Voxtral speech and TTS models
Voxtral TTS voice cloning, Voxtral Transcribe 2 and Realtime STT, benchmarks against ElevenLabs, latency, languages, and architecture details.
31.8%
Best tweets about Mistral AI
44 Best Tweets About Mistral AI (2026)
Find the best tweets about Mistral AI, including model releases, Le Chat, Codestral, open models, benchmarks, and developer workflows. Updated every two weeks.
Specific Mistral model and product analysis, benchmarks, deployment experience, and developer examples.
- Creators
- 34
- Updated
Original Xholic analysis
What 44 top Mistral AI posts reveal
Across 44 posts from 34 creators, the largest theme is Voxtral audio models (31.8% share), and ElevenLabs comparisons recur. Developer deployment, Vibe coding agents, and LLM releases follow. Sentiment is 79.5% positive. Three of the five top outliers are first-party posts from MistralAI and mistralvibe; the other two are third-party Voxtral TTS recaps. Critical notes centre on Mistral Small 4 trailing open-weight peers and on Large 4's price and benchmarks.
- Dominant tone
- Positive
- Median score
- 22.7
- Leading format
- Announcement
- Recent posts
- 15.9%
79.5% of posts
All-time engagement
54.5% of posts
Published in 90 days
Conversation map
The themes creators return to
Local deployment and developer integrations
Running Mistral models locally, quantization, vLLM and ComfyUI support, third-party platform integrations, fine-tuning tutorials, and cost-tiered model selection.
22.7%
Strategic partnerships and European sovereignty
Deals with NVIDIA, Microsoft, Samsung, hosting third-party models, and positioning as a sovereign, open-weight European alternative for enterprises.
18.2%
Mistral LLM releases and benchmarks
Mistral Large 4, Small 4, Medium and earlier models: parameters, MoE design, benchmark scores, pricing, and critical reception versus open-weight peers.
15.9%
Mistral Vibe and coding agents
Mistral Vibe terminal coding agent, subagents, Devstral, Leanstral Lean prover agent, and hackathon projects built on Vibe.
15.9%
Compute, funding and acquisitions
Debt financing for Nvidia-powered European data centers, valuation and investment talks, revenue growth, and acquisitions like Koyeb and Emmi AI.
13.6%
Enterprise products and specialized models
Mistral Forge for training models on proprietary data, robotics navigation, industrial physics, and other domain-specific enterprise offerings.
9.1%
Tone and stance
SentimentPositive leads
Author postureSupportive leads
Performance benchmark
- Median likes
- 122
- Median reposts
- 14
- Median replies
- 6
- Median views
- 10K
Posts with media make up 77.3% of this collection. Their median all-time score is 35.6, compared with 4.98 for text-only posts.
Format mix
- Announcement54.5% · score 38.8
- Opinion15.9% · score 5.72
- Tutorial13.6% · score 9.88
- Case Study6.8% · score 21.4
Consensus and debate
Where creators agree, and where they do not
Shared view
Voxtral TTS framed as open-weight ElevenLabs rival
Several creators repeat the same points: a 68.4% win rate over ElevenLabs Flash v2.5, voice cloning from about 3 seconds of audio, and nine languages. Some also note open weights on Hugging Face. Alex Veremeyenko and Sumanth present it as suited to local or edge-device use.
Shared view
Voxtral transcription praised for latency and cost
Mistral AI highlights sub-200ms real-time latency and diarization for Voxtral Transcribe 2. Guillaume Lample calls it one of the most cost-effective transcription APIs. On Artificial Analysis's leaderboard, Voxtral Small (2.9% AA-WER) ties Gemini 3.1 Pro High and ranks ahead of Microsoft's MAI-Transcribe-1 (3.0%), but behind ElevenLabs Scribe v2 (2.3%).
Shared view
Sovereign, open-weight Europe narrative
Several posts frame Mistral as a European alternative. These include posts on the $830M debt raise (Seb Johnson, etn.), Shawn Chauhan's post on the expanded Microsoft Azure partnership, and Rohan Paul's post on Samsung investment talks. They describe governments and regulated industries keeping control over their data and infrastructure, or model weights.
Open debate
Small 4: gains versus peer gap
Artificial Analysis reports that Mistral Small 4 scores 27 on its Intelligence Index, up 12 points from Small 3.2, and uses fewer tokens than its peers. It still lags gpt-oss-120B (33), Nemotron 3 Super (36), and Qwen3.5 122B (42). That contrasts with Shawn Chauhan's upbeat framing of the launch.
