Mistral AI
B Tier · 7.5/10
**Nov 5: Mistral Studio, the enterprise platform, added managed agents (skills, code sandboxes, subagents and hooks), managed apps, new workflow, enterprise-context, observability and evaluation features, and a Mistral MCP server that deploys from Mistral Vibe or another coding agent with one command; Studio runs in Mistral's cloud, a customer VPC or on premises.** **Mistral Large 4 ('Le Chonk') went open weight on 2026-10-29: the 1T-parameter multimodal MoE (49B active per token, 1M context) is on Hugging Face under the Mistral Ecosystem Open-Weight License, a modified MIT licence that allows commercial use except that Model-as-a-Service providers need a separate licence, and the API price dropped to $0.70 input / $0.07 cached / $2.10 output per 1M tokens, about half the preview rate. Earlier, on 2026-10-20: Voxtral 3, with Voxtral Mini Transcribe 3 at $0.0035 per minute, Voxtral Mini TTS 3 at $13 per 1M characters and a voice agent SDK.** **New 2026-10-15: Timestral, Mistral's first time-series forecasting family (5M, 28M, 123M and 209M parameters, weights on Hugging Face since 10/14 under a non-Apache licence), with the 209M model claimed first on GIFT-Eval, TIME and fev-bench and CPU inference in 10-140 ms.** **Mistral Large 4 ('le Chonk', ML4) entered public preview on 2026-10-06: a 1T-parameter natively multimodal MoE with roughly 50B active parameters, a 1M context window, callable as mistral-large-4 at a preview price of $1.36 / $0.14 cached / $4.18 per 1M tokens (batch at half price; cut to $0.70 / $0.07 / $2.10 on 10/29), with open weights then promised by the end of October, and vendor-claimed open-weight leads in cyber (Cybench 93%), coding (DeepSWE v1.1 61.7%) and agentic work (AutomationBench 59.9%).** **Mistral opened a Munich hub for Physics AI and Industrial AI on 2026-09-28**, confirming the May 2026 acquisition of Emmi AI (30+ physicists and engineers in computational fluid dynamics, structural mechanics and multi-physics simulation), naming BMW (crash simulation) and Siemens Energy as partners, and committing to **build one gigawatt of European compute capacity by 2030**. **Mistral now powers Firefox Smart Window (2026-09-16)** -- Mozilla's beta AI browsing assistant runs on Mistral models for France and North America, UK and Germany later this year, with zero data retention. **EUR 3B Series D at a EUR 21B+ post-money (9/08, Samsung-led)**; Cloudera partnership (9/10); Medium 3 retired 8/31 in favour of Medium 3.5.
Score Breakdown
Benchmark Scores
Benchmarks for Mistral Large 4 preview (vendor-published 2026-10-06; third-party verification pending)
| Benchmark | Description | Score | |
|---|---|---|---|
| DeepSWE v1.1 | 61.7% | ||
| SWE-Atlas-QnA | 59.4% | ||
| Terminal-Bench 4 | 28.3% | ||
| AutomationBench | 59.9% | ||
| Cybench | 93% |
Last updated: 2026-10-06
Personality & Tone
The European pragmatist
Tone: Efficient, terse, and slightly blunt. Mistral answers in fewer words than Claude or ChatGPT, especially on factual questions, and rarely hedges or softens its take.
Quirks: Trained with less Anglocentric data than Llama, so it handles French, German, and Spanish notably better than US-origin models. Refusal rates are lower than ChatGPT or Gemini on most gray-area prompts.
The Good and the Bad
What we like
- +Mistral Large 4 is now a downloadable 1T-parameter model (2026-10-29) under a modified MIT licence, and the API price fell to $0.70 / $2.10 per 1M, so the biggest open-weight model from a Western lab is also one of the cheapest frontier-class APIs
- +Mistral Large 4 (public preview 2026-10-06) is the first Mistral flagship in the 1-trillion-parameter class and the first with a 1M context window; Mistral's own numbers put it ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max on its Coding Agent Index (49.8%) and claim the top score on the Artificial Analysis Cyber Index reproduce-and-patch test (82%) -- vendor-published preview-era figures from 2026-10-06; the 10/29 open-weight release quotes different measures (see the 2026-10-29 knownIssues entry), third-party verification pending
- +Mistral Medium 3.5 (April 29 2026) is Mistral's first 'flagship merged' model -- 128B dense, 256k context, 77.6% on SWE-Bench Verified, in public preview at $1.5/$7.5 per million tokens. Closes most of the coding-benchmark gap to Claude Opus / GPT-5.5 at materially lower API cost
- +Vibe Remote Agents (also 4/29) lets you launch cloud-based coding sessions that run asynchronously and in parallel via CLI or Le Chat -- file diffs, tool calls, and the ability to teleport a local session to the cloud while preserving history and approval state. Unique in the category as of today
- +Le Chat Work Mode (4/29) is the first agentic mode shipped at the consumer-chat tier -- multi-step task completion, cross-tool workflows, research synthesis, inbox triage, with explicit approval gates for sensitive operations
- +Mistral Small 4 (March 2026) unifies the previously-split Small/Magistral/Pixtral/Devstral lines into one 119B MoE Apache-2.0 model. Voxtral TTS (March 2026) fills the speech gap with a competent open-source 4B-param model that runs on consumer hardware
- +Extremely competitive API pricing remains the moat -- Small 4 at $0.20/1M tokens, Medium 3.5 at $1.5/$7.5 per million tokens, against frontier-class quality
What could be better
- −The Large 4 licence is not plain MIT: running a Model-as-a-Service business on it needs a separate commercial licence from Mistral, and self-hosting needs a full 8-GPU node
- −Le Chat web interface is bare-bones compared to ChatGPT or Claude
- −Smaller ecosystem -- fewer integrations and community resources
- −Less brand recognition means less community help when you get stuck
- −Documentation could be better, especially for newer models
Pricing
Le Chat (Free)
- ✓Web chat interface with Mistral models
- ✓Mistral Small 4 + Medium 3.5 available (Medium 3 retired 2026-08-31)
- ✓Basic features, limited rate
API (Mistral Small 4)
- ✓119B MoE, Apache 2.0 open-weight
- ✓Unifies Small/Magistral/Pixtral/Devstral into one model
- ✓Fast, efficient, 128K context
API (Mistral Medium 3.5)
- ✓Public preview SHIPPED 2026-04-29 -- Mistral's first 'flagship merged' model
- ✓128B dense, 256k context, 77.6% SWE-Bench Verified
- ✓Underlies new Vibe Remote Agents + Le Chat Work Mode
API (Mistral Medium 3 -- RETIRED 2026-08-31)
- ✓Launched April 9, 2026
- ✓EU AI Act compliance metadata
- ✓**RETIRED 2026-08-31 alongside Medium 3.1** -- `mistral-medium-2505` was deprecated 2026-05-22 and reached retirement today; the vendor-listed replacement is **Mistral Medium 3.5**
API (Mistral Large 4, open weights since 2026-10-29)
- ✓Price cut on 2026-10-29 from the preview rate of $1.36 / $0.14 / $4.18; the docs model page still labels mistral-large-4 (v26.10) Public Preview
