Benchmarks & research

Mark Zuckerberg

@finkd

Muse Voice Transcribe is MSL's first real-time audio perception model -- rolling out today. SOTA in streaming speech-to-text, it handles speaker diarization, and endpointing natively in a single model.

J A Z I I

@notjazii

meta muse just mogged fable 5.1 tested fable 5.1 and muse spark 1.3 with same prompt at highest reasoning available and results came out really different > muse spark 1.3 completed task in one minute and costed almost nothing > fable 5.1 completed task in 70 minutes and costed

Deedy

@deedydas

The coolest thing Meta AI's Muse Spark can do by far is counting objects! As you can tell, it's far from perfect. They call it "visual grounding" and it can count objects and do bounding boxes. I've been playing with the new model and here's what I think so far: Good stuff: –

Ornith does really well. TielCoder (https://llm-bench.io/benchmarks/cmt7kp2zj002r01lcmpchvlko) might be even a bit better in coding. Will give it a try soon. Details of the comparison see here: https://llm-bench.io/compare/runs?runs=cmt6ecf8g000001p45vwzux53%2Ccmt6ergk5000701p41hqdyy78%2Ccmt6f2oob000e01p49o9592cb%2Ccmt6fqddm000l01p4l1vm7skd

mindstudio.ai

mindstudio.ai

MindStudio notes Muse Spark 1.3 topped DeepSWE at 75.4 and placed third on Artificial Analysis, yet in a hands-on game-clone test produced "a cube shooting at other cubes."

geekwire.com

geekwire.com

GeekWire reports Amazon blocked Muse from making purchases as of September 20, saying the agent accessed its store without permission, didn't identify itself, and appeared to store credentials.

thehype.

@thehypedotnews

muse glimmer 30b vs nemotron 3.5 lightning – on agentic coding two 30b open-weight agent models, released three days apart, both sold as the model you leave running. same harness, same tools, same tasks, two of them: repair a broken landing page, then write breakout from a spec

Arena.ai

@arena

Meta Muse Video just entered the Video Arena at #3. @AIatMeta’s new video model scored 1459 in the Text-to-Video Arena. It outperforms Alibaba’s HappyHorse 1.0 by +30pts and ranks ahead of Grok Imagine, Sora 2 Pro and Google Veo-3.1 models. Meta has now reached the video AI

Iam_

@SPAC89

Muse Spark 1.3 Ultra Contributor vs Fable 5.1 xHigh Same prompt, both ran for roughly 2 hours The prompt had a self improvement rule: if the independent judges scored the result below 9.5/10, it had to keep improving and try again, What shocked me most was that Muse Spark just

Both local models implement two new features in an existing SuperAsteroids game; the winner's feature branch gets merged and the loser's is deleted.

Vals AI

@ValsAI

Muse Spark 1.2 just cracked the top 5 on the Vals Index, at just $0.69 per test. This is 3x cheaper than Kimi and 10x or more cheaper than Fable, Opus, and 5.6 Sol.

Cline

@cline

Muse Spark 1.1 just launched and it's their most capable coding agent model yet. On Terminal-Bench 2.1 it scores 80.0%, in the same cluster as Opus 4.8 (82.7%) and GPT 5.5 (83.4%). Use it in Cline with the Meta API!

AI at Meta

@AIatMeta

Today we’re also previewing WildArtifactBench, an internal evaluation framework designed to assess agents on complex, real-world tasks across diverse deliverable formats. By using win rates and Elo scores from human and agentic preference judges rather than strict ground-truth

wh

@nrehiew_

Muse Glimmer was trained directly logit distilled from Muse Spark. This means that there isn't a traditional 'base model' in that it was trained from the start on agentic traces. Super cool and haven't seen this approach in a while Welcome back GPT OSS

Artificial Analysis

@ArtificialAnlys

At 30B parameters, Muse Glimmer sits near the Intelligence vs Parameters frontier for open weights models: 5 points above Gemma 4 31B (Reasoning) at the same size, effectively matching Kimi K2.5 (Reasoning) at 33x fewer total parameters, and just behind Qwen3.6 27B (Reasoning),

pilvar (Philippe Dourassov)

