[whatmodel.sh](../../README.md) / Qwen3.6 Max Preview # Qwen3.6 Max Preview Alibaba · released 2026-04 · proprietary **score 71.2** ±1.1 · #67 of 732 · from 8 benchmark results across 2 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Qwen3.6 Max Preview among every model on that task or test, worst to best.}

task            vs the others         rank  counts  tests
agent tasks     ▁▁▃▆█▄▅▅●▄▂▁   #74 of 313*     23%      1
coding          ▁▁▁▃▅▇█▇▅●▃▁   #59 of 469*     46%      2
chat & writing  ▁▁▁▃▆▄▆█▆▇●▃   #44 of 402*     12%      1
math            ▁▁▃▅█▆▄█●▂▁▁   #68 of 431*    3.2%      1

agent tasks · #74 of 313 · 23% of its score

Beats [GPT-5 Pro](gpt-5-pro.md), [GPT-5](gpt-5.md) here, though they rank higher overall. Trails [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md), [MiniMax-M3](minimax-m3.md), [Qwen3.5 397B-A17B](qwen3-5-397b-a17b.md) here, though they rank lower overall.

test                result  vs the others in its run  difficulty      counts  why
SWE-bench Verified   76.7%  ▁··▁·▁▂▁▄●▄▁ beats 20/29  hard · avg 19%     23%  sharp

coding · #59 of 469 · 46% of its score

Beats [GPT-5.4](gpt-5-4.md), [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall. Trails [GLM-5.1](glm-5-1.md), [Qwen3.7 Plus](qwen3-7-plus.md), [Claude Sonnet 4.6](claude-sonnet-4-6.md) here, though they rank lower overall.

test                       result  vs the others in its run   difficulty      counts  why
SWE-bench Verified          76.7%  ▁··▁·▁▂▁▄●▄▁ beats 20/29   hard · avg 19%     23%  sharp
Arena WebDev (Code Arena)  1481.8  ▁▁▃▂▅█●▆▅▅▁▂ beats 69/129  avg 981            22%  sharp

chat & writing · #44 of 402 · 12% of its score

Beats [GPT-6 Sol](gpt-6-sol.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [Claude Haiku 5.5](claude-haiku-5-5.md) here, though they rank higher overall. Trails [MiMo-V2.5-Pro (1T-A42B)](mimo-v2-5-pro.md), [GLM-5.1](glm-5-1.md), [Grok 4.20 (0309 reasoning)](grok-4-20.md) here, though they rank lower overall.

test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1460.2  ▁▁▁▂▃▃▃▆▆▇●▃ beats 334/385  avg 1260       12%  blunt

math · #68 of 431 · 3.2% of its score

Trails [INTELLECT-3](intellect-3.md), [GPT-5 mini](gpt-5-mini.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md) here, though they rank lower overall.

test                      result  vs the others in its run    difficulty            counts  why
OTIS mock AIME 2024–2025   91.1%  ▃▃▄▃▃▃▇▇●▃▂▅ beats 145/197  very hard · avg 7.2%    3.2%  sharp · noisy

other tests (4)

test                          result  vs the others in its run    difficulty            counts  why
Mystery Game Puzzles (Epoch)   19.0%  █··▂▃●▄█▂▄▂▁ beats 31/77    very hard · avg 9.4%     13%  sharp · noisy
GPQA Diamond                   87.4%  ▁▁▁▁▂▄▆▄▅▇●▅ beats 156/211  hard · avg 39%           12%  sharp
Chess Puzzles (Epoch)          20.0%  █▁▁▁▁▂●▃▃▂▁▁ beats 77/137   very hard · avg 5.8%    9.1%  sharp · noisy
SimpleQA Verified              52.0%  ▁▁▃▂▃▃▆▆●▄▂▅ beats 58/81    very hard · avg 8.0%    4.7%  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
Epoch AI                             6   65% ▇▇▇▇▇▇▇▇
LMArena leaderboard dataset          2   34% ▇▇▇▇
prior: pull toward the middle             1% ▇
weighted average                        = 71.2

_{Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))} ## How to run it ### Through an API api (Alibaba Cloud Model Studio) · $2.92/M blended · $1.3 in / $7.8 out per 1M tokens · [alibabacloud.com](https://www.alibabacloud.com/help/en/model-studio/text-generation-model) _{[price source](https://www.alibabacloud.com/help/en/model-studio/model-pricing): USD per 1M tokens, Singapore (International), qwen3.6-max-preview, 0\

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