Open debate
Large 4 hype meets price and benchmark skepticism
TestingCatalog and Mark Kretschmann welcome the 1T-parameter Large 4 and its planned open-weight release. Bhavy calls it disappointing and places it near GLM-5.3 but behind Kimi K3. Bhavy also calls it expensive relative to Chinese open models at $1.30/$3.90, says some scores feel benchmaxxed, and still calls it a big step up.
Open debate
Voxtral vs ElevenLabs v3 caveat
Alex Veremeyenko celebrates the end of the ElevenLabs moat. He also notes that the 68.4% win rate was measured against Flash v2.5, and that ElevenLabs shipped v3 after that evaluation.
What performs
Patterns behind standout posts
First-party posts lead the outliers
The top outlier is Mistral AI's Voxtral Transcribe 2 post (590.55, 26x median). Its NVIDIA partnership post follows at 18.69x. The MistralAI account has a median score of 507.47 across 2 posts.
Media posts score higher than text
77.3% of posts include media. Media posts have a median all-time score of 35.56, versus 4.98 for text-only posts.
Voxtral theme scores highest
The Voxtral audio theme has the highest theme median, at 73.57. It is ahead of Vibe coding agents (21.382), LLM releases (11.671), developer deployment (9.86), partnerships (5.25), funding (5.08), and enterprise models (1.62).
List and announcement formats lead
LIST posts have the highest format median at 81.124, though from only 3 posts. ANNOUNCEMENT posts make up 54.5% of the set and have a median of 38.78. OPINION posts sit at 5.719.
Statistical standouts
- View standout post 1Score 590.5 · 26× median
- View standout post 2Score 424.4 · 18.69× median
- View standout post 3Score 392.3 · 17.28× median
- View standout post 4Score 308.7 · 13.59× median
- View standout post 5Score 156.7 · 6.9× median
Creator landscape
Who shapes this conversation
The five most represented creators account for 22.7% of the selected posts.
1. Artificial Analysis
@ArtificialAnlys
2 posts
2. etn.
@etnshow
2 posts
3. simp 4 satoshi
@iamgingertrash
2 posts
4. Chubby♨️
@kimmonismus
2 posts
5. Mistral AI
@MistralAI
2 posts
6. Mistral for Developers
@MistralDevs
2 posts
Third-party recap among top outliers
Chubby (kimmonismus) has a median score of 220.08. Their Voxtral TTS recap is the third-largest outlier at 17.28x median, ahead of the Vibe 2.0 launch post. The recap cites figures of roughly 63%/70% and five-second cloning, which differ from the 68.4% and 3-second figures in other posts.
Developer examples from Mistral's own channels
The mistralvibe and MistralDevs accounts showcase Vibe 2.0 and hackathon builds such as Vibecheck and AR coding. They also cover vLLM's day-zero support for Voxtral Realtime, which together provide concrete deployment examples.
Long-tail hands-on developer posts
Smaller creators describe local and cost-driven use. Examples include a simple local server for Mixtral, 50 tokens/s on $800 consumer hardware with a Mistral model, a ComfyUI node for Voxtral-4B TTS, and Mistral Medium used as a cheaper enrichment tier.
Since the previous snapshot
What changed since Aug 25, 2026
- 61.4% of the selected posts remained.
- The creator count changed by -1.
- The leading sentiment remained stable.
How this analysis was made
Themes, sentiment, stance, and post format are classified per tweet. All counts, shares, medians, creator concentration, freshness, and performance comparisons are then calculated directly from the published snapshot.
Xholic's all-time score compares engagement while accounting for reach, post age, and creator consistency. It is used for relative comparisons within this collection.
This report analyzes the exact 44-post snapshot shown below. AI identifies editorial categories and drafts explanations; all statistics are calculated from the snapshot, and every narrative claim is checked against cited posts before publication.
Best Mistral AI tweets
Top Mistral AI tweets from 34 creators
Ranked 01–44
- 01
@MistralAI ·
Introducing Voxtral Transcribe 2, next-gen speech-to-text models by @MistralAI. State-of-the-art transcription, speaker diarization, sub-200ms real-time latency. Details in 🧵
Watch video- 117Replies
- 443Reposts
- 3.9KLikes
- 651.5KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 02
@MistralAI ·
🚀Announcing a strategic partnership with NVIDIA to co-develop frontier open-source AI models, combining Mistral AI’s frontier model architecture and full-stack AI offering with NVIDIA’s leading compute infrastructure and development tools.
