whatmodel.sh / Devstral Medium 1.0 (2507)

Devstral Medium 1.0 (2507)

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Devstral Medium 1.0 (2507) is a closed-weights language model from Mistral, released July 2025.

Catalogue identity notes

Comparison-only. Arena entry 'devstral-medium-2507' (webdev). Released 2025-07: Mistral model card dated July 10, 2025. Mistral card status 'Deprecated' since 2/27/2026; with the 6-month GA notice elapsed no current API is asserted.

54.2±2.5comparison only: no current run option verifiedfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

$ how it compares

The LLMs ranked either side of Devstral Medium 1.0 (2507), out of 1132 with a score, then more from Mistral.

#modelscorewhereRAMreleased
446InternLM3 8B Instruct Shanghai AI Laboratory (InternLM)54±11local~6.8GB2025-01
447Nova Premier Amazon54±2api $5.00/M2025-04
448Devstral Medium 1.0 (2507) Mistral54±3comparison only2025-07
449Mercury Inception54±3comparison only
450Nanbeige4-3B-Thinking-2511 Nanbeige54±3local~3.7GB2025-11
more from Mistral
88Mistral Large 4 (public preview) Mistral71±2api $2.06/M2026-10
204Mistral Medium 3.5 (128B) Mistral65±1api $3.00/M +local~42GB2026-04
212Mistral Medium 3.1 (2508) Mistral64±2comparison only2025-08

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▅▇█▇▅▄▂▁ #315 of 549 * · 97% of its score · 1 test

Beats Qwen3-VL 32B Instruct, Solar Open 100B (102B-A12B), GLM-4.7-Flash here, though they rank higher overall. Trails DeepSeek LLM 67B Chat, QwQ-32B-Preview, InternLM2.5 20B Chat here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena WebDev (Code Arena)1075.8 ✎▃▂▄▃▅█▄▇▆▄▁▁ beats 0/119avg 981▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%sharp

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 97%

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
LMArena leaderboard dataset1▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 97%CC BY 4.0
prior · pull toward the middle▇ 3%
weighted average= 54.2

$ 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