whatmodel.sh / Qwen-VL-Max (2025-08-13)

Qwen-VL-Max (2025-08-13)

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Qwen-VL-Max (2025-08-13) is a closed-weights language model from Alibaba, released August 2025.

Catalogue identity notes

Comparison-only. Arena entry 'qwen-vl-max-2025-08-13' (vision). Released 2025-08: date in Alibaba's snapshot id qwen-vl-max-2025-08-13. Exact dated snapshot; the snapshot id was decommissioned 2026-05-13 (the undated qwen-vl-max alias is not asserted to be this checkpoint).

63.3±3.9comparison 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 Qwen-VL-Max (2025-08-13), out of 1132 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
219MiniCPM5 2B OpenBMB64±1local~3GB2026-09
220Grok 3 Beta SpaceXAI (xAI)63±1comparison only2025-04
221Qwen-VL-Max (2025-08-13) Alibaba63±4comparison only2025-08
222Qwen3-Next 80B-A3B Thinking Alibaba63±1api $0.41/M +local~27GB2025-09
223DeepSeek R1 (Jan '25) DeepSeek63±1api $4.00/M +local~110GB2025-01
more from Alibaba
18Qwen3.8 Max (2.4T-A95B) Alibaba78±1api $3.00/M +local~380GB2026-08
34Qwen3.8 Max (0902) Alibaba76±1api $3.00/M2026-09
37Qwen3.8-Flash-Next Alibaba75±1local~59GB2026-08

see all LLMs · find the ones that fit your machine

$ what it's good at

vision & documents▁▁▃▃▂▃▅▅▇▇█▃ #103 of 199 * · 93% of its score · 1 test

Beats DeepSeek V4 Flash Vision (Exp) here, though they rank higher overall. Trails GPT-4.1 mini, Qwen3.5 4B, Gemini 2.5 Flash here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
Arena vision (style controlled)1184.9 ✎▁▁▂▂▂▂▄▅▅▆██ beats 63/145avg 1068▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%blunt

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · LMArena leaderboard dataset 93%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 93%CC BY 4.0
prior · pull toward the middle▇▇ 7%
weighted average= 63.3

$ improve this page

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data updated · sources and licences · json