@pilvar222

We ran Muse Spark 1.3 on our Cybersecurity benchmark, and it's actually not that good (yet) 😬 - At pass@1, it rediscovers an average of 19/32 CVEs. In comparison, Grok 4.6 scores 23.3/32 - When pooling the results of 3 runs (pass@3), Muse scores 24/32. DeepSeek V4 Pro gets

Artificial Analysis

@ArtificialAnlys

Meta has released Muse Spark 1.2. It's their third release in four months and scores 54 on the Artificial Analysis Intelligence Index, significantly improving agentic knowledge work capabilities over prior releases and putting Meta next to SpaceXAI in a tie for third place

Louis-François Bouchard 🎥🤖

@Whats_AI

We just measured Meta Muse Spark 1.3 on our internal writing benchmark hoping for new SOTA. Unfortunately, it isn't... It enters at #24 of the 87 models we track, up from #31 for Muse Spark 1.1. As @alexandr_wang highlighted, it beats every Gemini configuration we have,

Tom Collins

@thetomcollins

$META just went from 3.5% to 45.4% token share on OpenCode in just over two weeks Muse Spark 1.3 being good + free is enough to become the default for most users Default gets you usage → usage gets you data → data makes the next model better Anthropic and OpenAI can’t afford

A harder rematch between Muse Glimmer and Qwen 3.8 27B (bumped to high reasoning) on the TalkWithMe project.

チャエン | デジライズ CEO《重要AIニュースを毎日最速で発信⚡️》

@masahirochaen

【速報】Metaが30BのオープンモデルMuse Glimmerを公開。18GBのRAMで動く。 久々のMetaからの本格オープンモデル。 小型なので低スペックのPCでも動かせるAIモデル。 ・Apache 2.0で商用利用可、重みはHugging Faceで公開 ・画像も読めるvisionモデル、100言語超に対応 ・MCP Atlas

thehype.

@thehypedotnews

muse spark 1.2 vs gpt 5.6 sol vs kimi k3 vs grok 4.5 – on two landing pages four coding agents built two desktop landing pages from scratch, then had to open them in a real browser, find their own bugs and fix them before they were allowed to hand anything over the setup: each

Artificial Analysis

@ArtificialAnlys

Last week the Intelligence Index vs Cost Pareto frontier moved out substantially. Claude Fable 5.1, Muse Spark 1.3, and GPT-6 Astra each set a new point in efficient intelligence Link to analysis: artificialanalysis.ai/#intelligence-…

Some people told me that the difference in richness and layout between Glimmer and Qwen wasn't clear to them. This example makes it super clear. I'm aware that comparing Glimmer 30B (a dense model) with Qwen 3.6 (a MoE) isn't entirely fair, but if we compare it to the dense Qwen 27B, the gap will likely be even bigger. If you want, I can add the 27B version later. For now, I'm waiting for Qwen 3.8 27B to see how close it gets to the blueprint. As for the technical details: Both were run on a custom llama.cpp build optimized for the RTX 5080, with a temperature of 0.5 and a 125k context window. Regarding the music: I created it myself without using AI I specifically wanted it to sound that weird.

Benchmarked qwen3.8 xhigh, medium and muse glimmer. Xhigh effort mode with qwen3.8 took almost 30hrs. (And still failed on 16 cases because of the 32K output token limit) Medium effort mode and muse glimmer were 3-4 hours each. But I'm actually surprised by the muse glimmer results, they came better than the qwen. These benchmarks are on implicit knowledge of the model, which is a bit unfair to smaller models, but throw in a RAG and I'm sure they get on par with frontier models. I have taken the result of claude models directly from embedeval repo by ecro. I'm not pushing qwen down here, I like how qwen thinks and gives better results. I know with more context and RAG qwen will do better. I'm just appreciating muse here, cause i feel it is underrated. The advantage is efficient kv cache due to sliding window, which can give you more context window.

kgptalkie.com

kgptalkie.com

KGP Talkie asked Qwen 3.8 27B, Muse Glimmer 30B and Gemma 4 26B the same twelve questions ten times each to see which answers the same way twice.

@barkleesanders

@barkleesanders

A static teardown of Meta's Muse macOS app (codename Endo) finding a full computer-use agent in the public build, switched off by five server-delivered feature flags.