- ✓About 1T total parameters, 49B active per token (52B with embeddings and output layers), 128 routed experts with 4 active, 1.6B vision encoder, 1M context
- ✓Three reasoning efforts: low, medium and high (default)
- ✓Weights on Hugging Face (mistralai/Mistral-Large-4-1T-A52B, plus an NVFP4 checkpoint) under the Mistral Ecosystem Open-Weight License: modified MIT, free for commercial use, but a Model-as-a-Service business needs a separate commercial licence
- ✓Also on OpenRouter, Microsoft Foundry and NVIDIA-hosted endpoints / NIM
API (Mistral Large 3)
- ✓Flagship sparse MoE
- ✓256K context
- ✓MRL license (paid for commercial self-hosting)
API (Voxtral 3, 2026-10-20)
- ✓Voxtral Small Transcribe 3: diarization, context biasing and end-of-turn detection across 50 languages (vendor claims it beats Whisper, Scribe v2 and Gemini 3.5 Transcribe)
- ✓Voxtral Mini Transcribe 3: 4B parameters at $0.0035 per minute
- ✓Voxtral Mini TTS 3: 35+ languages and 50+ dialects, voices that adapt emotion and pace to context
- ✓Voice agent SDK (listen, reason, call tools, speak in one streaming pipeline) and Pro Voice adaptation from 30 minutes of audio
- ✓Mistral API on European, US and global endpoints, or self-deployment
Voxtral TTS
- ✓4B-param open-source speech model, March 2026
- ✓9 languages, runs on consumer hardware
- ✓Apache 2.0
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| Mistral Small 3 / Devstral 2 (24B dense, Apache 2.0) | 10 GB VRAM (Q4) | 1× A100 40 GB FP16 |
| Mistral 14B / 8B / 3B (Apache 2.0) | 6 / 4 / 2 GB VRAM (Q4) | 24 / 16 / 8 GB VRAM FP16 |
| Mixtral 8x22B (legacy) | 64 GB RAM + 24 GB GPU (Q3) | 2× A100 80 GB FP16 |
| Mistral Large 4 (about 1T total / 49B active; open weights 2026-10-29, Mistral Ecosystem Open-Weight License) | One 8-GPU data-center node: Mistral's vLLM and SGLang examples run tensor parallel 8, and the NVFP4 checkpoint cuts memory on Blackwell GPUs; not a single-card model | Full checkpoint across an 8-GPU Blackwell-class node; Mistral publishes no minimum GPU spec, so the API at $0.70 / $2.10 is the practical route for most teams |
| Mistral Large 3 (flagship) | Not self-hostable under free terms -- MRL license | Requires paid commercial license to self-host |
Known Issues
- MISTRAL STUDIO ADDS MANAGED AGENTS AND APPS (2026-11-05, vendor-primary): 'Today, we're expanding Studio with managed agents and apps, alongside new capabilities for workflows, enterprise context, agent observability and evaluations.' **Managed agents** carry skills, 'sandboxes where they can run code', subagents and hooks, and Mistral runs them on 'the proven infrastructure that supports millions of Mistral Vibe sessions each week'. **Deployment:** 'in the cloud, in your own VPC, on-premises or across a combination of environments'. **Developer path:** 'Through the new Mistral MCP server, developers can build with Mistral Vibe or the coding agent they already use, test locally and deploy to their Studio environment with one command.' Mistral pitches Mistral Large 4 ('Le Chonk') as the model to start from and lists ABANCA, Accenture, Alten, BNP Paribas, Capgemini, CMA CGM and Tata Consultancy Services as production customers or partners. **No pricing** for Studio or managed agents was given in the post -- check with Mistral sales.Source: Mistral AI (mistral.ai/news/build-with-studio/, RSS pubDate Thu, 05 Nov 2026, JSON-LD 2026-11-05) -- fetched 2026-11-05 · 2026-11-05
- MISTRAL LARGE 4 WEIGHTS RELEASED, PRICE ROUGHLY HALVED, LICENCE NAMED (2026-10-29, vendor-primary): 'Today, we're releasing the weights of this improved Mistral Large 4.' Mistral says three more weeks of reinforcement learning since the 10/06 preview improved coding and defensive security. **Licence:** 'the Mistral Ecosystem Open-Weight license, a modified MIT license. The model is free to use, modify, fine-tune, and commercialize, with a single condition: businesses that operate a Model-as-a-Service offering must obtain a separate commercial license from Mistral.' The Hugging Face card tags it license: other. **Price:** 'Starting today, Mistral Large 4 is priced at $0.70 input / M, $0.07 cached input / M, and $2.10 output / M tokens' (was $1.36 / $0.14 / $4.18); the docs model page shows the same rates and still labels the model Public Preview. **Model card:** about 1T total and 49B active parameters per token (52B with embeddings and output layers), 128 routed experts with 4 active plus a shared expert, 1M context, reasoning effort low / medium / high, an embedded speculative-decoding draft head, an NVFP4 checkpoint for Blackwell; vLLM and SGLang examples run tensor parallel 8. **Vendor-reported results:** Artificial Analysis Coding Agent Index 48% ('effectively tied' with GLM-5.3 at 47% fewer tokens per task); Terminal-Bench 4.0 41% (+13 points over the preview); Cognition's FrontierCode 2.0 19.7%; Artificial Analysis Cyber Index 51%; 96.6% average refusal on malicious cyber prompts; Artificial Analysis Intelligence Index v5 26, 'the highest among western open-weight models' (the card concedes Chinese open models score higher overall). **Distribution:** Mistral Studio and API, OpenRouter, Microsoft Foundry ('the first cloud provider to bring Mistral Large 4 to customers'), NVIDIA-hosted endpoints and NIM.Source: Mistral: Mistral Large 4 (Le Chonk) is now open weight (mistral.ai/news/mistral-large-4-oss/, JSON-LD 2026-10-29, RSS pubDate Thu 29 Oct 2026) + Hugging Face mistralai/Mistral-Large-4-1T-A52B (API: public, not gated, lastModified 2026-10-29; raw README.md) + docs.mistral.ai/models/mistral-large-4-0 ($0.7 / $0.07 / $2.1, Mistral Ecosystem Open Weight License), all fetched 2026-10-29 · 2026-10-29
- VOXTRAL 3: NEW SPEECH-TO-TEXT AND TTS MODELS PLUS A VOICE AGENT SDK (2026-10-20, vendor-primary): 'we introduce Voxtral 3: a new family of state-of-the-art speech-to-text and text-to-speech models, alongside a voice agent SDK'. Voxtral Small Transcribe 3 handles diarization, context biasing and end-of-turn detection across 50 languages (24 official EU languages plus low-resource Asian and Indic ones), and Mistral claims it outperforms Whisper, Scribe v2 and Gemini 3.5 Transcribe. Voxtral Mini Transcribe 3 is 4B parameters at $0.0035 per minute. Voxtral Mini TTS 3 covers 35+ languages and 50+ dialects at $13 per 1M characters, which Mistral says undercuts OpenAI TTS-1, Deepgram, ElevenLabs Flash and Gemini Flash. Pro Voice adapts a speaker's voice from 30 minutes of audio. Available on Mistral's European, US and global API endpoints or for self-deployment; no new Voxtral 3 weights had appeared on Mistral's Hugging Face org as of 2026-10-29. Date note: Mistral's RSS dates the post Tue 20 Oct 2026 while the page's JSON-LD says 2026-10-22; we use the RSS date, as on the Timestral entry. Separately (10/21) Mistral announced a Large 4-based engineering simulation collaboration with NVIDIA (partnership, no product change).Source: Mistral: Voxtral 3 (mistral.ai/news/voxtral-3/, RSS pubDate Tue 20 Oct 2026, JSON-LD 2026-10-22) + Hugging Face API author=mistralai search=voxtral (newest Voxtral repo 2026-10-08) + Mistral Advances AI for Engineering with NVIDIA (2026-10-21), fetched 2026-10-29 · 2026-10-20