- 107Replies
- 409Reposts
- 4.1KLikes
- 251.3KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 03
@kimmonismus ·
Mistral AI released Voxtral TTS, a 3-billion-parameter text-to-speech model with open weights that the company says outperformed ElevenLabs Flash v2.5 in human preference tests roughly 63% of the time on standard voices and nearly 70% on voice customization. The model runs on about 3 GB of RAM, achieves 90-millisecond time-to-first-audio, supports nine languages, and can clone a voice from just five seconds of reference audio, including cross-lingual adaptation that preserves the speaker's accent.
- 39Replies
- 116Reposts
- 1.3KLikes
- 75.4KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 04
@mistralvibe ·
Mistral Vibe 2.0 is now available on Le Chat Pro and Team plans. Build, maintain, and ship code faster with the terminal-native coding agent by @MistralAI. Here’s what’s new 🧵
Watch video- 84Replies
- 354Reposts
- 2.6KLikes
- 388KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 05
@_avichawla ·
An open-weight alternative to ElevenLabs! Voxtral is a TTS model by Mistral with: - just 4B params - 70ms latency for voice agents - voice cloning from 3s of audio - 9 languages + cross-lingual transfer - 68.4% win rate over ElevenLabs Flash v2.5 Open weights on Hugging Face.
Watch video- 9Replies
- 47Reposts
- 328Likes
- 25.1KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 06
@jeffboudier ·
What a week for open audio models! 🕺 💃 I demo: 🗣️ Voxtral 4B TTS from @MistralAI 🎙️ Transcribe 2B from @cohere 🏭 and how to run a batch transcribe job in 1 line of CLI using @vanstriendaniel uv script links below
Watch video- 4Replies
- 42Reposts
- 340Likes
- 32.6KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 07
@AlbertQJiang ·
https://t.co/TMFEJO05cE Excited to share what the formal team @MistralAI has been building for the last couple of months: an Apache 2 Lean code agent with 6B active parameters. Outperforms open models like Qwen3.5, GLM5, Kimi-K2.5, and very competitive against Claude 4.6. Use for free in Mistral-Vibe: ``` $ uv tool install mistral-vibe --upgrade $ vibe / leanstall ``` enjoy the lean mode via shift+tab!
- 22Replies
- 58Reposts
- 525Likes
- 38.3KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 08
@alex_verem ·
🚨Holy shit. Mistral just killed the ElevenLabs moat. > Voxtral TTS is open-weight, > Clones any voice from 3 seconds of audio, > Runs in 9 languages, > Beats ElevenLabs Flash v2.5 with a 68.4% human preference win rate. ElevenLabs built a moat on proprietary weights and API lock-in. Mistral just put the weights on Hugging Face. The model captures not just the voice but the person. Accents, inflections, intonations, vocal fillers the "ums" and "ahs" that make a voice sound human instead of synthetic. From 3 seconds of reference audio. Zero fine-tuning. Zero shot. The numbers: → 68.4% win rate against ElevenLabs Flash v2.5 in zero-shot multilingual voice cloning → Beats ElevenLabs Flash v2.5 on every one of the 9 supported languages → Matches ElevenLabs v3 on emotional expressiveness and quality → 70ms model latency same time-to-first-audio as Flash v2.5 at higher quality → 4B parameters. Runs on 3GB RAM. Smartphone. Laptop. Edge devices. → 9 languages: English, French, German, Spanish, Dutch, Portuguese, Italian, Hindi, Arabic → Cross-lingual voice cloning French voice prompt generating English speech works out of the box The strategic bet Mistral is making: enterprises don't want to rent a voice. They want to own it. Open weights means the model ships to your infrastructure. Mistral never sees your data. For healthcare, finance, and government Mistral's core customers that's not a nice-to-have. It's a compliance requirement. ElevenLabs built a $1B+ valuation on being the only serious option for production-grade voice AI. Today that changes. The weights are on Hugging Face. The API is $0.016 per 1,000 characters. Any developer can clone a voice, run it locally, and ship a voice agent without sending a single audio byte to a third party. One honest caveat: ElevenLabs shipped v3 after this evaluation. Voxtral matches v3 on quality metrics but the 68.4% win rate was head-to-head against Flash v2.5. The race isn't over. It just got a lot more competitive. Voice AI just got its open-source moment.
