whatmodel.sh / Magistral Medium 1.2 (2509)

Magistral Medium 1.2 (2509)

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Magistral Medium 1.2 (2509) is a closed-weights language model from Mistral, released September 2025. Licence: proprietary.

Catalogue identity notes

Comparison-only. Mistral docs: 'September 18, 2025 ... Deprecated ... v 25.09 ... magistral-medium-2509 ... Context 128k ... This model is deprecated. Use Mistral Medium 3.5 for new integrations. Deprecation date 5/22/2026'. API kept null (retired or deprecated in favour of Mistral Medium 3.5). Rows: Mistral's own Magistral-Small-2509 card table, row 'Magistral Medium 1.2'.

Model information sources

Release date ↗

62.6±1.8comparison only: no current run option verifiedfrom 4 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Magistral Medium 1.2 (2509), out of 828 with a score, then more from Mistral.

#modelscorewhereRAMreleased
198GPT-4.5 Preview OpenAI63±1comparison only2025-02
199Trinity-Large-Thinking Arcee AI63±2api $0.39/M +local~141GB2026-04
200Magistral Medium 1.2 (2509) Mistral63±2comparison only2025-09
201MiniMax-M1 (80k) MiniMax62±1api $0.96/M +local~959GB2025-06
202Gemma 4 12B Google62±1local~8.5GB2026-06
more from Mistral
79Mistral Large 4 (public preview) Mistral71±2api $2.06/M2026-10
174Mistral Medium 3.5 (128B) Mistral65±1api $3.00/M +local~42GB2026-04
180Mistral Medium 3.1 (2508) Mistral64±2comparison only2025-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▃█······▃·▃▃ #3 of 7 * · 13% of its score · 1 test

Trails Magistral Small 1.2, Mistral Small 4 (119B-A6.5B) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LiveCodeBench v5 (window unspecified) lab75.0% ✎▄█▄······▄·▄ beats 5/5medium · avg 50%▇▇▇▇▇ 13%at its level
math▁▂▃▅▇▅▅█▄▁▁▁ #154 of 462 * · 54% of its score · 2 tests

Beats Claude Sonnet 4.6, MiMo-V2.5 (310B-A15B), MiniMax-M2.7 here, though they rank higher overall. Trails Nemotron 3 Nano 30B-A3B, Olmo 3 32B Think, Olmo 3.1 32B Think here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AIME 2025 lab83.5% ✎█▃·····▃···▃ beats 5/5medium · avg 49%▇▇▇▇▇▇▇▇▇▇▇▇ 31%sharp
AIME 2024 lab91.8% ✎█▃·····▃···▃ beats 5/5medium · avg 60%▇▇▇▇▇▇▇▇▇ 23%sharp
other tests1 not in a task · 32% of its score
testresultvs the others in its rundifficultycountswhy
GPQA Diamond lab76.3% ✎█·█·███····█ beats 5/5medium · avg 53%▇▇▇▇▇▇▇▇▇▇▇▇▇ 32%sharp

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · mistralai/Magistral-Small-250… 99%

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The 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's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
mistralai/Magistral-Small-2509 model card · lab-reported4▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 62.6

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json