Mistral AI launched a public preview of Mistral Large 4 on October 6, 2026: a natively multimodal mixture-of-experts with about 1 trillion parameters and 49 billion active, which Mistral calls its largest and most capable model to date. Its in-house nickname is le Chonk. The preview API is live in Mistral Studio, and in Mistral’s words, “Weights drop end of this month.” This page reads Mistral’s announcement (12:00 UTC), its model page, its API pricing page and its post on X (13:06 UTC), as of October 6, 2026.
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Mistral launched a public preview of Mistral Large 4 on October 6, 2026: a natively multimodal mixture-of-experts with about 1 trillion parameters and 49 billion active.
The preview API is live in Mistral Studio at $0.68 per million input tokens and $2.09 per million output; Mistral says the weights come by the end of October.
On Mistral’s numbers it scores 61.7% on DeepSWE v1.1 and 59.9% on AutomationBench, with a Coding Agent Index of 49.8%, ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max.
Mistral’s headline claim is security: 93% on Cybench and 82% on a reproduce-and-patch test where, it says, Claude Opus 5.5 and GPT-6 Astra score near zero because they refuse.
It was trained from scratch on 3,800 Nvidia Grace Blackwell GPUs in Mistral’s own European datacenters, with training data in more than 160 languages.
Mistral’s model page lists the license only as Open, and on DeepSWE v1.1, Kimi K3 and DeepSeek V4.1 Flash post higher published scores than Large 4’s 61.7%.
§ 01What Mistral launched
| Item | Mistral Large 4 |
|---|---|
| Status | Public preview, API only |
| Architecture | Granular mixture-of-experts, hybrid instruct and reasoning |
| Total parameters | About 1 trillion (1.05 trillion on the model page) |
| Active parameters | 49 billion |
| Vision encoder | 1.6 billion parameters (text and image input) |
| Context window | 1 million tokens |
| Languages in training data | More than 160 |
| Model id | mistral-large-4 |
| API features | Structured outputs, function calling, document Q&A, batch, agents and built-in tools |
| Price (Standard tier) | $0.68 input, $0.07 cached input, $2.09 output per million tokens |
| Weights | By the end of October 2026 |
| License | Listed as Open on the model page; terms not yet named |
Mistral frames the release as Europe’s open-weight answer to the Chinese labs. It says Large 4 is competitive with the strongest open models in the world while “significantly outperforming any open-weight model developed in the US or Europe.” Mistral Large 3, the model it replaces at the top of the catalog, shipped under Apache 2.0. The Large 4 model page says only Open, so the license terms are one of the things to read when the weights land.
§ 02What it costs
Mistral prices the preview above Large 3 but well below its own Medium 3.5. The rival rows below are host list prices on OpenRouter, not the labs’ own price sheets, so read them as a guide to what developers pay today rather than a vendor quote.
| Model | Source | Input | Cached input | Output |
|---|---|---|---|---|
| Mistral Large 4 | Mistral | 0.68 | 0.07 | 2.09 |
| Mistral Large 3 | Mistral | 0.50 | 0.05 | 1.50 |
| Mistral Medium 3.5 | Mistral | 1.50 | 0.15 | 7.50 |
| DeepSeek V4 Pro 0813 | OpenRouter | 0.66 | not listed | 1.98 |
| Qwen3.8 Max 0902 | OpenRouter | 2.00 | not listed | 6.00 |
| Kimi K3 | OpenRouter | 0.95 | not listed | 14.00 |
Read plainly: an output token on Large 4 costs about 39% more than on Large 3 and about 28% of what Medium 3.5 charges, and the preview sits within a few cents of DeepSeek V4 Pro 0813’s OpenRouter listing. One caution: Mistral’s model page shows $1.36 input and $4.18 output beside the Standard figures, exactly double, and the pages we read do not say whether today’s price is a preview rate. Large 4 was not yet listed on OpenRouter when we checked at 13:26 UTC.
§ 03The benchmarks Mistral published
Every number in this section is Mistral’s. Mistral says some rows come from outside evaluators: the coding rows use numbers reported by Artificial Analysis, the legal and finance comparisons come from vals.ai, and the coding-quality ratings come from a blind study run with Surge AI. We had not yet seen those evaluators’ own pages for the preview when we wrote this.
| Benchmark | Mistral Large 4 | What Mistral says around it |
|---|---|---|
| DeepSWE v1.1 | 61.7% | Artificial Analysis number |
| SWE-Atlas-QnA | 59.4% | Artificial Analysis number |
| Terminal-Bench 4 | 28.3% | Artificial Analysis number |
| Coding Agent Index | 49.8% | Ahead of DeepSeek V4 Pro 0813 and Qwen3.8 Max |
| AutomationBench (657 workflows) | 59.9% | Ahead of Kimi K3, MiMo-V2.6-Pro and DeepSeek V4 Pro |
| AA-Briefcase | 1,393 Elo | Ahead of DeepSeek V4 Pro |
| Dense 200 (visual grounding) | 42% | GPT-6 Astra scores 41% |
| Cybench (40 challenges) | 93% | One of the highest open-weight scores reported |
| Reproduce-and-patch test, AA Cyber Index | 82% | Highest of any model |
| Lakera B3 attack resistance | 93.3% | No higher score seen among competitors |
| KORA Benchmark | 1.691 of 2 | Highest Mistral measured among open models |
| Surge AI blind coding rating | 3.74 of 5 | Second of five; Claude Opus 5 led at 4.22 |
In the post’s text Mistral also says Large 4 ranks among the top five models overall on the Artificial Analysis Cyber Index and beats GPT-6 Astra on vals.ai’s legal and finance tasks, without giving those numbers in the text.