- 15Replies
- 27Reposts
- 189Likes
- 13.9KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 09
@testingcatalog ·
BREAKING 🔥: Mistral announced Mistral Large 4 "Le Chonk", a new 1T-parameter open-weight model! > 49B active parameters, native multimodality. > Rolling out via APIs today; open-weight release is planned for the end of October. > SOTA on "critical workloads", including cyber defense. Le Chaton Fat "Le Chonk" is here 👀
Watch video- 31Replies
- 27Reposts
- 585Likes
- 39.1KViews
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@GuillaumeLample ·
Very excited to release Voxtral 2! Voxtral 2 comes with two powerful new models: Voxtral Realtime: a SOTA real-time transcription model released under an Apache 2 license, with latency configurable to sub-200 ms Voxtral Mini Transcribe 2: a SOTA transcription model with speaker diarization, word-level timestamps, and context biasing Voxtral 2 supports 13 languages. It’s available on the Mistral API and is one of the most cost-effective transcription APIs on the market.
- 15Replies
- 50Reposts
- 441Likes
- 22.8KViews
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- 11
@akshay_pachaar ·
Mistral just open-sourced a 4B TTS model that clones any voice from 3 seconds of audio. - 68.4% win rate over ElevenLabs Flash v2.5 - 9 language support w/benchmarks - Sub-second latency, 32 concurrent streams on a single H200 - Strong expressivity, emotion + naturalness
- 22Replies
- 41Reposts
- 271Likes
- 23.9KViews
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@wildmindai ·
ComfyUI + Mistral's Voxtral-4B. Text-to-speech. - 20 preset male/female voices, 9 different languages. - fine-grained control. - NVIDIA (CUDA), Apple (MPS), CPU-only https://t.co/mzrmLewPvc
- 3Replies
- 9Reposts
- 122Likes
- 6.8KViews
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@sophiamyang ·
Welcome @gokoyeb to the @MistralAI fam! Our first acquisition! This strategic move strengthens our Compute capabilities and accelerates our mission to build a full-stack AI champion. 🔹 Why Koyeb? Their high-performance serverless platform empowers developers to run and scale AI applications without infrastructure hassles. It’s a perfect fit for Mistral AI’s sandboxes, MCP servers, and on-premises deployments. 🔹 What’s next? Koyeb’s team of 16 talented engineers, including its three co-founders, will join Mistral in March 2026. Together, we’ll optimize GPU usage, enhance inference scaling, and push the boundaries of AI-native applications. This is just the beginning. Stay tuned for more innovations as we combine forces to shape the future of AI infrastructure!
- 20Replies
- 60Reposts
- 461Likes
- 27.5KViews
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@MistralDevs ·
🎙️A new blog from vLLM: Streaming Requests & Realtime API Learn how the vLLM team, in collaboration with us, added support for real-time and streaming capabilities, ensuring day-zero compatibility with Voxtral 4B Realtime. > vllm serve mistralai/Voxtral-Mini-4B-Realtime-2602
- 4Replies
- 11Reposts
- 191Likes
- 9.6KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 15
@abacaj ·
Mistral-7B base outperforms GPT-3.5-turbo in zero-shot financial news sentiment classification with accuracy of 97.3%. Running same test against gpt-3.5-turbo gives accuracy of 79.3%... did not expect this performance from mistral-7B. Data generated by GPT-4 (pos, neg, neutral)
- 29Replies
- 80Reposts
- 987Likes
- 186.7KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 16
@kimmonismus ·
Mistral just dropped Voxtral TTS an open-weight text-to-speech model with ultra-low latency, emotional expressiveness, and support for 9 languages. It outperformed ElevenLabs v2.5 Flash in zero-shot voice cloning tests judged by native speakers.
Watch video- 15Replies
- 22Reposts
- 278Likes
- 18KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 17
@SebJohnsonUK ·
BREAKING: Mistral AI has raised a WHOPPING $830m in debt to build Nvidia-powered AI centres! The first facility is being built near Paris and will start operations before the end of June. It will house 13,800 of Nvidia’s top-end GB300 AI chips. Arthur Mensch, co-founder and CEO, said: "Scaling our infrastructure in Europe is critical to empower our customers and to ensure AI innovation and autonomy remain at the heart of Europe,” The company 20x’ed revenue in the last year, surpassed $400m of ARR and is on track to surpass $1bn this year. It has benefitted massively from Europe's push to sovereignty and also plans to invest €1.2bn to build out new AI data centres in Sweden. The financing has come from a bunch of banks, including Bpifrance, BNP Paribas, HSBC and MUFG. Europe is BUILDING - LETS GO.
