Mistral Large 4 is now open weights, narrowly making it the #1# open weights model developed outside of China @MistralAI has updated and released the weights of their flagship model, Mistral Large 4, with 1.05T total and 52B active parameters. With high reasoning, Mistral Large 4 scores 26 on the Artificial Analysis Intelligence Index v5, up 1 point from Mistral Large 4 Preview (25), with the major differences being increased token efficiency and reduced hallucinations. It sits 1 point ahead of the leading US-developed open weights model, Muse Spark Open (xhigh, 25). On the Artificial Analysis Cyber Index, Mistral Large 4 (high) scores 51, third among current open weights models behind DeepSeek V4.1 Flash 1017 (max, 57) and MiMo-V2.6-Pro (56). Key takeaways: ➤ Narrowly leads non-Chinese open weights models: Mistral Large 4 (high) leads Muse Spark Open (xhigh), the next-best open weights model developed outside China, with the largest gap on Terminal-Bench 4.0 (25% vs. 8%). It trails leading open weights models DeepSeek V4.1 Flash 1017 (max) and GLM-5.3 (max) by 6 points on the Intelligence Index, largely due to weaker coding and agentic knowledge work performance: 15% on FrontierCode 2.0 (vs. 29% and 22%, respectively) and 1349 Elo on AA-Briefcase (vs. 1570 and 1504) ➤ Increased token efficiency: Mistral Large 4 (high) uses 82K output tokens per Intelligence Index task, 39% fewer than Preview (135K), placing it on the open weights Pareto frontier. The seven open weights models that score higher use between 113K and 225K output tokens per task ➤ #2# open weights model on the Coding Agent Index: On the Artificial Analysis Coding Agent Index, Mistral Large 4 (high) scores 48 in Mistral Vibe while using 8.5M tokens per task, about half as many as the leading open weights model GLM-5.3 (max) in Opencode (49, 15.9M). It also scores above another leading open weights model, DeepSeek V4.1 Flash 1017 (max), in DeepSeek Harness (46, 16.7M) ➤ Reduced hallucinations: Mistral Large 4 (high) has a 28% hallucination rate on the updated AA-Omniscience v1.1, down from 41% for Preview and matching Muse Spark Open (xhigh). However, its accuracy remains relatively low at 26%, similar to Ling 3.1 Flash 1019 (27%), and behind DeepSeek V4.1 Flash 1017 (max, 40%) and Muse Spark Open (xhigh, 42%) ➤ Lower price and cost per task than the Preview: Mistral Large 4 is priced at $0.70/$2.10 per 1M input/output tokens, a ~50% reduction from the Preview's pricing of $1.36/$4.18. At the new pricing, it costs $2.06 per Intelligence Index task, down 63% from $5.54 for the Preview. This is comparable to Muse Spark Open (xhigh, $2.02) but more costly than leading open weights models DeepSeek V4.1 Flash 1017 (max, $0.60) and MiMo-V2.6-Pro ($0.26) Key model details: ➤ Size: 1.05T total parameters / 52B active parameters ➤ Context window: 1M tokens ➤ Multimodality: Text and image input, text output ➤ Pricing: $0.70/$2.10 per 1M input/output tokens ($0.07 per 1M cached input tokens) ➤ License: MEOW License (Mistral Ecosystem Open-Weight License), where operating a Model-as-a-Service business requires a separate commercial license with Mistral Congratulations to the team at @MistralAI on the release!

AI for industrial engineering will bring humanity to its next technological era. This is a key pillar of our research, and we're deepening our partnership with NVIDIA to address the needs of the manufacturing world

🎙️ Introducing Voxtral 3 ! A new family of SOTA speech models: ASR: Mini & Small for batch transcription, plus real-time streaming TTS: expressive, multilingual speech, batch + streaming Voice agent SDK: low-latency conversations + tool calling Available via API or on your own infrastructure !

