
Sr Director @AIatMeta Meta Superintelligence Labs; Prev: CTO @CohereAI, GM/VP Product + Eng + AI/ML @Unity, @Quantcast, @awscloud / EMR, Athena. Tweets my own.
Seattle, WA
Joined June 2008
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Saurabh Baji retweeted
Muse Ember from @AIatMeta is now live on OpenRouter! 🔥
Your coding agent just got a whole crew. Use a frontier model like Muse Wonder to plan, then fan the bounded sub-tasks out to Muse Ember and watch them fly 🚀
❤️
BREAKING: Muse Ember by @AIatMeta has redefined the Preference vs Speed Pareto frontier on Overall HTML, knocking out 10 top models from @GoogleDeepMind, @OpenAI, and @DeepSeek_ai.
At 33s per generation, Muse Ember is 9.5× faster on average than the previous Pareto frontier models. It is also at least 34× cheaper on average than all the previous Pareto frontier models it replaced at $0.10/$0.50 per 1M input/output tokens ($0.004 cache). It is also ranked 10th overall with an Elo of 1345!
A huge congrats to the @AIatMeta team on a top-10 placement on the leaderboard!
Saurabh Baji retweeted
Our first version of Muse Ember 🔥
Ember aims to maximize intelligence per GW of inference to help bring personal superintelligence to everyone.
We would love all the feedback to keep hill-climbing. Please send it our way. We will keep hill-climbing on efficiency, user experience, and better approach to train with internal frontier!
More to come 🫡
Saurabh Baji retweeted
Meta's newest model Muse Ember scores 36 on the Artificial Analysis Intelligence Index, matching Claude Haiku 5.5 (max) at a third of the Cost per Task
Muse Ember is Meta's newest efficient-tier model line, with the same input and output token prices as Claude Haiku 5.5 and GPT-6 Luna
Key Takeaways:
➤ On the Intelligence Index vs. Cost per Task Pareto frontier at both max and xhigh effort. Muse Ember (max) is the cheapest model scoring 36 or higher, at $0.14 per task, and Muse Ember (xhigh) scores 34 for $0.12. Claude Haiku 5.5 (max) costs almost 3x more at $0.41 for the same score as Muse Ember (max). Muse Ember (max) is also about 4x cheaper than DeepSeek V4.1 Flash 1017 (max, 33, $0.60) and GLM-5.3-Flash (28, $0.58)
➤ 8 points behind Muse Wonder for about 1/50th of the Cost per Task. Muse Wonder (max) scores 44 at $6.95 per Intelligence Index task
➤ Stands out on AutomationBench-AA. Muse Ember (max) scores 68.1% on AutomationBench-AA, level with Muse Wonder (max), and beats Claude Haiku 5.5 (max) on GDP.pdf (22.8% vs 20.8%), but trails it on AA-Briefcase (1521 vs 1574 Elo)
➤ Mixed in agentic coding in our Intelligence Index evaluations. It beats Claude Haiku 5.5 (max) on Terminal-Bench 4.0 (40.9% vs 32.8%) but trails it on FrontierCode 2.0 (27.2% vs 34.9%)
➤ Ahead of similarly priced models when running in its own coding agent. In Muse Code, Muse Ember (max) scores 46 on the Artificial Analysis Coding Agent Index, ahead of Claude Haiku 5.5 in Claude Code (max, 37) and GPT-6 Luna in Codex (max, 31)
➤ Knowledge is the main weakness, but it rarely hallucinates. Its AA-Omniscience accuracy of 21.1% is the lowest of the similarly intelligent models we compared, against 34.7% for Claude Haiku 5.5 (max) and 50.5% for Gemini 3.8 Flash (high). This is mostly because it declines to answer 64% of questions, which keeps its hallucination rate at 17.4%, half of Claude Haiku 5.5’s (max)
Other details:
➤ Pricing: $0.10 input / $0.004 cached input / $0.50 output per 1M tokens
➤ Reasoning effort: results shown at max effort, with xhigh also on the Intelligence vs. Cost chart ➤ Availability: Meta's first-party API and Muse Code
Saurabh Baji retweeted
Muse Ember's standout agentic result is AutomationBench-AA, where it scores 68.1%, level with Muse Wonder and GPT-6 Astra
Agentic coding is mixed. Muse Ember beats Claude Haiku 5.5 on Terminal-Bench 4.0 (40.9% vs 32.8%) but trails it on FrontierCode 2.0 (27.2% vs 34.9%)
Welcome Muse Ember - our smallest and most efficient model yet! It's a workhorse for agents (tool calls, coding subtasks, routing, extraction, long doc parsing), with more intelligence per dollar than Haiku 5.5 or GPT-6 Luna at any reasoning setting. Matches Haiku's best score at a third of the cost. Dominates the efficiency frontier: no model scores higher for less per task.