- TIMESTRAL -- MISTRAL'S FIRST TIME-SERIES FOUNDATION MODELS, OPEN WEIGHTS BUT NOT APACHE (weights 2026-10-14, post 2026-10-15, vendor-primary): Mistral's RSS dates 'Introducing Timestral' Thu 15 Oct 2026 (the page's JSON-LD carries an older July date; the RSS and the Hugging Face upload times agree on this week). **What it is:** 'our first family of time-series foundation models' in four sizes -- **5M, 28M, 123M and 209M parameters** -- giving 'probabilistic, multivariate forecasts' with past-only and known-future covariates (including categorical ones) and full quantile outputs. **Claims (vendor-reported, submitted to the GIFT-Eval and TIME leaderboards):** the 209M model 'ranks first on all three of the field's main public benchmarks, GIFT-Eval, TIME, and fev-bench', from a single checkpoint without ensembling; the 28M model beats Chronos-2 (about four times larger) on GIFT-Eval and TIME; the 209M beats TimesFM-3 and Falcon-2.0 with fewer parameters; the Hugging Face card lists GIFT-Eval MASE 0.658 for the 209M. **No GPU needed:** on an 8-thread server CPU, a one-day-ahead forecast from three weeks of 15-minute history takes **10 ms (5M) to 140 ms (209M)**; the 123M is within 0.2% of the 209M at three quarters of its latency. Fine-tuning via Mistral Forge. **Licence:** all four repos (mistralai/Timestral-5M/28M/123M/209M, created 2026-10-14) declare `license: other` -- not Apache 2.0, so check terms before commercial use. No API price or La Plateforme model id announced. Relevance: this is an enterprise-analytics model (sensor telemetry, observability, supply chain, finance), not a chat model; Mistral pitches pairing it with its LLMs in agentic root-cause workflows. Large 4 weights (due end of October) were still not on Hugging Face on 10/15; they were released on 2026-10-29.Source: Mistral (mistral.ai/news/timestral/, RSS pubDate Thu 15 Oct 2026) + Hugging Face API (mistralai/Timestral-209M, createdAt 2026-10-14, license other; model card front matter) -- fetched 2026-10-15 · 2026-10-15
- VOXTRAL MINI 4B REALTIME ARABIC AND LIDSTRAL ARABIC -- TWO ARABIC-FOCUSED OPEN MODELS (Hugging Face repos created 2026-10-08, vendor-primary model cards): **Voxtral Mini 4B Realtime Arabic** is 'a streaming speech-to-text model for Arabic dialects and Modern Standard Arabic', fine-tuned from Voxtral-Mini-4B-Realtime-2602 (about 4.4B parameters, BF16), built for code-switching and live captions; Mistral reports **an average character error rate of 8.82% across seven Arabic benchmarks at a 480 ms transcription delay**, and the card tags Maghrebi, Gulf, Nile Valley, Levantine and other dialects. **LIDstral Arabic** is a fastText language and dialect classifier for Arabic-script text (Apache 2.0) that also separates non-Arabic languages written in the same script (Persian, Urdu, Pashto, Kurdish, Hausa, Malay in Jawi and others). No Mistral blog post accompanied either release; both appear only on Hugging Face. Mistral Large 4 weights were still due by 2026-10-31 and were not on the Hugging Face org as of 10/12 (they landed on 2026-10-29; see the newer entry).Source: Hugging Face model cards mistralai/Voxtral-Mini-4B-Realtime-Arabic and mistralai/LIDstral-Arabic (raw README.md; createdAt 2026-10-08 from the HF API; no newer item in the mistral.ai RSS after 2026-10-06) -- fetched 2026-10-12 · 2026-10-08
- MISTRAL LARGE 4 ('LE CHONK') -- PUBLIC PREVIEW OF A 1-TRILLION-PARAMETER OPEN-WEIGHT FLAGSHIP, WEIGHTS DUE BY THE END OF OCTOBER (2026-10-06, vendor-primary): Mistral launched a **public preview of Mistral Large 4** ('Unofficially ML4, very officially: le Chonk'), callable today on Mistral Studio as `mistral-large-4` (v26.10; the docs model page is dated October 6, 2026, status 'Public Preview', license shown only as 'Open'). **Architecture:** the launch post says 'a 1 trillion-parameter natively multimodal model with 49 billion active parameters'; the docs model card says '52B active parameters and 1.05T total parameters, and a 1.6B vision encoder' with a **1M context window** -- the two vendor figures disagree slightly and both are recorded. Trained 'from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's own datacenters in Europe', with more than 160 languages in the training data. **Rate card (docs model page, per 1M tokens): $1.36 input, $0.14 cached input, $4.18 output; a second column at exactly half ($0.68 / $0.07 / $2.09) matches Mistral's standing 50% batch discount.** Neither the public pricing page nor the docs pricing table listed Large 4 at check time -- the model page is the only price source so far. **Weights: 'We will release the weights by the end of the month'**; until then Mistral is 'red-teaming the model in real-world settings with cybersecurity leaders, vetted partners, and state authorities, who will access the same model with reduced moderation and expanded cyber capabilities'. **Vendor benchmarks (all Mistral-published, no third-party verification yet):** DeepSWE v1.1 61.7%, SWE-Atlas-QnA 59.4%, Terminal-Bench 4 28.3%, Coding Agent Index 49.8% ('ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max'); AutomationBench 59.9%; AA-Briefcase 1,393 Elo; Cybench 93% of 40 challenges; the Artificial Analysis Cyber Index reproduce-and-patch test at 82% ('the highest of any model', with the note that Claude Opus 5.5 and GPT-6 Astra 'score near zero on the same test because they refuse to perform the task'); Lakera B3 attack resistance 93.3%; KORABench 1.691 of 2; Dense 200 visual grounding 42% vs 41% for GPT-6 Astra; a Surge AI blind human coding evaluation placed ML4 Preview second of five (3.74) behind Claude Opus 5 (4.22) and ahead of Kimi K3, GLM-5.3 and GLM-5.2. Mistral calls it 'competitive with the strongest open-source models globally, while significantly outperforming any open-weight model developed in the US or Europe'. **Positioning:** sovereignty and cyber -- 'provider-level refusals can block legitimate vulnerability research', so an open-weight, self-deployable model is pitched at security operations, served from a European deployment 'that Mistral operates end-to-end' plus other regions. **Not published yet:** the weight license, architecture details, the post-training write-up and 'additional benchmarks' are all promised 'as we work toward releasing the weights'; no Vibe availability was stated. **Watch: weights and license by 2026-10-31; a row on the public pricing page; day-one adds on GitHub Copilot or Cursor (none in either changelog as of 10/06).**Source: Mistral (mistral.ai/news/mistral-large-4/, RSS pubDate 'Tue, 06 Oct 2026 12:00:27 GMT', JSON-LD datePublished 2026-10-06T12:00:27Z, on-page 'October 6, 2026'); Mistral docs model page (docs.mistral.ai/models/mistral-large-4-0 -- 'October 6, 2026', 'Public Preview', 'Open', v26.10, 1M context, $1.36 / $0.14 / $4.18 per 1M); Mistral docs models overview (docs.mistral.ai/getting-started/models/models_overview/ -- 'Mistral Large 4 | Open | v26.10') -- all fetched 2026-10-06 via curl with browser UA · 2026-10-06