We've just released a comprehensive suite of state-of-the-art voice models Voice will be the most important interface to AI systems, and we deploy voice models physically close to reasoning models

Mistral has released new Speech models! Voxtral Small Transcribe 3 takes the #3# spot on the Artificial Analysis Speech to Text leaderboard, and Voxtral 3 TTS enters the top 12 in both Text to Speech arenas @MistralAI has released Voxtral 3: a new Text to Speech model and three Speech to Text models (Small Transcribe 3, Mini Transcribe 3 and Mini Transcribe Realtime 3). We evaluated all of them. Key takeaways: ➤ Speech to Text: Voxtral Small Transcribe 3 takes the #3# spot on the Artificial Analysis Speech to Text leaderboard with a 2.1% AA-WER Index, ahead of ElevenLabs Scribe v2 (2.2%) and Grok Voice Transcribe 2.0 (2.3%). Voxtral Mini Transcribe 3 lands at #5# with 2.2%. ➤ Streaming Speech to Text: Voxtral Mini Transcribe Realtime 3 scores 3.3% final-transcript AA-WER at the 960 ms setting (#6# of 46) and 3.6% at 480 ms, down from 5.2% for the previous Voxtral Mini Transcribe Realtime. ➤ Text to Speech, Provider Voice: Voxtral 3 TTS scores an Elo of 1,218 across 1,599 arena appearances, ranking #12# of 98, just behind Breeze TTS 2 at 1,221 and ahead of Inworld Realtime TTS-2 Flash and StepAudio 2.5 TTS at 1,216. It ranks #13# in English (US) and #12# in English (UK). ➤ Text to Speech, Controlled Voice: Voxtral 3 TTS scores 1,068 across 1,614 appearances, ranking #12# of 43 with the same 8 cloned voices, behind Eleven v3 at 1,072 and level with Ant Group's Ming-3.0-tts at 1,068. It ranks #10# in Assistants and #10# in English (UK). ➤ Pronunciation Robustness: Voxtral 3 TTS scores 84.4%, ranking #13# of 32 models, behind Qwen-Audio-3.1-TTS-Plus at 84.6% and ahead of HeyGen Voice at 83.2%. It ranks #3# in Standalone Terms at 93.9%. ➤ Pricing: Voxtral 3 TTS is priced at $13/1M characters, compared to Gemini 3.8 Flash TTS at $16.5/1M characters, Eleven v4 Turbo at $40/1M characters and Simba 3.2 at $6.6/1M characters. See more details and listen to samples below ⬇️

Should Europe leave frontier AI to the Americans and the Chinese? The idea has gained ground in Europe. It is a fundamental mistake. Build or surrender: for now, the choice is still ours. Full article below.

Today I'm excited to share that New Material is joining @MistralAI to design sovereign frontier AI. We're establishing a new team, 'Solutions Design' within Mistral: working across product, engineering and AI, the team will co-develop with customers, creating a tight feedback loop between real-world deployment and Mistral’s products.

🇫🇷 Le Chonk joins the sudo L7 leaderboard 🇫🇷 We just added @MistralAI's Mistral Large 4 to sudo L7, our benchmark for staff-level software engineering. Mistral Large 4 scores 13.3%, ahead of Muse Spark 1.3 (12.2%) and Grok 4.7 (10.6%). sudo L7 tests whether coding agents can go beyond writing correct code to exercise the judgment of experienced engineers: navigating messy systems, spotting hidden risks, and knowing when technically correct code is still the wrong thing to ship. Allez, le Chonk ! 🥖 Leaderboard ↓

I’m in Seoul building Mistral’s founding Korea team. Looking for kind, ambitious researchers and engineers to build and customise LLMs for real problems, from data and evals to RL and training at scale. Here until 16th October. DM me or apply here:

We are releasing the only Western OSS LLM that is competitive with the best Chinese OSS models for coding tasks! Mistral Large 4 preview, AKA le Chonk 🐈 is SOTA OSS on cyber and human evals. Check 🧵 below.

Today, we are launching a preview of our new model, Mistral Large 4 (ML4), aka le Chonk 🐈. ML4 is a 1T-parameter model with 49B active parameters, trained natively with multimodal capabilities. It is at the frontier of open models, and by far the strongest open-weight model from the US or Europe. The RL run behind this preview is still in flight and shows no sign of saturation -- we will release a final version before the end of the month along with the weights of the model. 🧵 1/n

Excited to share that I have joined @MistralAI, where I will be building a team in Montreal and across Canada to push the frontier of safe, capable, and open LLMs. Mistral has played, and will continue to play, an important role in keeping frontier AI open and advancing it safely. I deeply believe in Mistral's mission, and since joining, that conviction has only grown after seeing firsthand the ambition and commitment of the people here. A big part of my role will also be building strong bridges between Mistral and @McGillU, @Mila_Quebec, and the broader Montreal and Canadian AI ecosystem. And we are hiring! Across levels and across the LLM stack. If you are excited about pushing the frontier, reach out.