whatmodel.sh / MiniMax M3.1 Flash

MiniMax M3.1 Flash

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

MiniMax M3.1 Flash is a closed-weights language model from MiniMax, released October 2026. Licence: proprietary.

Model information sources

Release date ↗

75.4±2.3#32 of 957 LLMsfrom 1 benchmark result across 1 source* thinly tested, treat the rank as provisional

Score within the LMArena (text) comparison set only.

$ how it compares

The LLMs ranked either side of MiniMax M3.1 Flash, out of 1188 with a score, then more from MiniMax.

#modelscorewhereRAMreleased
30Kimi K3 Moonshot AI76±1api $6.00/M +local~884GB2026-07
31GPT-6 Luna OpenAI75±1api $0.20/M2026-09
32MiniMax M3.1 Flash MiniMax75±2api $0.26/M2026-10
33GPT-5.6 Luna OpenAI75±1api $0.45/M2026-07
34Grok 4.6 SpaceXAI (xAI)75±1api $3.00/M2026-08
more from MiniMax
105MiniMax-M3 MiniMax69±1api $0.48/M +local~131GB2026-06
147MiniMax-M2.7 MiniMax67±1api $0.53/M +local~75GB2026-03
190MiniMax-M2.5 MiniMax65±1api $0.53/M +local~75GB2026-02

see all LLMs · find the ones that fit your machine

$ what it's good at

coding▁▁▁▃▆▇█▇▅▄▃▁ #33 of 478 * · 98% of its score · 1 test

Beats GPT-5.5, GPT-5.6 Terra, Claude Opus 4.8 here, though they rank higher overall. Trails GLM-5.2, GLM-5.3-Flash, Grok 4.7 here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena WebDev (Code Arena)1587.5 ✎▁▁▃▃▆█▄▇▅▅▁▁ beats 97/126avg 978▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 98%sharp

$ where to run it

Through an API

api · $0.26/M · $0.15 in / $0.6 out per 1M tokens · 1M context · platform.minimax.io ↗ · price sourceUSD per 1M input/output tokens for MiniMax-M3.1-Flash. Cache-read discount excluded. Observed 2026-10-21.

$ how the score was worked out · LMArena leaderboard dataset 98%

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 dataset1CC BY 4.0
prior · pull toward the middle
weighted average= 75.4

$ 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