- MUNICH HUB, THE EMMI AI ACQUISITION ON THE RECORD, AND A ONE-GIGAWATT EUROPEAN COMPUTE PLEDGE (2026-09-28, vendor-primary): 'Today, we are putting that conviction into practice by opening our new hub in Munich', housing 'specialised research teams dedicated to Physics AI and Industrial AI, alongside applied engineers serving our enterprise partners directly'. Two facts this page had not carried: (1) **'Following our acquisition of Emmi AI in May 2026, more than 30 physicists, researchers, and engineers with unique expertise in Physics and Engineering AI joined Mistral'** -- Emmi specialised in 'large-scale AI modelling of computational fluid dynamics, structural mechanics, and multi-physics simulations'; Mistral's pitch is replacing simulations that take 'days per run' with learned physics models, working 'with BMW on crash simulations and engineering AI, and with Siemens Energy on industrial AI applications'. (2) **'To secure Europe's AI sovereignty, Mistral will build one gigawatt of European compute capacity by 2030'**, on top of the in-region inference and sovereign infrastructure announced 8/20. The sovereignty argument is restated around open weights: 'Mistral's model weights are fully accessible to the customer. Our models run on the customer's own infrastructure, trained on their data, operated under European law'. Germany's digital minister Karsten Wildberger is quoted. No product, price or model change; recorded for the acquisition, the compute commitment and the Physics AI line, which is a new product category for Mistral.Source: Mistral (mistral.ai/news/hallo-deutschland/, RSS pubDate Mon, 28 Sep 2026; on-page 'September 28, 2026') -- fetched 2026-09-28 via curl · 2026-09-28
- MISTRAL POWERS FIREFOX SMART WINDOW -- FIRST CONSUMER-BROWSER DISTRIBUTION FOR MISTRAL MODELS, WITH ZERO DATA RETENTION (2026-09-16, vendor-primary): Mistral and Mozilla announced a partnership under which **Firefox Smart Window (beta), Mozilla's AI browsing assistant, 'is now powered by Mistral models'**. Smart Window summarises complex searches, recalls things you clicked away from and sources information from your open tabs. **Regions: France and North America now; the United Kingdom and Germany 'expected to follow later this year'.** Privacy terms as stated: conversations 'aren't saved on Mozilla's servers by default, and partners like Mistral agree to zero data retention'. Mistral frames it as 'two open source advocates' pairing open weights with open distribution, and says the models are fine-tuned on regional languages and dialects so the browser assistant 'feels native'. **What it changes for this page:** Mistral's consumer reach has historically been Vibe (ex-Le Chat) only; Firefox is the first third-party consumer surface at scale, and the zero-retention clause is a concrete privacy commitment worth quoting against Chrome's Gemini integration. Not stated: which Mistral model, whether inference is local or cloud, or any commercial terms.Source: Mistral (mistral.ai/news/mistral-x-mozilla/, RSS pubDate 'Wed, 16 Sep 2026 12:00:00 GMT', on-page 'September 16, 2026', by-line 'Mistral and Mozilla') -- fetched 2026-09-17 via curl with browser UA · 2026-09-16
- EUR 3 BILLION SERIES D AT MORE THAN EUR 21 BILLION POST-MONEY, LED BY SAMSUNG -- THE LARGEST EUROPEAN TECH EQUITY ROUND ON RECORD, PER MISTRAL (2026-09-08, vendor-primary): Mistral announced 'it has raised **EUR 3 billion in a Series D funding round at a post-money valuation of more than EUR 21 billion**, the largest equity fundraising round ever completed by a European technology company, three years after the company's launch.' **Samsung Electronics led**, with co-leads **Scaleup Europe Fund (managed by EQT)** and existing investor **PSG Equity**. **Stated use of funds:** expanding frontier research, scaling compute for training, building out infrastructure (the European sovereign-compute programme announced 8/11), and commercial and international growth; Mistral says it now operates across **20 countries** and supports **125+ global enterprises** including Airbus, ASML and HSBC. **WHY IT MATTERS ON THIS PAGE:** this roughly doubles the valuation from the September 2025 round and puts a strategic hardware investor at the top of the cap table -- Samsung is a memory and foundry supplier to every frontier lab, so the tie-up reads as a compute-and-distribution partnership as much as a financing. It also funds the open-weight strategy explicitly ('sovereign, open-weight AI'), which is the commercial bet that distinguishes Mistral from the closed US labs it competes with on Vibe and the API. **Same window, minor:** a **Cloudera partnership** (9/10) to run Mistral models inside customers' Cloudera environments for regulated industries, and a solutions post (9/09) on migrating 40,000 lines of Fortran 77 to C++ with agents for a European energy operator. Neither changes pricing or product availability. **No model releases from Mistral this window** -- newest model post remains Shieldstral (8/04).Source: Mistral (mistral.ai/news/mistral-makes-sovereign-open-weight-ai-to-frontier/, RSS pubDate 'Tue, 08 Sep 2026 12:00:22 GMT'); Mistral (mistral.ai/news/mistral-x-cloudera/, RSS pubDate 'Thu, 10 Sep 2026 10:42:55 GMT') -- both fetched 2026-09-14 · 2026-09-08