- 11Replies
- 35Reposts
- 216Likes
- 9.7KViews
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@ArtificialAnlys ·
Microsoft has released MAI-Transcribe-1: a speech transcription model achieving 3.0% on AA-WER (#4), and is fast at 69x real-time The model was developed by Microsoft AI (MAI)’s Superintelligence team and supports 25 languages including English, French, Arabic, Japanese, and Chinese. MAI-Transcribe-1 API is currently available in public preview via Azure Speech on Microsoft Foundry. On the Artificial Analysis Speech to Text (STT) leaderboard, MAI-Transcribe-1 achieves a 3.0% word error rate on AA-WER for speech transcription accuracy, positioning it 4th overall behind Mistral’s Voxtral Small (2.9% AA-WER), Google’s Gemini 3.1 Pro High (2.9% AA-WER) and ElevenLabs’ Scribe v2 (2.3% AA-WER). It also stands out as one of the faster high-accuracy transcription models available, processing audio at ~69x real-time. See more details below ⬇️
- 4Replies
- 23Reposts
- 247Likes
- 17.5KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 19
@leanprover ·
Interested in Lean and near 🇫🇷 Paris? The inaugural Paris Lean Meetup is happening April 24 at 7:00 PM, hosted by Mistral AI. Two talks: Leonardo de Moura of Lean FRO, and the Mistral team introducing Leanstral, their open-source code agent for Lean 4. Pizza and informal discussion afterward. 🔗 Registration details here: https://t.co/1jarqxeRfA
- 4Replies
- 16Reposts
- 158Likes
- 12.2KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 20
@TheTuringPost ·
.@MistralAI's new Voxtral TTS generates expressive, multilingual speech from just ~3 seconds of reference audio It solves one of the hardest problems in speech, separating what you say from how you sound ➡️ Voxtral factorizes speech into two parts: • semantic tokens → the content (words) • acoustic tokens → the voice (tone, prosody, style) and uses different models for different problems + Voxtral Codec that compresses speech into ultra-low bitrate tokens This gives: - High-quality voice cloning that works best with ~3–25 second - support for 9 languages - 68.4% win rate vs ElevenLabs Flash v2.5 in voice cloning Here's how it works:
- 10Replies
- 21Reposts
- 108Likes
- 8.7KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 21
@Sumanth_077 ·
Clone any voice with just a 3-second audio clip! Mistral just open sourced Voxtral TTS, a text-to-speech model that clones voices from 3 seconds of audio and runs on edge devices. Here's what makes it different. Most TTS models need cloud GPUs and long audio samples. Voxtral runs locally on your phone or laptop and generates voice clones from 3 seconds. The model is 4B parameters. Small enough to fit on a smartphone but accurate enough to capture accents, inflections, intonations, and even vocal fillers like "ums" and "ahs" from the reference voice. It beat ElevenLabs Flash v2.5 in human preference tests with a 68.4% win rate. Key features: • 9 languages supported: English, French, German, Spanish, Dutch, Portuguese, Italian, Hindi, and Arabic • Zero-shot cross-lingual voice adaptation: Feed it a French voice sample and English text, get English speech with a French accent • Real-time performance: 90ms time-to-first-audio, renders 10-second clips in 1.6 seconds • Built for live voice agents and real-time applications Voxtral TTS is part of Mistral's full audio stack alongside their transcription and real-time STT models. Link to the model weights and research paper in the comments!
- 7Replies
- 22Reposts
- 81Likes
- 21.2KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 22
@mark_k ·
Mistral is back with a trillion-parameter beast called "Le Chonk". 🐈🔥 @MistralAI just announced Mistral Large 4, its biggest and most capable model yet. It can process text and images, with 49 billion parameters active at a time out of more than a trillion in total. Mistral says it’s the strongest open-weight model developed outside China, taking aim at coding, AI agents and cybersecurity. The preview API is available today, with the weights scheduled to drop on October 27. That’s the part I’m most interested in: another serious model that developers can run and customize themselves. The open-weight race needs more contenders, and Europe badly needs more companies building.
- 18Replies
- 11Reposts
- 122Likes
- 4.9KViews
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@mistralvibe ·
Here's another💎 from our recent hackathon in Tokyo. Adam Lensenmayer and Leonard Lin built "Vibecheck", a mobile app that lets you control your Mistral Vibe agents from your phone. Includes tool call approval, talking to your agent, and notifications when it needs you.