Design Arena

@DesignArena

BREAKING: Muse Spark 1.3 (xhigh) takes 1st overall on Website Arena with an Elo of 1362! This is a jump of 5 positions from Muse Spark 1.2, establishing a new Pareto frontier for Speed and Price. Only a month after the release of Muse Spark 1.2, @AIatMeta has topped this

elie

@eliebakouch

extremely exciting to see meta getting back into open weight models with a 30B dense first, and soon muse spark 1.2 they used knowledge distillation from muse spark, architecture wise it's similar to gemma 4 (which is llama 3 + swa (again!) + vision encoder), with scale free QK

AI at Meta

@AIatMeta

To understand whether we're making genuine progress on reasoning, we entered our AI models in five STEM Olympiad competitions. The results: 🏅 Asian Physics Olympiad (APhO): Perfect score, theory exam 🏅 International Physics Olympiad (IPhO): Perfect score, theory exam 🥇 ht

Dan

@DanDr1s

Meta’s Muse Spark 1.3 just scored 62 on Artificial Analysis’ Intelligence Index. That ties Claude Fable 5, but Muse costs 8x less for input and nearly 12x less for output. It also scores above GPT-5.6 Sol, Grok 4.6, Kimi K3, and Gemini 3.8 Flash. Meta is suddenly in the top

John Yang

@jyangballin

My favorite demo from the launch: muse spark 1.1 + opencode runs evaluation of *itself* + mini-SWE-agent (by @KLieret, @closji, urs truly) on DeepSWE!

Paweł Huryn

@PawelHuryn

So, I finally tested Muse Spark 1.3. 2 real repos, 105 planted bugs, find and fix what you can. Original harness and API. Big surprise: Muse Spark 1.3 (max): 33 Fable 5.1 (high): 33 Grok 4.6 (xhigh): 27 Opus 5 (max): 27 Muse Spark 1.3 (high): 19 Meta joined the frontier.

Alex Volkov

@altryne

Here's my full in depth review of @muse (muse.ai) Meta's new FREE AI agent with it's own browser, computer and connectors to anything from FB marketplace to Apple health! I go into setting it up, have it buy stuff securely with @link, and 3 great usecases that

🚨 AI News | TestingCatalog

@testingcatalog

Since the Meta Muse agent isn't available to everyone, here is a quick UI walkthrough of its web version. Highlights 👀 > The animated Muse avatar is very cool! You can ask Muse to change how it looks, and it will generate the new look along with all the animations. >

li yin

@panda_liyin

muse spark 1.1 in AdaL Engineer beats Opus4.8 in Claude Code with 20% of the cost loop engineering, when done right, is beyond just running longer, its delivering better results when contexts are managed well and when workers are better prompted to stay honest. how GANs had

Derya Unutmaz, MD

@DeryaTR_

Here is a crazy Jev @typesafeai example that I’m betting nobody has thought about: I asked @Muse from @Meta to use Jev to select the top 100 unanswered questions in immunology from 10,000 literature-grounded candidates. A few minutes later, it came back with some of the best

Morgan

@morganlinton

Okay, finally finished my Muse Spark 1.3 benchmark. This ended up being the longest-running benchmark I've ever done at @VulcanBench and it definitely looks like Muse has some serious issues at lower effort levels. I was able to run Astra through my v4 eval suite across every

+1

deeplearning.ai

deeplearning.ai

DeepLearning.AI's The Batch covers Muse's security design (isolated VMs, the Sentinel credential layer, prompt-injection classifiers) and its free tier of up to 100M tokens a week.

Muse Glimmer 30B feels significantly more precise and reliable, it almost never drops the ball or breaks rules. However, its designs lack creative depth and richness. Qwen3.6 35B, on the other hand, is prone to more occasional blunders/hallucinations, but its creative output is superior. It generates far richer, more complex voxel worlds and offers higher design quality. LLama.ccp Build Provenance: • Base: llama.cpp upstream (merge 4445f8d, build 661) • CUDA Toolkit 13.1 + MSVC 19.44 + sm_120a-real (native Blackwell PTX) • Flags: GGML_CUDA=ON, GGML_CUDA_FA=ON, GGML_CUDA_FA_ALL_QUANTS=ON, GGML_CUDA_GRAPHS=ON, GGML_NATIVE=OFF • License: MIT (upstream llama.cpp) Do you think Qwen3.6 is still the undisputed king here?