[whatmodel.sh](../../README.md) / Magistral Medium 1.1 (2507) # Magistral Medium 1.1 (2507) Mistral · released 2025-07 · proprietary **score 55.1** ±1.4 · comparison only: no current run option verified · from 4 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Magistral Medium 1.1 (2507) among every model on that task or test, worst to best. 
task    vs the others          rank  counts  tests
coding  ▃●······▃·▃▃       #5 of 7*     10%      1
math    ▁▁▃▆█●▅█▃▁▁▁   #251 of 530*     57%      2
coding · #5 of 7 · 10% of its score Beats [Magistral Small 1.0 (2506)](magistral-small-2506.md) here, though they rank higher overall. 
test                                result  vs the others in its run  difficulty        counts  why
LiveCodeBench v5 (window unspecif…   59.4%  ▄●▄······▄·▄ beats 2/5    medium · avg 50%     10%  at its level
math · #251 of 530 · 57% of its score Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Tulu 2 DPO 70B](tulu-2-dpo-70b.md) here, though they rank lower overall. 
test               result  vs the others in its run  difficulty        counts  why
AIME 2025 (maker)   61.0%  ●▃·····▃···▃ beats 0/5    medium · avg 48%     33%  sharp · at its level
AIME 2024 (maker)   72.0%  ●▃·····▃···▃ beats 2/5    medium · avg 60%     24%  sharp · at its level
other tests (1) 
test                  result  vs the others in its run  difficulty        counts  why
GPQA Diamond (maker)   71.5%  █·█·██●····█ beats 4/5    medium · avg 52%     32%  sharp · 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
mistralai/Magistral-Small-2509 model ca…        4   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 55.1
Licences: mistralai/Magistral-Small-2509 model card ([Apache-2.0](../../licenses/mistralai--Magistral-Small-2509.txt)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/magistral-medium-2507) · [JSON](https://whatmodel.sh/api?format=json&all=1)