Watch video- 5Replies
- 15Reposts
- 138Likes
- 9.9KViews
Rewrite this post in your own voice and angle.See the hook, structure, and reusable template behind this post. - 24
@iamgingertrash ·
The model mistral released is an absolute gamechanger for us. We can now run inference at 50 tokens/s on $800 consumer grade hardware Truffle is going to be a fantastic product
- 15Replies
- 18Reposts
- 402Likes
- 79KViews
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@ArtificialAnlys ·
Mistral has released Mistral Small 4, an open weights model with hybrid reasoning and image input, scoring 27 on the Artificial Analysis Intelligence Index @MistralAI's Small 4 is a 119B mixture-of-experts model with 6.5B active parameters per token, supporting both reasoning and non-reasoning modes. In reasoning mode, Mistral Small 4 scores 27 on the Artificial Analysis Intelligence Index, a 12-point improvement from Small 3.2 (15) and now among the most intelligent models Mistral has released, surpassing Mistral Large 3 (23) and matching the proprietary Magistral Medium 1.2 (27). However, it lags open weights peers with similar total parameter counts such as gpt-oss-120B (high, 33), NVIDIA Nemotron 3 Super 120B A12B (Reasoning, 36), and Qwen3.5 122B A10B (Reasoning, 42). Key takeaways: ➤ Reasoning and non-reasoning modes in a single model: Mistral Small 4 supports configurable hybrid reasoning with reasoning and non-reasoning modes, rather than the separate reasoning variants Mistral has released previously with their Magistral models. In reasoning mode, the model scores 27 on the Artificial Analysis Intelligence Index. In non-reasoning mode, the model scores 19, a 4-point improvement from its predecessor Mistral Small 3.2 (15) ➤ More token efficient than peers of similar size: At ~52M output tokens, Mistral Small 4 (Reasoning) uses fewer tokens to run the Artificial Analysis Intelligence Index compared to reasoning models such as gpt-oss-120B (high, ~78M), NVIDIA Nemotron 3 Super 120B A12B (Reasoning, ~110M), and Qwen3.5 122B A10B (Reasoning, ~91M). In non-reasoning mode, the model uses ~4M output tokens ➤ Native support for image input: Mistral Small 4 is a multimodal model, accepting image input as well as text. On our multimodal evaluation, MMMU-Pro, Mistral Small 4 (Reasoning) scores 57%, ahead of Mistral Large 3 (56%) but behind Qwen3.5 122B A10B (Reasoning, 75%). Neither gpt-oss-120B nor NVIDIA Nemotron 3 Super 120B A12B support image input. All models support text output only ➤ Improvement in real-world agentic tasks: Mistral Small 4 scores an Elo of 871 on GDPval-AA, our evaluation based on OpenAI's GDPval dataset that tests models on real-world tasks across 44 occupations and 9 major industries, with models producing deliverables such as documents, spreadsheets, and diagrams in an agentic loop. This is more than double the Elo of Small 3.2 (339) and close to Mistral Large 3 (880), but behind gpt-oss-120B (high, 962), NVIDIA Nemotron 3 Super 120B A12B (Reasoning, 1021), and Qwen3.5 122B A10B (Reasoning, 1130) ➤ Lower hallucination rate than peer models of similar size: Mistral Small 4 scores -30 on AA-Omniscience, our evaluation of knowledge reliability and hallucination, where scores range from -100 to 100 (higher is better) and a negative score indicates more incorrect than correct answers. Mistral Small 4 scores ahead of gpt-oss-120B (high, -50), Qwen3.5 122B A10B (Reasoning, -40), and NVIDIA Nemotron 3 Super 120B A12B (Reasoning, -42) Key model details: ➤ Context window: 256K tokens (up from 128K on Small 3.2) ➤ Pricing: $0.15/$0.6 per 1M input/output tokens ➤ Availability: Mistral first-party API only. At native FP8 precision, Mistral Small 4's 119B parameters require ~119GB to self-host the weights (more than the 80GB of HBM3 memory on a single NVIDIA H100) ➤ Modality: Image and text input with text output only ➤ Licensing: Apache 2.0 license
- 17Replies
- 24Reposts
- 313Likes
- 216.9KViews
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@aryanXmahajan ·
ran 1,500 companies through an AI enrichment pipeline came out the other side with a projected $7,500 bill per run changed which model tier we used for 3 of the calls next run: $147 same output quality. same data. same pipeline. the entire industry defaults to the most powerful model available for every single task this is driving to the mailbox in a Formula 1 car here's how to actually think about it: REASONING TASKS → Claude Sonnet/Opus — complex analysis, voice matching, multi-step reasoning. anything where the output quality directly affects revenue FORMATTING / EXTRACTION (you already have the data, you just need it structured) → GPT-4.1-mini — $0.40 per million input tokens. this is the go-to for classification, JSON extraction, reformatting. replaced GPT-4o entirely. cheaper and better. → Claude Haiku 4.5 — $1 per million tokens. same use case when you need Claude-quality instruction following without the reasoning overhead WEB RESEARCH & ENRICHMENT → Perplexity sonar — $0.006/call. factual lookups, news checks, basic research. most enrichment pipeline calls belong here, not at the tier above it → Perplexity sonar-pro — $0.025/call. cross-referencing multiple sources, financial synthesis, strategic extraction → Mistral Medium with web connector — ~$0.03/call. solid for company enrichment. strategic initiatives, financials, triggers. 