- TWO MISTRAL MEDIUM GENERATIONS RETIRE TODAY -- MEDIUM 3.1 AND MEDIUM 3 BOTH HIT END-OF-LIFE 2026-08-31 (verified on Mistral's model lifecycle table): both models now sit in the **'Deprecated & retired models'** section of Mistral's models overview with a retirement date of **8/31/2026**. Specifically: **Mistral Medium 3.1** (`mistral-medium-2508`, v25.08) -- deprecated **5/22/2026**, retired **8/31/2026**; and **Mistral Medium 3** (`mistral-medium-2505`, v25.05) -- deprecated **5/22/2026**, retired **8/31/2026**. **The vendor-listed alternative for both is Mistral Medium 3.5.** **Why this matters more than a routine EOL:** Medium is Mistral's volume commercial tier, and this retires **two consecutive generations on the same day**, roughly 100 days after a single joint deprecation notice. If you pinned a model string rather than tracking an alias, a `mistral-medium-2508` or `mistral-medium-2505` call breaks today, and the migration target is a different model with a different price -- **Medium 3.5 is $1.5/$7.5 per 1M**, against the **$1 per 1M** this page recorded for Medium 3. **So for anyone still on Medium 3 the retirement is also a price increase, which the lifecycle table does not tell you.** Note Mistral's deprecation-to-retirement gap here was about **three months**, shorter than the twelve months OpenAI gave the Assistants API -- worth knowing when you plan around Mistral model lifetimes generally. Same-day cohort from the same 5/22 notice: **Devstral 2**, **Magistral Medium 1.2**, **Magistral Small 1.2** and **Mistral Nemo 12B** were all retired on 7/31, so this 8/31 pair is the tail of that wave rather than a new announcement.Source: Mistral (docs.mistral.ai/getting-started/models/models_overview/, 'Deprecated & retired models' table -- rows 'Mistral Medium 3.1 | 25.08 | mistral-medium-2508 | 5/22/2026 | 8/31/2026 | Mistral Medium 3.5' and 'Mistral Medium 3 | 25.05 | mistral-medium-2505 | 5/22/2026 | 8/31/2026 | Mistral Medium 3.5') -- fetched 2026-08-31 via curl · 2026-08-31
- AGENTIC SEARCH -- MISTRAL'S RETRIEVAL LAYER, WITH UNUSUALLY LARGE VENDOR BENCHMARK DELTAS (2026-08-20, vendor-primary): Mistral shipped **Agentic Search**, a multi-step retrieval loop positioned explicitly against one-shot RAG. Rather than handing a model a fixed set of retrieved chunks, it **navigates documents using five tools -- search, open, navigate, read and grep** -- and builds on your existing search index rather than requiring a new one. **AVAILABILITY:** through the **Mistral Search Toolkit**, and built into **Libraries in both Studio and Vibe**. Mistral stresses the deployment story for regulated buyers: 'portable and open tooling helps you unlock value from your data **without crossing your isolation boundaries** in the cloud or on-premises.' **THE VENDOR NUMBERS, QUOTED AND LABELLED AS VENDOR-MEASURED -- NO THIRD-PARTY VERIFICATION EXISTS YET:** on **FinanceBench**, correctness on financial filings goes **from 26.7% to 86%** (Mistral describes this as 'up to 3x correctness'); on **OfficeQA Pro**, table-heavy multi-document questions gain **+45.6 points, from 6.3% to 51.9%**; **p90 latency falls up to 39.6%** and token consumption drops '**by up to one-third**' from fewer repeated searches. **HOW TO READ THOSE DELTAS HONESTLY:** a 26.7% baseline is a one-shot RAG configuration Mistral chose, and the size of the gain is a statement about how badly chunk-based RAG performs on dense financial filings as much as about Agentic Search itself. The efficiency claims (latency, tokens) are the more transferable ones because they are architectural -- targeted navigation genuinely issues fewer retrieval calls. **NO PRICING IS PUBLISHED IN THE POST** and it does not state whether Agentic Search is metered separately from Search Toolkit usage; treat cost as unknown until Mistral's pricing page reflects it. **STRATEGIC READ:** this extends the 5/28 Search Toolkit into the agentic layer and pairs with the 8/11 in-region inference push -- Mistral is assembling a sovereignty-first enterprise stack where the differentiator is that the retrieval runs inside your boundary, not that the model is the smartest availableSource: Mistral AI (mistral.ai/news/agentic-search/, RSS pubDate 'Thu, 20 Aug 2026 12:00:17 GMT', fetched 2026-08-20 via curl) · 2026-08-20
- REGIONAL ENDPOINTS GO GA, A PRIORITY TIER ENTERS PREVIEW, AND MISTRAL STARTS HOSTING SOMEONE ELSE'S OPEN MODEL (2026-08-11, vendor post): three changes, two of them concrete product news. **(1) Mistral Regional Endpoints are now generally available** -- customers 'choose whether their inference runs in Europe or the US', aligning inference location with data-residency, regulatory and latency requirements. Read the carve-out Mistral itself states rather than the headline: processing happens in the selected region **'subject to limited, safeguarded transfers to sub-processors that may occur outside that region'** -- so this is regional control, not an absolute guarantee that no byte leaves. **(2) Mistral Priority Tier, public preview** -- committed service levels for mission-critical workloads, custom rate limits, backed by an **uptime SLA**. Mistral's claim: it is 'the only European AI lab to offer both' region choice and a committed SLA-backed service level. No pricing published for either. **(3) The genuinely surprising one: Mistral's platform will serve third-party open models, starting with Z.ai's GLM-5.2**, running on 'the same infrastructure, regional controls, and service commitments as Mistral models.' A frontier lab reselling a Chinese lab's open weights under its own sovereignty guarantees is a real strategic shift -- it concedes that customers want model choice more than they want Mistral-only, and it turns Mistral's European infrastructure into the product rather than the models alone. **USEFUL CROSS-CHECK: this vendor page names GLM-5.2 as the current Z.ai model**, which independently corroborates our standing decision not to ship the unsourced 'GLM-5.5' claim that has circulated on aggregators. **(4) Company/roadmap, not a product:** Mistral is assembling an anchor group of enterprises (**ASML, Amadeus** among the quoted participants) whose multi-year commitments fund European infrastructure, sold as **European Compute Units (ECUs)**, targeting **up to 1 GW of capacity by 2030**. That is a financing structure and a 2030 ambition -- no capacity exists today on the strength of this post, and it should not be read as shipped infrastructureSource: Mistral AI (mistral.ai/news/regional-inference-open-models-new-compute/, RSS pubDate Tue, 11 Aug 2026 12:00:27 GMT, on-page date August 11, 2026, fetched 2026-08-13) · 2026-08-11
- SHIELDSTRAL RELEASED (2026-08-04, vendor-primary): Mistral shipped **Shieldstral 1.0**, a **3B-parameter, Apache 2.0 open-weights, policy-adaptive multimodal safety classifier** -- a guard model that screens prompts, responses, and **images** for harmful content. The design point is that it treats moderation as question-answering: you supply your **safety policy in plain language at inference time**, with **no retraining or fine-tuning**, and it returns a **calibrated yes/no probability from a single forward pass**. Mistral's claim is that it **matches or beats guard models up to 7x its size** on text safety, refusal detection, policy adaptability, and multimodal safety. Practical appeal: it runs on **a single 16GB NVIDIA GPU**, so self-hosters and small teams can put a real moderation layer in front of an open model without renting a second big box. Weights are on Hugging Face as **mistralai/Shieldstral-1.0-3B**; no pricing (open weights, free to download). Caveats worth knowing before you deploy it: **all comparative benchmark numbers are vendor-published and not yet third-party verified**, and **multilingual coverage is listed as future work** -- Mistral did not claim non-English safety performance at launch, which is a notable gap for a lab whose main differentiator is multilingual strength. Context: guard models are becoming table stakes as the **EU AI Act's Article 50 transparency duties went enforceable 2026-08-02**, and an EU-hosted, open-weights, self-deployable classifier is a pointed answer to US-hosted moderation APIsSource: Mistral AI (mistral.ai/news/shieldstral/), Mistral AI news RSS (pubDate 2026-08-04), Hugging Face (mistralai/Shieldstral-1.0-3B) · 2026-08-04