Excited to see customers put this to full use - "...impressive accuracy, speed and token efficiency."
Available today on the Meta Model API - dev.meta.ai
Full details: research.meta.ai/blog/muse-e…
It's been just a week since we launched Muse Wonder. The response has been amazing, along with asks to have it on several more surfaces. So here we go!
Muse Wonder is now available on @Cline, @Cloudflare, @Cognition, @Cursor_AI, @OpenRouter, @Oracle and
@Vercel - with more on the way.
Keep building! research.meta.ai/blog/muse-w…
Muse Wonder is now available on @Cline, @Cloudflare, @Cognition, @Cursor_AI, @OpenRouter, @Oracle and @Vercel - with more partners coming soon.
We look forward to seeing what the community builds with Muse Wonder!
Start building 👉 bit.ly/47wMYCp
Muse Wonder now on @cursor_ai 🚀
Saurabh Baji retweeted
1.4.8 made Muse Code a lot more token-efficient, so we wanted you to feel it right away.
🎉 Everyone's usage is reset, effective now. Have fun.
Muse Code 1.4.8 is out and is focused on token efficiency.
- Auto-review and background agents now use a cheaper model when available, and long auto-reviews stop sooner instead of burning tokens.
- We've fixed a bug that routed a small number of users who had enabled contributor mode to the full-priced models.
Muse Spark Open is here - the most capable US open-weights model!
- Strong agentic performance
- 816B total / 42B active params
- 1M context
- Natively multimodal (text, image, video, audio)
Optimized to run on latest Apple M5 Ultra Mac Studios; runs on leading inference engines like SGLang, vLLM, TensorRT-LLM, Hugging Face Transformers, llama.cpp, and Unsloth with optimizations for AMD, Apple, Intel, and NVIDIA silicon!
Saurabh Baji retweeted
Muse Code 1.4.8 is out and is focused on token efficiency.
- Auto-review and background agents now use a cheaper model when available, and long auto-reviews stop sooner instead of burning tokens.
- We've fixed a bug that routed a small number of users who had enabled contributor mode to the full-priced models.
What a year it's been!
14 months ago, I joined MSL as we began overhauling Meta's AI efforts.
today, i'm super proud of this team and this milestone!
check it out: research.meta.ai/blog/muse-w…
Saurabh Baji retweeted
we have identified and fixed the issue that was causing the unusually fast quota usage for muse wonder and muse wonder full. you will now see much lower usage against your limits.
also, as promised, now that we've fixed the issue...
muse code reset!!
Thanks for the quick feedback, folks. Sorry for the trouble, and keep the feedback coming!
Replying to @pratanchandani
This is now fixed! Sorry for the issue, and we have reset all your limits if you ran into this!
Saurabh Baji retweeted
What a crazy time in AI with Meta now jumping fully into the frontier. Now that there are multiple players pushing the state of the art, this is going to lead to a further acceleration of advances, as well as the lowering cost of tokens for even the highest levels of capability.

