97% cheaper than deep research for the same quality on standard enrichment tasks → sonar-deep-research — $0.75-$1.00/call. 125x more expensive than sonar. use it when you genuinely need 20+ search multi-hop browsing. almost never for enrichment pipelines. SPEED-FIRST INFERENCE → Groq — open source models (Llama, Mixtral, Gemma) at inference speeds closed APIs can't match. matters when pipeline response time is the bottleneck OPEN SOURCE → Llama 3, Mistral, Qwen, Deepseek R1 — self-hosted. when data privacy matters, when margins are thin at scale, or when you need to fine-tune on proprietary data. the gap between open and closed is closing faster than most people realize FRONTIER REASONING → GPT o3/o4 — OpenAI's reasoning ceiling. use when you need it. prohibitive at pipeline scale → Grok 3 — xAI's flagship. stronger than most people give it credit for on reasoning and real-time data. it's connected to X natively — underutilized for anything requiring social signal synthesis or market intelligence the rule is simple: match model capability to task complexity most pipeline steps don't need intelligence. they need reliable formatting at cheap compute. that's a $0.40/million tokens problem. not a $15/million tokens problem. the companies winning on AI infra in 2026 aren't the ones with the best models they're the ones who know which model to use for which job
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@Sumanth_077 ·
Make your CLI 10x more powerful with custom subagents! Mistral Vibe is an open-source coding agent that runs in your terminal. Most terminal agents give you one assistant that does everything. With Vibe, you can build custom subagents for targeted tasks like deploy scripts, PR reviews, and test generation, then invoke them on demand. Here's how it works: Each subagent runs independently and handles its job without cluttering your main workflow. You might have one for PR reviews, another for testing, and another for deployment. The agent also has full access to your codebase. It scans your project structure and Git status automatically, so it understands context across multiple files, not just the one you're editing. Key capabilities: • Web search and fetching - query and fetch content from the web mid-session for documentation, or API references. • Multi-choice clarifications - when your instruction is ambiguous, Vibe prompts with options instead of guessing. You pick the right path before it acts. • Full skills support - slash commands for preconfigured workflows, custom subagents for specialized tasks, and extensibility through the Agent Skills standard. Vibe works in your terminal or connects to VS Code, JetBrains, and Zed through the Agent Client Protocol. Mistral Vibe is available through Le Chat Pro and Team plans. It's 100% open source Link to Mistral Vibe in the comments!
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@sophiamyang ·
Nice to see @MistralAI support in @openclaw 🦞 - Mistral Models support - Mistral Embeddings support - Voxtral Audio Understanding support
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@etnshow ·
BREAKING: French AI start-up Mistral has raised $830mn in debt financing to support the construction of Nvidia-powered data centres across Europe. CEO @arthurmensch says: "Scaling our infrastructure in Europe is critical to empower our customers and to ensure AI innovation and autonomy remain at the heart of Europe". The investment comes as the company undergoes rapid growth from companies and governments seeking “sovereign” European alternatives to US groups such as Microsoft, Amazon and Google.
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@MistralDevs ·
CHECK THIS OUT. Coding in augmented reality with @mistralvibe. Built at the Mistral AI Worldwide Hackathon in Paris by @Min0laa, @tr_mz_, and @glwesteneng. Shoutout to Voxtral and Devstral 2 models. Demo was run on a Meta Quest 3.
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@Bhavani_00007 ·
Mistral Large 4 is disappointing. For a model from Europe, this is still reasonably good, and it could be a comeback. European AI has been quiet for ages. It’s near GLM-5.3 but still behind Kimi K3. Still, a big step up from the last Mistral. It’s also pretty expensive compared to Chinese open models, at $1.30 in and $3.90 out. Some of the benchmark scores feel a little benchmaxxed. I’ll test it soon.
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@paulbz ·
Mistral Forge: THE most important product drop from company in long time. You can now train true frontier models from internal knowledge instead of just fine-tuning/RAG. Natively trained vs RAG is a game changer. Real ownership, privacy, domain mastery, no cloud vendor lock-in.
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@iamgingertrash ·
Made a simple server to run the Mixtral model locally: https://t.co/4fChqOzZhb Looks like a pretty good model but is not instruct tuned yet!