- MICROSOFT PARTNERSHIP EXPANDED -- MULTIBILLION-DOLLAR DEAL (2026-07-21, Microsoft newsroom): Microsoft and Mistral announced a major expansion of their strategic partnership aimed at enterprises and regulated industries (finance, healthcare, manufacturing). Terms: **thousands of NVIDIA Vera Rubin GPUs** allocated for EU-based compute, and **Mistral Medium 3.5 + Mistral OCR 4 now available in Microsoft Foundry and Copilot Studio**. The pitch is control/sovereignty -- cloud, Azure Local, and fully air-gapped/disconnected deployment so regulated customers can run frontier Mistral models on their own terms. Strategically this deepens Mistral's distribution on Azure (Microsoft is also a Mistral investor) and gives European enterprises a non-US-lab frontier option inside the Microsoft stack. Not a new base model -- an availability + partnership expansionSource: Microsoft (news.microsoft.com/source/2026/07/21/microsoft-and-mistral-expand-strategic-partnership-to-give-enterprises-and-regulated-industries-frontier-ai-they-can-control/) · 2026-07-21
- ROBOSTRAL NAVIGATE (2026-07-08, vendor-primary): Mistral's first **embodied-AI navigation model** -- an 8B-param model, built in-house and trained entirely in simulation (400K trajectories across 6K simulated environments, RL via CISPO), that guides **wheeled, legged, and flying robots** using just a single RGB camera + a plain-language instruction (no LiDAR/depth sensors). Vendor benchmarks: **R2R-CE 79.4% success (seen) / 76.6% (unseen)** -- +9.7 pts over the best single-camera approach and +4.5 over the best depth/multi-camera system. Caveats: all results are simulation-only, the pointing-based approach can't handle targets outside the camera's field of view, and **no weights, API, or license were published** -- access is 'talk with our team.' A research/enterprise play, not a product you can use today; notable as Mistral's entry into robotics. Same week (7/9): **Prompt & Skills Management** shipped in Mistral Studio -- a versioned system-of-record for prompts and skillsSource: Mistral AI (mistral.ai/news/robostral-navigate/), Mistral AI news (Studio prompt management, 2026-07-09) · 2026-07-08
- LEANSTRAL 1.5 RELEASED (2026-07-02, hit #1 on Hacker News 7/4): a formal-verification / Lean 4 theorem-proving model -- 119B total / 6B active params, Apache 2.0 open weights (mistralai/Leanstral-1.5-119B-A6B on Hugging Face) plus a FREE API endpoint (leanstral-1-5). Vendor-reported results: saturates miniF2F (100%), 587/672 on PutnamBench, 87% FATE-H / 34% FATE-X. Niche (math/proof engineering) but notable as a genuinely open frontier release in a specialty domain where closed labs dominateSource: Mistral AI blog (mistral.ai/news/leanstral-1-5) · 2026-07-02
- REBRAND (2026-05-28): **Le Chat is now 'Vibe'** -- Mistral merged its consumer chat product into a single agent brand spanning Work Mode and Code Mode, with a new VS Code extension and CLI. Mistral Medium 3.5 (public preview since 4/29, broader rollout 5/22) is the default model powering Vibe's remote coding agents. Adjacent late-May moves: Emmi AI (physics/industrial simulation, via acquisition) added to the enterprise platform and a new Search Toolkit (5/28). If you bookmarked chat.mistral.ai as 'Le Chat,' it's the same product under the new name -- pricing tiers unchangedSource: Mistral AI blog (mistral.ai/news/vibe-agent, mistral.ai/news/vibe-remote-agents-mistral-medium-3-5) · 2026-05-28
- ENTERPRISE PRODUCT (2026-04-28 public preview): Mistral Workflows -- a Temporal-powered durable orchestration engine for AI workloads. Built on the same Temporal core that backs Netflix / Stripe / Salesforce, with Mistral-added streaming, payload handling, multi-tenancy, and observability. Python SDK v3.0, Helm-deployable workers, customer-perimeter data residency. Human-in-the-loop approvals via simple Python (wait_for_input()), full execution tracking in Studio, deploys cloud / on-prem / hybrid. Distinct from Vibe Remote Agents (the consumer-facing async coding sessions); Workflows is the enterprise infra layer that makes them and other AI workloads durable at scale. Live customers cited at preview: ASML, ABANCA, CMA-CGM, France Travail, La Banque Postale, Moeve. Pricing during preview not disclosedSource: Mistral AI blog (mistral.ai/news/workflows) · 2026-04-28
- Mistral Medium 3.5 SHIPPED 2026-04-29 in public preview, accompanied by two net-new agentic offerings: Vibe Remote Agents (cloud-based coding sessions, async + parallel, CLI or Le Chat entry) and Le Chat Work Mode (agentic chat for multi-step tasks across tools). The model is 128B dense, 256k context, and posts 77.6% on SWE-Bench Verified. Pricing is $1.5/$7.5 per million tokens (input/output). 'Flagship merged' framing means Medium 3.5 supersedes Medium 3 for new workloads -- existing Medium 3 deployments continue to workSource: Mistral AI blog (mistral.ai/news/vibe-remote-agents-mistral-medium-3-5) · 2026-04-29
- Le Chat occasionally slower than competitors during European business hoursSource: Reddit r/MistralAI · 2026-03
- Voxtral TTS English output is competent but trails ElevenLabs v3 on expressiveness -- it's positioned as an open-source alternative, not a quality leaderSource: TechCrunch Voxtral coverage · 2026-03
Best for
Developers who want cheap, high-quality API access. Also strong for multilingual applications and European companies that prefer an EU-based AI provider for data residency.
Not for
Non-technical users looking for a polished chat experience. ChatGPT and Claude are much better as consumer products.
Our Verdict
Mistral is the scrappy underdog that keeps surprising people. Their models are impressively efficient -- you get frontier-adjacent quality at a fraction of the API cost, and Mistral Large 4 (public preview October 6, 2026; open weights under a modified MIT licence and a halved API price on October 29) puts a 1T-parameter flagship on that same cheap-and-open footing. But the consumer experience (Le Chat) is rough. This is primarily a developer's tool. If you're building AI applications on a budget, Mistral should be on your shortlist.
