Meta Releases Muse Ember as Its Smallest, Most Cost-Efficient Model
Meta Superintelligence Lab released Muse Ember, described as the lab’s smallest and most cost-efficient AI model to date, according to TechCrunch. Meta claims the model provides more intelligence per dollar than Haiku 5.5 and GPT-6 Luna, but the supplied report contains no independent benchmark validating that comparison.
Muse Ember adds a new cost-focused model from Meta Superintelligence Lab, though the reported intelligence-per-dollar advantage remains an unverified vendor claim.
Meta has launched Muse Ember, its smallest and most efficient model, for the high-volume work that keeps AI agents running: tool calls, coding subtasks, routing, extraction, and long-document parsing. It is available now in Muse Code and through the Meta Model API as muse-ember-1.0, with Responses API support. Pricing is $0.10 per million input tokens, $0.004 per million cached input tokens, and $0.50 per million output tokens.
1/ announcing our smallest, most efficient little model is here: muse ember 🔥 built for work that agents repeat all day, it gets you more intelligence per dollar than Haiku, Luna, and all other flash models at any reasoning setting and does it at a third of the cost or less! pic.twitter.com/ClmgPOTrsR— Alexandr Wang (@alexandr_wang) November 9, 2026
Ember has a 1 million-token context window and supports up to 512,000 output tokens, enough to take in a full codebase or document set in one call. Agents can use it to read files, run tests, make targeted fixes, pull records, call tools in sequence, and update systems across apps. It natively processes text, images, audio, and video, including screenshots, charts, scans, voice memos, meetings, and long documents.
#Meta#Meta Superintelligence Lab#Muse Ember#Haiku 5.5#GPT-6 Luna