[whatmodel.sh](../../README.md) / Mistral Large 4 (public preview) # Mistral Large 4 (public preview) Mistral · released 2026-10 · 1050B params (52B active) · proprietary **score 71.2** ±1.2 · comparison only: no current run option verified · from 6 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Mistral Large 4 (public preview) among every model on that task or test, worst to best.}

task                vs the others         rank  counts  tests
agent tasks         ▁▁▂▆█▄▅▄●▃▁▁   #59 of 354*     36%      1
coding              ▁▁▁▃▄▆█▇▅●▃▁   #44 of 539*     25%      1
chat & writing      ▁▁▁▃▅▄▇█▇▇●▃   #90 of 474*     14%      1
vision & documents  ▁▁▂▃▂▃▅▄▆●█▄   #50 of 189*    9.3%      1

agent tasks · #59 of 354 · 36% of its score

Beats [Muse Spark 1.1](muse-spark-1-1.md), [Muse Spark 1.2](muse-spark-1-2.md), [Qwen3.7 Max](qwen3-7-max.md) here, though they rank higher overall. Trails [Beam 1.0 501B A23B](beam-1-0-501b-a23b.md), [Claude Sonnet 4.6](claude-sonnet-4-6.md) here, though they rank lower overall.

test                                     result  vs the others in its run  difficulty      counts  why
Arena Agent (October 2026 aggrega…  -0.0602 IPS  ▁▁▁▃●▂█▁▂▂▁▁ beats 13/59  avg -0.379 IPS     36%  sharp

coding · #44 of 539 · 25% of its score

Beats [GPT-5.5](gpt-5-5.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [GPT-5.4](gpt-5-4.md) here, though they rank higher overall.

test                       result  vs the others in its run   difficulty  counts  why
Arena WebDev (Code Arena)  1544.3  ▁▁▃▂▅█▃●▅▅▁▂ beats 86/129  avg 981        25%  sharp

chat & writing · #90 of 474 · 14% of its score

Beats [Claude Haiku 5.5](claude-haiku-5-5.md), [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md) here, though they rank lower overall.

test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1432.0  ▁▁▁▂▃▃▃▆▆▇●▃ beats 289/385  avg 1260       14%  blunt

vision & documents · #50 of 189 · 9.3% of its score

Beats [MiMo-V2.6-Pro](mimo-v2-6-pro.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank higher overall. Trails [Qwen3.6 Flash](qwen3-6-flash.md), [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md), [Kimi K2.5](kimi-k2-5.md) here, though they rank lower overall.

test                             result  vs the others in its run    difficulty  counts  why
Arena vision (style controlled)  1251.9  ▁▁▂▂▂▂▃▄▅▅●▇ beats 109/152  avg 1061      9.3%  blunt

other tests (2)

test                   result  vs the others in its run    difficulty            counts  why
Chess Puzzles (Epoch)   33.0%  █▁▁▁▁▂▃▃●▂▁▁ beats 109/137  very hard · avg 5.8%     10%  sharp · noisy
SimpleQA Verified       20.0%  ▁▁▃▂●▃▆▆█▄▂▅ beats 12/81    very hard · avg 8.0%    5.3%  at its level

## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md)

source                         results  share of the score
LMArena leaderboard dataset          4   84% ▇▇▇▇▇▇▇▇▇▇
Epoch AI                             2   16% ▇▇
prior: pull toward the middle             1% ▇
weighted average                        = 71.2

_{Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/mistral-large-4) · [JSON](https://whatmodel.sh/api?format=json&all=1)