Sources
- Mistral AI: Le Chonk brings the brains. Studio puts it to work. (2026-11-05) (accessed 2026-11-05)
- Mistral: Mistral Large 4 (Le Chonk) is now open weight, $0.70 / $0.07 / $2.10, Mistral Ecosystem Open-Weight License (2026-10-29) (accessed 2026-10-29)
- Hugging Face: mistralai/Mistral-Large-4-1T-A52B model card (1T / 49B active, 1M context, licence other) (accessed 2026-10-29)
- Mistral docs: Mistral Large 4 model page ($0.7 / $0.07 / $2.1, still labelled Public Preview, checked 2026-10-29) (accessed 2026-10-29)
- Mistral: Voxtral 3, speech-to-text, TTS and voice agents (RSS 2026-10-20) (accessed 2026-10-29)
- Mistral: Introducing Timestral (2026-10-15) -- time-series foundation models 5M-209M (accessed 2026-10-15)
- Hugging Face: mistralai/Timestral-209M model card (created 2026-10-14, license: other) (accessed 2026-10-15)
- Hugging Face: mistralai/Voxtral-Mini-4B-Realtime-Arabic (created 2026-10-08) (accessed 2026-10-12)
- Hugging Face: mistralai/LIDstral-Arabic (created 2026-10-08, Apache 2.0) (accessed 2026-10-12)
- Mistral: Introducing Mistral Large 4 -- public preview, 1T params / ~49B active, weights end of October, vendor benchmarks (2026-10-06) (accessed 2026-10-06)
- Mistral docs: Mistral Large 4 model page -- mistral-large-4, v26.10, Public Preview, 1M context, $1.36 / $0.14 / $4.18 per 1M (2026-10-06) (accessed 2026-10-06)
- Mistral docs: models overview -- Mistral Large 4 listed as Open, v26.10 (verified 2026-10-06) (accessed 2026-10-06)
- Mistral: Hallo, Deutschland! -- Munich hub for Physics AI and Industrial AI, Emmi AI acquisition (May 2026), 1 GW European compute by 2030, BMW and Siemens Energy (2026-09-28) (accessed 2026-09-28)
- Mistral: Mistral and Mozilla are bringing open, private and multilingual AI to your web browser -- Firefox Smart Window (2026-09-16) (accessed 2026-09-17)
- Mistral: Mistral raises EUR 3B to make sovereign, open-weight AI the technology frontier -- Series D, >EUR 21B post-money, Samsung-led (2026-09-08) (accessed 2026-09-14)
- Mistral: Cloudera and Mistral partner for sovereign enterprise AI (2026-09-10) (accessed 2026-09-14)
- Mistral docs: models overview + deprecated/retired model lifecycle table -- Medium 3.1 and Medium 3 both retired 2026-08-31, replacement Medium 3.5 (verified 2026-08-31) (accessed 2026-08-31)
- Mistral AI: Introducing Agentic Search -- FinanceBench 26.7% to 86%, OfficeQA Pro +45.6pt (2026-08-20) (accessed 2026-08-20)
- Mistral AI: In-region inference, open models, and new European infrastructure for sovereign AI -- Regional Endpoints GA, Priority Tier preview, GLM-5.2 hosting, ECUs (2026-08-11) (accessed 2026-08-13)
- Mistral AI: Introducing Shieldstral (2026-08-04) (accessed 2026-08-04)
- Hugging Face: mistralai/Shieldstral-1.0-3B (accessed 2026-08-04)
- Microsoft: Microsoft and Mistral expand strategic partnership (2026-07-21) (accessed 2026-07-22)
- Mistral AI: Leanstral 1.5 (2026-07-02) (accessed 2026-07-05)
- Mistral AI: Workflows public preview (2026-04-28) (accessed 2026-05-04)
- Mistral AI: Vibe Remote Agents + Mistral Medium 3.5 (2026-04-29) (accessed 2026-04-30)
- Mistral AI official site (accessed 2026-04-30)
- TechCrunch: Mistral releases Voxtral TTS (accessed 2026-04-16)
- SiliconANGLE: hardware-efficient language models (accessed 2026-04-16)
- LMSYS Chatbot Arena rankings (accessed 2026-04-16)
- API testing (accessed 2026-04-16)
Explore more Mistral AI rankings
Deeper leaderboards, benchmarks, task-specific tier lists, and status/pricing pages for Mistral AI.
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Alternatives to Mistral AI
Llama 4 (Meta)
**Nov 2026: no new Llama since April 2025 -- Meta now ships open weights as Muse.** Meta's Llama 4 open-weights family -- Scout (10M context) and Maverick (multimodal 400B MoE) -- is still downloadable and documented at dev.meta.ai/llama, where Llama 4 is still labelled LATEST. Meta's current open weights are Muse Glimmer (30B, Apache 2.0, 2026-08-10) and Muse Spark Open (816B, 2026-10-15), covered on the Muse Spark page
DeepSeek
**DeepSeek-V4.1-Flash-1017 (2026-10-17) replaced V4.1-Flash on the web, app and API at unchanged prices** (the deepseek-flash model name now serves it; MIT weights on Hugging Face), with higher scores and shorter outputs, and DeepSeek opened its in-house **Search and Fetch APIs at $0.75 per 1,000 calls per endpoint** -- server-side Search/Fetch calls through DeepSeek Harness, Claude Code and Codex were free until 2026-10-31 16:00 UTC (00:00 Beijing time on 11/01) and are now past their free period. Earlier: **DeepSeek-V4.1-Flash shipped 2026-09-10**: a 552B MoE on a new Causal Encoder-Decoder architecture (8B active for input, 16B for output), natively multimodal, MIT-licensed on HF, and priced BELOW the V4-Flash it replaces -- $0.15/$0.60 off-peak, $0.30/$1.20 peak per 1M, with cache-hit input cut to $0.003/$0.006. V4-Flash and V4-Flash-Vision-Exp are retired (aliases route to V4.1-Flash). DeepSeek said V4-Pro would be phased out on 9/14, then **reversed that four days later** -- V4-Pro stays on the API at unchanged $0.66/$1.98 off-peak, $1.32/$3.96 peak until V4.1-Pro lands. V4.1-Flash's vendor benchmarks beat V4-Pro (DeepSWE 74.2, Terminal-Bench 2.1 90.6, GPQA 90.9)
Gemma 4 (Google)
Google DeepMind's open-weights model family -- multimodal, 256K context, runs on edge devices **EmbeddingGemma 2 (announced 2026-10-06; weights on Hugging Face since 9/14) adds a 740M-parameter Apache 2.0 embedding model built on Gemma 4 that maps text, code, images, video and audio into one 768-dimensional space, with an 8K context, Matryoshka truncation down to 128 dimensions and about 191 MB of RAM for text-only use on a Pixel 11 Pro.**
DiffusionGemma (Google)
Google DeepMind's experimental open-weights TEXT-DIFFUSION model (June 10, 2026) -- 26B MoE (3.8B active), Apache 2.0, generates 256-token blocks in parallel with bidirectional attention for up to 4x faster output (1,000+ tok/s on H100). Trades some quality vs Gemma 4 for raw speed
Qwen (Alibaba)
**Qwen-Image-2.1 went open-weight (weights 2026-09-14, blog 2026-09-20)** -- a unified text-to-image and editing model with a 7B visual-generation component, native transparent (RGBA) output, up to 10 reference images and mask-guided local edits -- but under a **research-only licence, not Apache**: commercial use needs a separate licence from Alibaba. Alibaba's open-weights + API family, and August 2026 was its biggest open-release month yet: **Qwen3.8-2.4T-A95B -- the Max-class flagship -- went open-weight on 2026-08-08** (custom Qwen3.8-Max licence), **Qwen3.8-27B** dense landed Apache 2.0 (8/05), and **Qwen3.8-Flash-Next** (8/24 weights, 8/26 blog) previews the Qwen4 architecture: 125B main + 51B n-gram embeddings with only 6B active, served as Qwen3.8-Flash at $0.15/$0.47 per 1M. Qwen 3.7 Max remains the GA API flagship ($2.50/$7.50)
GLM / Z.ai (Zhipu AI)
**GLM 5.2 has a named production deployment at Perplexity: on 2026-09-21 Perplexity disclosed that Perplexity Computer sessions are served by GLM 5.2, post-trained by Perplexity on real user sessions** (21.2% relative cut in live tool-call failures). **GLM-5.3 and GLM-5.3-Flash released 2026-08-25** (weights on Hugging Face): 5.3 is a post-training-only upgrade of the 5.2 base that Z.ai calls 'the most capable open-weights model for coding' (Terminal Bench 2.1 88.2, DeepSWE 66.9, CyberGym 84.5) but moves from MIT to a custom licence; 5.3-Flash (320B/18B active, multimodal) stays MIT at $0.15/$0.50 per 1M.