§ 04Where it sits against other open models
Reflection AI published its own table a day earlier for its Beam model, with competitors’ scores it says came from Artificial Analysis and DataCurve. Setting Large 4’s numbers beside those rows shows where Mistral’s claim lands. These are two companies’ tables, not one test run.
| Model | DeepSWE v1.1 | AutomationBench | Source |
|---|---|---|---|
| DeepSeek V4.1 Flash | 74.2 | 54.8 | Reflection |
| Kimi K3 | 68.0 | 46.7 | Reflection |
| Mistral Large 4 | 61.7 | 59.9 | Mistral |
| GLM 5.3 | 61.0 | 48.2 | Reflection |
| Qwen 3.8 Max | 51.0 | 39.8 | Reflection |
| Reflection Beam | 44.4 | 37.0 | Reflection |
On DeepSWE, Large 4 lands level with GLM 5.3 and behind Kimi K3 and DeepSeek V4.1 Flash, both from Chinese labs. It is well ahead of Beam, the open model a US lab announced the day before. On AutomationBench it posts the highest number in this set, but Reflection labels its column the public split while Mistral describes 657 workflows, so treat that row as indicative. The picture matches Mistral’s own wording: the strongest open model from the US or Europe by its numbers, competitive rather than leading worldwide.
§ 05The cybersecurity pitch
Mistral leans hardest on security. Its argument is that defenders need a model that will do vulnerability work under their own policy: “Several leading closed models, including Claude Opus 5.5 and GPT-6 Astra, score near zero on the same test because they refuse to perform the task.” That 82% against near zero is a refusal gap, not a skill comparison, since the closed models were never scored on the work itself.
Two more details sit beside it. Until the weights ship, Mistral says it is red-teaming with cybersecurity leaders, vetted partners and state authorities “who will access the same model with reduced moderation and expanded cyber capabilities.” And its safety section reports the other edge: on malicious cyber prompts from JailbreakBench, StrongREJECT and AgentHarm, Large 4’s average refusal rate is higher than every open model Mistral compared. Mistral’s pitch, in short, is a model that does defensive work and turns down attacks, run on private cloud or on premises under the customer’s own rules.
§ 06How it was built
Mistral trained Large 4 from scratch on 3,800 Nvidia Grace Blackwell GPUs in its own datacenters in Europe, and serves the preview on the same hardware. It says a significant share of the training data was multilingual, across more than 160 languages.
Post-training is reinforcement learning at scale. At about 3,000 GPUs, Mistral says one training run produces roughly 33 billion tokens a day, about 16 billion of them trainable completions after filtering. It also says “The reinforcement learning run behind this preview is still in flight, and the model is showing no signs of saturation,” so the model behind the preview API will keep changing. Large 4 is the first milestone funded by the €3 billion Series D Mistral announced on September 8, and the base for a new set of specialized Mistral models.
§ 07What this means if you run agents
For teams that must keep data in Europe, Large 4 is Mistral’s largest open-weight model yet, and Mistral says it runs a European deployment end to end under European law. For everyone else it adds another strong open option beside DeepSeek, Kimi, GLM and Qwen, at a price close to DeepSeek V4 Pro. Two practical notes. A mixture-of-experts model still has to hold all of its weights in memory to serve, so 49 billion active parameters cut compute per token, not the hardware needed to load about a trillion parameters. And a preview is a moving target: the training run is still going, the price may be a preview rate, and the license is unnamed.
CellCog’s AI employees run on Claude Opus 5.5 at every tier, not on Large 4. We track open-weight releases because they set the floor on price and are the option for teams that must run models on their own hardware.
§ 08What we are watching
- The weights and the license name, which Mistral says arrive by the end of October.
- The architecture and post-training details Mistral promised with the weights.
- Outside evaluations, starting with the Artificial Analysis pages behind the coding and cyber numbers.
- Hosts and price: when Large 4 reaches OpenRouter and the clouds, and whether $0.68 and $2.09 hold after the preview.
§ 09Sources
- Mistral AI, “Introducing Mistral Large 4”, October 6, 2026
- Mistral AI docs, Mistral Large 4 model page, read October 6, 2026
- Mistral AI, API pricing, read October 6, 2026
- Mistral AI on X, launch post, October 6, 2026
- Reflection AI, “Introducing Beam”, October 5, 2026
- OpenRouter, model list and prices, read October 6, 2026
Q1Is Mistral Large 4 open source?
It is announced as an open-weight model, but on October 6, 2026 only the preview API was available. Mistral says it will release the weights by the end of the month, with more on the architecture and post-training. Its model page lists the license as Open without naming the terms; Mistral Large 3 shipped under Apache 2.0.
Q2How big is Mistral Large 4?
About 1 trillion total parameters (1.05 trillion on Mistral’s model page) with 49 billion active per token, plus a 1.6 billion parameter vision encoder and a 1 million token context window.
Q3How much does Mistral Large 4 cost?
$0.68 per million input tokens, $0.07 cached and $2.09 output on Mistral’s Standard tier. The model page also shows $1.36 and $4.18, exactly double, and the pages we read do not say whether the lower figure is a preview rate.
Q4How does it compare with Kimi K3 and DeepSeek?
On Mistral’s numbers it beats Kimi K3 and DeepSeek V4 Pro on AutomationBench and leads DeepSeek V4 Pro 0813 on the Coding Agent Index. On DeepSWE v1.1, Reflection AI’s published table puts Kimi K3 at 68.0 and DeepSeek V4.1 Flash at 74.2, above Large 4’s 61.7.
Q5Does CellCog use Mistral Large 4?
No. CellCog’s AI employees run on Claude Opus 5.5 at every tier. We track open-weight models like Large 4 because they set the floor on price and are the option for teams that must run models on their own hardware.