[whatmodel.sh](../../README.md) / Qwen3.8 Max (0902)
# Qwen3.8 Max (0902)
Alibaba · released 2026-09 · closed weights
**score 74.4** ±0.8 · #39 of 732 · from 10 benchmark results across 4 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen3.8 Max (0902) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #35 of 313* 8.7% 2
coding ▁▁▁▃▅▇█▇▅▄●▁ #11 of 469* 11% 1
math ▁▁▃▅█▆▄█▄●▁▁ #28 of 431 56% 3
agent tasks · #35 of 313 · 8.7% of its score
Beats [GPT-5.6 Terra](gpt-5-6-terra.md), [GPT-5.6 Luna](gpt-5-6-luna.md), [Grok 4.6](grok-4-6.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md), [Grok 4.5](grok-4-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Terminal-Bench 4.0 27.0% █▅▅▅●·▅▃▃▅▅▅ beats 10/20 very hard · avg 0.5% 6.1% sharp
Video2World V2WScore (coding agen… 26.6% ●··▄█·▄····▄ beats 1/6 very hard · avg 7.3% 2.6% at its level
coding · #11 of 469 · 11% of its score
Beats [GPT-6.1 Terra](gpt-6-1-terra.md), [GPT-5.6 Sol](gpt-5-6-sol.md), [Claude Fable 5](claude-fable-5.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1675.9 ▁▁▃▂▅█▃▆▅●▁▂ beats 118/129 avg 981 11% sharp
math · #28 of 431 · 56% of its score
Beats [Gemini 3.8 Flash](gemini-3-8-flash.md), [DeepSeek V4.1 Flash](deepseek-v4-1-flash.md), [Grok 4.6](grok-4-6.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 65.6% ▂··▁▁▃▄▄●▆▄▃ beats 51/86 very hard · avg 1.2% 28% sharp · at its level
FrontierMath tier 4 v2 34.1% ▁▂▁▂█●▂▃▂▁▁▁ beats 40/67 very hard · avg 0.1% 27% sharp · at its level
OTIS mock AIME 2024–2025 100.0% ▃▃▄▃▃▃▇▇█▃▂● beats 192/197 very hard · avg 7.2% 0.9% sharp · maxed out · noisy
other tests (4)
test result vs the others in its run difficulty counts why
Furniture Assembly (Epoch) 20.0% ●▆▆▆▆▂▆▂▂▂▂▅ beats 0/32 very hard · avg 1.2% 11% sharp · at its level
Chess Puzzles (Epoch) 40.0% █▁▁▁▁▂▃▃●▂▁▁ beats 119/137 very hard · avg 5.8% 6.1% sharp · at its level · noisy
GPQA Diamond 92.3% ▁▁▁▁▂▄▆▄▅▇█● beats 189/211 hard · avg 39% 4.3% sharp
SimpleQA Verified 47.3% ▁▁▃▂▃▃▆▆●▄▂▅ beats 49/81 very hard · avg 8.0% 2.5% at its level
source results share of the score
Epoch AI 7 80% ▇▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 11% ▇
Terminal-Bench 4.0 official leaderboard… 1 6% ▇
Video2World: Benchmarking Coding Agents… 1 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 74.4
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/)) · Terminal-Bench 4.0 official leaderboard submissions ([Apache-2.0](../../licenses/terminal-bench-leaderboard-submissions-Apache-2.0.txt)) · Video2World: Benchmarking Coding Agents for Interactive World Modeling from Embodied Videos ([CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/))
## How to run it
### Through an API
api (Alibaba Cloud Model Studio) · $3.00/M blended · $2 in / $6 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), dated snapshot qwen3.8-max-0902, thinking and non-thinking modes, 0\
[interactive version](https://whatmodel.sh/model/qwen3-8-max-0902) · [JSON](https://whatmodel.sh/api?format=json&all=1)