Kimi K3.1 Pro / K3 (Moonshot)
**Kimi K3.1 Pro (launched 2026-10-24, weights on Hugging Face from 2026-11-02) is the new flagship: a post-trained K3 on the same 2.8T total / 104B active MoE with 1M context, shipped as quantization-aware MXFP4 weights under a new Kimi K3.1 Pro License, at $2 / $10 per 1M tokens on the Kimi API, a third below K3. A Highspeed variant runs 2-3x faster at $4 / $20. The K2 series closed to new API users on 2026-10-31 and is due to retire by the end of December.** Moonshot's 2.8T-parameter Kimi K3 (launched 2026-07-16/17) is the largest open-weight model ever released -- 1M context, multimodal, $3/$15 per 1M via API, ranked best-available on Arena.AI at launch. WEIGHTS SHIPPED ~2026-07-26/27 on Hugging Face (2.8T total / 104B activated, safetensors) under a custom Kimi K3 License, not the Modified MIT of the K2 line
Nemotron (Nvidia)
Nvidia's open-weights family -- hybrid Mamba-Transformer MoE architecture, optimized for efficient reasoning on Nvidia hardware. Nemotron 3 Ultra (550B total / 55B active) shipped 2026-06-04 as the family flagship, joining Super (120B/12B, March) and Nano
MiniMax M3
**MiniMax M3.1 Flash (2026-10-19) is a 428B-total, 23B-active multimodal MoE with a 1M-token context at $0.15 input / $0.60 output per 1M tokens ($0.015 cached) on the MiniMax API, M Plan and MiniMax Code. Its weights are now on Hugging Face under the minimax-community licence, which needs a visible Built with MiniMax M3.1-Flash notice for commercial use and written approval above $20M yearly revenue.** **MiniMax-H3's weights are public on Hugging Face (repo 2026-07-28, updated 2026-08-13) under a community licence whose open-weight grant is limited to the US, EU, UK and South Korea**, and **MiniMax Music 3 (2026-08-07)** ships open weights for five-minute full-song generation with a prominent-attribution commercial clause. MiniMax's coding/agent flagship -- M3 (June 1 2026): 1M-token context, MSA sparse attention (>15x decoding speedup at long context), SWE-Bench Pro 59.0%, Terminal-Bench 66.0%. OPEN WEIGHTS LIVE on HuggingFace since June 12 (~428B total / ~23B active, native multimodal, minimax-community license)
Falcon (TII)
UAE Technology Innovation Institute's open-weights family, now led by the hybrid Transformer and Mamba Falcon-H1 line (Falcon-H1R 7B for reasoning, Falcon-H1-Arabic, Falcon-H1-Tiny models from 90M parameters) alongside Falcon 3 and Falcon-E. On 2026-10-06 TII announced Falcon-Emirati (7B), Falcon-ASR (1.6B) and Falcon-OCR-Arabic. Licensed under the TII Falcon License (based on Apache 2.0 with modifications, plus an Acceptable Use Policy)
gpt-oss (OpenAI)
OpenAI's FIRST open-weight models -- gpt-oss-120b (runs within 80GB of memory, near parity with o4-mini on core reasoning) and gpt-oss-20b (runs on 16GB edge devices). Apache 2.0, weights natively quantized in MXFP4. Launched 2025-08-05; gpt-oss-safeguard (2025-10-29 research preview) adds policy-following safety classifiers. Still no gpt-oss successor as of November 2026
IBM Granite 4.2
IBM's enterprise-focused open-weight family. Granite 4.2 (2026-08-25) is IBM's first dense reasoning family: 3B, 8B and 30B with thinking, non-thinking and low-effort modes, native tool calling, 128K native context extendable to 512K, Apache 2.0. Granite 4.0 keeps the hybrid Mamba-2 models, including Nano 350M for CPU. IBM's Granite AI management system holds ISO 42001 certification
Arcee Trinity-Large-Thinking
Arcee AI's US-made open-weight frontier reasoning model, launched 2026-04-01: 398B total parameters, about 13B active, sparse MoE with 256 experts and 4 active (1.56% routing), trained from scratch. The whole Trinity family moved to the Linux Foundation's OpenMDW-1.1 license on 2026-05-29 (no field-of-use restrictions). Arcee's own OpenAI-compatible API serves it at $0.25 / $0.80 per 1M input/output tokens
Olmo 3 / 3.1 (Ai2)
Allen Institute for AI's fully-open reasoning models -- Olmo 3 family (2025-11-20; 7B and 32B; Base, Think, Instruct, RL Zero), the Olmo 3.1 Think 32B and Instruct 32B refresh (2025-12-12), and Olmo Hybrid 7B (2026-03-05, transformer plus linear RNN). Apache 2.0 with open Dolma 3 data, stage checkpoints, and training code. Olmo 3-Think 32B narrows the gap to Qwen 3 32B on roughly 6x fewer training tokens. No Olmo 4 yet as of the November 2026 check
AI21 Jamba2
AI21 Labs' hybrid SSM-Transformer (Mamba-style) open-weight family -- Jamba2 launched 2026-01-08. Two sizes: 3B dense (runs on phones / laptops) and Jamba2 Mini MoE (12B active / 52B total). Apache 2.0, 256K context, mid-trained on 500B tokens
StepFun Step 5 Preview
StepFun's (China) agent-focused open-weight family. Step 5 Preview (2026-09-20) is the new flagship: 600B sparse MoE with 27B active, up to 1M context, text and image input (video too on the API), open weights under the StepFun Model License 1.0, API $1.00 input / $2.70 output per 1M tokens. Step 3.7 Flash (198B MoE, Apache 2.0) remains the smaller open model
Cohere Command A
Cohere's enterprise-multilingual Command A family. Command A+ (2026-05-20) is the Apache 2.0 successor: a 218B total / 25B active MoE covering 48 languages, with vision input, reasoning and translation in one model that runs on 2x H100 (or 1x B200), priced at $2.50 / $10 per 1M tokens on Cohere's API. The original 111B Command A (256K context, 23 languages) keeps CC-BY-NC 4.0 weights, so commercial self-hosting of that model requires a Cohere enterprise contract
LongCat-2.0 (Meituan)
Meituan's open-source 1.6T-parameter MoE (~48B active) with native 1M-token context, MIT license -- trained entirely on domestic Chinese AI ASICs and revealed as the stealth 'Owl Alpha' model that had been topping OpenRouter
Inkling (Thinking Machines Lab)
**Nov 5: Thinking Machines introduced Inkling 2 Small, its second model generation -- a 594B-total / 17B-active MoE with image input and variable effort that the lab says scores 31 on the Artificial Analysis Intelligence Index. It is on Tinker now with closed weights at $0.30 / $1.00 per 1M input / output tokens for serverless inference, and the lab intends to publish Apache 2.0 weights next week after external safety testing.** **Inkling-Small (276B total / 12B active) was released as open weights on 2026-07-30 at $1.20 per 1M output tokens versus $4.05 for Inkling, and on 2026-11-04 Cognition shipped SWE-2 Flash, a Devin coding model post-trained from it.** Mira Murati's $12B lab ships its first model (2026-07-15): a 975B/41B-active open-weights MoE that reasons natively over text, images, and audio with a 1M-token context -- positioned not as the strongest model, but as the best starting point for fine-tuning via Tinker
Bonsai 27B (PrismML)
The first 27B-class model that runs on a phone (2026-07-14) -- ternary and 1-bit quantizations of Qwen3.6 27B squeeze a multimodal, tool-calling, 262K-context model into 3.9-5.9GB under Apache 2.0