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Mistral Large 4

Mistral Large 4
2 min read
Mistral Large 4

Mistral Large 4, which Mistral AI put into public preview on October 6, 2026, is Mistral's largest model yet, a trillion-parameter multimodal mixture-of-experts with open weights promised for the end of the month.

  • Model: Mistral Large 4 (mistral-large-4-0)

  • Maker: Mistral AI

  • Ship date: October 6, 2026 (public preview)

  • Pricing: $1.36 / $4.18 per M tokens ($0.68 / $2.09 at the 50% launch discount)

  • Context: 1M tokens per Mistral (AA lists 524K)

  • Where you can use it:

  • When to reach for it:

    • Defensive security work others refuse

    • Legal, finance and visual grounding tasks

    • Skip it for terminal-heavy coding

  • How it compares:

    • Trails GLM-5.3 and Kimi K3 on broad indexes

    • Best US or European open model, says Mistral

  • What makes it different:

    • Open weights promised, built in Europe

    • Made for sovereign, self-hosted use

  • Early reactions: Hacker News split between praise for its cyber and vision results and complaints that its broad scores trail Chinese open models, and VentureBeat noted several rival scores in Mistral's charts couldn't be checked publicly.

  • Read more: Introducing Mistral Large 4, model page

The benchmarks

Mistral's preview numbers, many run by Artificial Analysis and Vals AI before public listing; rivals are mostly Chinese open-weight models, with GPT-6 Astra or Claude Opus 5.5 added mainly where ML4 wins, coding rivals ran in their own agent harnesses, and Mistral says the RL run was still going.

Mistral's numbers

  • Harvey's Legal Agent Benchmark (legal agent tasks): 15.8%. +2.9 over Kimi K3 (12.9%), +5.4 over Qwen3.8 Max (10.4%), +7.5 over GLM-5.3 (8.3%), +10.4 over GPT-6 Astra (5.4%).

  • Artificial Analysis Cyber Index (find and fix flaws): 50. +0.0 over GLM-5.3-Flash (50), +9 over Kimi K3 (41), +14 over GLM-5.3 (36), +17 over GPT-6 Astra (33), +21 over Claude Opus 5.5 (29).

  • CyberGym-E2E (reproduce and patch bugs): 82%. +3 over MiMo-V2.6-Pro (79%), +8 over Grok 4.7 (74%), +24 over Kimi K3 (58%), +53 over GLM-5.3 (29%).

  • DeepSWE v1.1 (agentic coding): 61.7%. +0.7 over GLM-5.3 (61%), +4.7 over DeepSeek V4 Pro 0813 (57%), −6.3 under Kimi K3 (68%).

  • Terminal-Bench 4.0 (agentic terminal tasks): 28.3%. +7.3 over Kimi K3 (21%), +18.3 over DeepSeek V4 Pro 0813 (10%), −11.7 under GLM-5.3 (40%).

  • AutomationBench (business app workflows): 59.9%. +1.6 over Kimi K3 (58.3%), +3.2 over DeepSeek V4 Pro 0813 (56.7%), −2.3 under GLM-5.3 (62.2%).

  • Dense200 (bounding box grounding): 42.0%. +0.5 over GPT-6 Astra (41.5%), +13.1 over Kimi K3 (28.9%).

  • Finance Agent v2 (financial research agent): 54.7%. +1.2 over GPT-6 Astra (53.5%), +1.6 over Kimi K3 (53.1%), −1.1 under GLM-5.3 (55.8%).

Independent numbers

  • Artificial Analysis Intelligence Index (current v4.3.2): 38.4. +2.4 over DeepSeek V4 Pro 0813 (36.0), −5.2 under Kimi K3 (43.6), −6.4 under GLM-5.3 (44.8).

  • Vals Index (finance, legal, coding mix): 48.05%. +0.42 over DeepSeek V4 Pro 0813 (47.63%), −2.25 under Kimi K3 (50.30%), −5.46 under GLM-5.3 (53.51%).

  • Terminal-Bench 4.0 (Vals AI run, Mini-SWE-agent): 22.73%. +8.59 over DeepSeek V4 Pro 0813 (14.14%), +5.56 over Kimi K3 (17.17%), −16.16 under GLM-5.3 (38.89%).

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