Meta Releases Its Smallest and Most Efficient Model, Muse Ember
Meta AI has released Muse Ember, describing it as its smallest and most efficient model, aimed at complex reasoning execution and tasks that agents perform repeatedly, including tool calling, coding subtasks, routing, extraction, and long-document parsing. Under short prompts, its pricing matches GPT-6 Luna and Claude Haiku 5.5 at $0.10 per million input tokens and $0.50 per million output tokens; beyond each model's respective threshold, prices rise, with Muse Ember at $0.30/$1.50 above 200k. The author tested it across six reasoning levels by generating an SVG of a pelican riding a bicycle, finding that reasoning level does not strictly correspond to the visual quality of the output, and that the high level failed to return a complete SVG.
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The author compares the new model with competitors at the same price tier, breaking down pricing by long-prompt tiers and cost per task, to help assess its position in low-cost reasoning scenarios.
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10th November 2026 - Link Blog
Introducing Our Smallest, Most Efficient Model: Muse Ember. Meta AI just launched their entry in the Claude Haiku/GPT Luna category: Muse Ember, which they describe as:
[...] our smallest and most efficient model designed for complex reasoning execution and the work agents repeat all day: tool calls, coding subtasks, routing, extraction, and long document parsing.
For shorter prompts it's priced the same as GPT-6 Luna and Claude Haiku 5.5 - both are $0.10/million input tokens and $0.50/million output. Today's other big model release, Claude Fable 5.2, is 80x that price at $8/$40.
The pricing is a little less straightforward as prompts get longer, though. Luna, Ember, and Haiku all support up to a million input tokens, but they also all have a threshold after which the price goes up. Taking that threshold into account Ember is priced between the two others:
GPT-6 Luna ≤272k $0.10 $0.01 $0.50
>272k $0.20 $0.02 $0.75
Muse Ember 1.0 ≤200k $0.10 $0.004 $0.50
>200k $0.30 $0.012 $1.50
Claude Haiku 5.5 ≤100k $0.10 $0.01 $0.50
>100k $0.50 $0.05 $2.50
But comparing price-per-token doesn't really make sense these days, when reasoning tokens mean that different models might use dramatically different numbers of tokens to solve the same problem.
For that, one measure that's useful is the Artificial Analysis.
And even more useful than that is their Intelligence Index vs. Cost per Task chart, which shows the following for those three models:
The Muse Ember models sit right on that Pareto line - the line at which you can only improve one dimension by giving up another. Meta call this out in their announcement:
It also sits on the efficiency frontier of the full Artificial Analysis leaderboard: no model scores higher for less per task.
(That grey dot on the Pareto line in the middle of the green area is GPT-6.1 Terra (low) - clearly I should be paying more attention to the Terra family!)
Muse Ember has six reasoning levels - minimal, low, medium, high, xhigh, and max - so I rendered six pelicans riding bicycles. The "high" reasoning level failed to return a full SVG, and the other SVGs were surprising in that the reasoning level didn't cleanly map to the visual quality of the response.
Here's the "max" one, but the others are worth a look too:
The Ember pelicans are comparable in quality to those I got from Muse Wonder, which is 40x the price of Muse Ember.
Posted 10th November 2026 at 7:28 am
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This is a link post by Simon Willison, posted on 10th November 2026.