a multimodal reasoning model with a 1 million token context window and up to 512,000 output tokens. It accepts text, images, audio, and video. Meta designed it for high-volume execution rather than one-shot frontier problems.
On Artificial Analysis evaluations at max reasoning, Ember scores 36 on the Intelligence Index, tying Claude Haiku 5.5 and beating GPT-6 Luna (28). It leads on AutomationBench-AA (37.8 versus 13.9 for Haiku and 20.2 for Luna) and Terminal-Bench 4.0 (40.9). It trails Haiku on FrontierCode 2.0-AA. Meta reports roughly $0.14 per Intelligence Index task versus about $0.41 for Haiku at similar list prices.
Pricing is $0.10 per million input tokens and $0.50 per million output tokens. Cached input costs $0.004 per million. The model ID is muse-ember-1.0. It is available today on the Meta Model API and in Muse Code.
How to start using Ember
Sign in to the Meta developer portal or Muse Code. Create or open an API key. Use the Responses, Chat Completions, or Messages endpoints at the Meta API base. Switch an existing Muse Spark call by changing only the model field.
- Obtain an API key from the Meta Model API console.
- Set the model parameter to
muse-ember-1.0. - Choose a reasoning effort from minimal to max (default is max).
- Send a bounded prompt that matches Ember’s strengths: file extraction, sequential tool calls, or a coding subtask.
- Monitor token use. The large context window helps, but high-volume loops still accumulate cost if prompts stay long.
Ember supports tool calling, structured output, computer use, and image understanding. Early partners including Box, Cline, Oracle, Cloudflare, and You.com tested it on contract review, multi-repo investigation, and coding quests. You.com reported the lowest latency among models they tested, with 36-48 percent fewer output tokens on some tasks.
Pair Ember with larger models
Meta recommends a split: larger models plan, Ember executes volume. Muse Wonder handles the hardest planning and long-horizon reasoning. Muse Spark covers broader coding and agentic loops. Ember runs the repeated sub-steps those models hand off.
In practice, ask Wonder or Spark to break a multi-day project into concrete tasks. Route each bounded task to Ember. This keeps expensive frontier tokens for decisions that need them and uses the cheaper model for the bulk of tool calls and file work. The same pattern works in multi-agent setups where a coordinator model dispatches work.
For Android or multi-platform coding agents, compare Ember against other efficient models on long-horizon benchmarks. See the guide on how Android Bench 2.0 helps you pick AI models for completion rates and cost trade-offs on multi-day engineering tasks.
Tips for better results
Keep prompts focused. Ember performs best on structured, repeatable work rather than open-ended architecture redesigns. Supply clear acceptance criteria and example outputs for each subtask. Use the 1M context window for large codebases or document sets, but trim unnecessary history in long agent loops.
Test latency and token counts on your own workload. Partner reports show speed gains, yet results vary by harness and task type. Start with max reasoning effort, then drop it if quality stays high and cost falls. Cache repeated system prompts or tool schemas to cut input charges.
Avoid using Ember alone for tasks that require the highest reliability on novel scientific or cybersecurity problems. Those still belong with frontier models or verified programs. Ember’s safety profile follows Meta’s standard approach; review outputs for any domain that carries risk.
Limits and next steps
Weights are not public. Ember runs only on Meta’s hosted infrastructure. No local or quantized option exists today. Long-context pricing details appear in the Meta docs; confirm current rates before high-volume production use. Independent evaluations are still early because the model launched the day before this article.
If your agent already uses Claude Haiku or GPT-6 Luna for subtasks, run a side-by-side pilot on the same prompts. Measure completion rate, tokens, latency, and manual cleanup. Ember’s edge is cost per successful task on repetitive work. Confirm it holds on your data.
Conclusion
Muse Ember gives agent builders a lower-cost option for the high-volume steps that dominate token spend. It matches Haiku-level scores on key benchmarks at roughly one-third the cost per task and adds a large multimodal context window. Use it for tool calls, coding subtasks, extraction, and routing. Pair it with Muse Wonder or Spark for planning. Test on your own workloads before switching production traffic.
Check Meta’s research blog and developer docs for the latest pricing and endpoint details. Agent efficiency gains come from matching the right model to each step rather than using one model for everything.
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