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@HaimantikaM ·
If you are building in/with AI, one topic for sure that you should have hands-on experience on is quantization (basically reducing model weight and that often leads of throughput and memory gain). I made a benchmarking video on running a stress test prompt on Mistral 7B Instruct model on a @digitalocean GPU droplet (H200). Watch it till the end to understand if quantization is the right infra choice or not. P.S. - If you are building AI infrastructure, let’s hop on a coffee chat, would love to learn from you. https://t.co/JRZknohw5b
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@etnshow ·
BREAKING: Mistral to begin hosting third-party open models on its platform, starting with GLM-5.2 from Zai, the Chinese AI lab, per Venture Beat.
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@shawnchauhan1 ·
The company that bet everything on OpenAI just signed a multibillion-dollar deal to run open-weight models instead. Microsoft and Mistral expanded their partnership this week, bringing Mistral's open-weight Medium 3.5 model into Azure, Microsoft Foundry, and Copilot Studio. The detail worth noticing: enterprises can now run frontier AI fully disconnected from the internet, a first for Microsoft's cloud. Thousands of GPUs are going into new European data centers specifically so regulated industries can keep their data, and their model weights, entirely under their own control. It lines up with what Microsoft's own leadership has said elsewhere: closed models can cost you twice, once in fees and again when you hand over what you put in every prompt. When your closest partner starts hedging toward open weights, that's not a trend. That's a verdict.
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@SwissCognitive ·
Mistral’s Emmi AI deal points to industrial physics modelling as a more defensible European #AI niche than general-purpose model scale. 🏭📐 #Technology #AINews #ArtificialIntelligence https://t.co/Wj6I85iqSq
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@latentspacepod ·
🆕 Mistral: Voxtral TTS, Forge, Leanstral, & what’s next for Mistral 4 https://t.co/ozJwPxIMI5 @MistralAI is firing on all cylinders. In this episode, Pavan Kumar Reddy and @GuillaumeLample explain why Voxtral TTS is their next big step in audio, why enterprises need models trained on their own proprietary data, why formal proofs could become a key training ground for reasoning, and why the future may belong to labs that can build both frontier models and the full stack around them.
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@softr_io ·
🤖 Gemini and Mistral models are now available when setting up AI agents in Softr Databases and Workflows! Available models include: Gemini: 3 Pro, 3 Flash, 2.5 Flash, Flash-Lite Mistral: Large 3, Medium 3.1, Small 3.2, Ministral 3 14B With more model choices, you can fine-tune each AI agent to your needs — whether that’s faster execution, deeper reasoning, or more cost-efficient processing at scale. 💪
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@TDataScience ·
If you're interested in Python, SLM fine-tuning, and/or emotion recognition, @PetrKorab presents a new, accessible tutorial based on the Mistral Small 3.1 model and a social media-based dataset. https://t.co/Zz5pSEBh28
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@shawnchauhan1 ·
The open-source AI coalition model is maturing into something more structured. Mistral joined NVIDIA's Nemotron Coalition as a founding member. Their first deliverable is a shared base model built on DGX Cloud. Mistral Small 4 ships simultaneously through Mistral's API, Hugging Face, and NVIDIA's tooling in one release. That is three distribution channels activated at once, backed by a €1.7 billion funding round and deepening hardware partnership. The gap between open and closed model ecosystems is closing faster than most closed-model providers want to acknowledge. The open-source strategy is no longer scrappy. It is now well-capitalized and infrastructure-backed.
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@vesting_tv ·
Mistral (@MistralAI) built an 8B robot-navigation model that beats multi-sensor systems with one cheap RGB camera. It hit 76.6% on unseen routes. The next robotics winner may sell the software that makes every robot useful.
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@Techmeme ·
Paris-based AI startup Mistral raised $830M in its debut debt financing to build Nvidia-powered data centers across Europe; Mistral plans to spend €4B in total (@tim / Financial Times) https://t.co/hJefJ5xQMA https://t.co/j1Kn3aMtEk 📥 Send tips! https://t.co/wlNZvXuhJs
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@rohanpaul_ai ·
Samsung is discussing a €1B Mistral investment at €20B valuation. It would nearly double Mistral’s €11.7B valuation from its previous financing. In this deal Samsung brings memory chips, devices, manufacturing scale, and direct access to billions of users. Mistral brings models that governments can run with more control over data and infrastructure. Europe wants that control after foreign access restrictions exposed dependence on American AI providers. Microsoft’s separate infra agreement gives Mistral distribution, while Samsung could strengthen its hardware position. --- reuters .com/business/finance/samsung-talks-invest-mistral-20-billion-euro-valuation-ft-reports-2026-07-22/
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