[whatmodel.sh](../../README.md) / Qwen2.5-Max (2025-01-25) # Qwen2.5-Max (2025-01-25) Alibaba · released 2025-01 · proprietary **score 57.4** ±1.1 · comparison only: no current run option verified · from 6 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 Qwen2.5-Max (2025-01-25) among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
coding          ▁▁▁▃▄▆●▇▅▄▃▁   #266 of 539*     32%      2
chat & writing  ▁▁▁▃▅▄▇█●▇▇▃   #163 of 474*     13%      1
math            ▁▁▃▆█●▅█▃▁▁▁   #261 of 530*     34%      2
coding · #266 of 539 · 32% of its score Beats [Qwen3.5 4B](qwen3-5-4b.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 3B Reasoning 2512](ministral-3-3b-reasoning.md), [s1.1 32B](s1-1-32b.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty            counts  why
Aider polyglot (225 exercises, tw…   21.8%  ▂·▂▃●▂▃█▆▄▂▂ beats 12/49  very hard · avg 7.6%     27%  sharp · at its level
WebApp1K-React pass@1 (onekq-ai)     57.6%  ▃▁▁█▁▄●▃▄▇▁▄ beats 14/31  medium · avg 40%        5.0%  sharp · at its level · noisy
chat & writing · #163 of 474 · 13% of its score Beats [o3-mini](o3-mini.md), [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md) here, though they rank higher overall. Trails [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1373.8  ▁▁▁▂▃▃▃▆●▇█▃ beats 215/385  avg 1260       13%  blunt
math · #261 of 530 · 34% of its score Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [GPT-4o (2024-08-06)](gpt-4o-2024-08-06.md) here, though they rank higher overall. Trails [Phi-2 (2.7B)](phi-2.md), [Granite 8B Code Base (4K)](granite-8b-code-base-4k.md), [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md) here, though they rank lower overall. 
test                      result  vs the others in its run   difficulty            counts  why
MATH level 5               67.2%  ▂▂▅▆▄█●▃▅▂▃▄ beats 58/92   hard · avg 28%           26%  sharp · at its level
OTIS mock AIME 2024–2025   16.1%  ▃▃▄▃●▃▇▇█▃▂▅ beats 54/197  very hard · avg 7.2%    8.8%  sharp · at its level · noisy
other tests (1) 
test          result  vs the others in its run   difficulty      counts  why
GPQA Diamond   56.1%  ▁▁▁▁▂▄▆●▅▇█▅ beats 82/211  hard · avg 39%     21%  sharp · 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                             3   55% ▇▇▇▇▇▇▇
Aider polyglot benchmark runs        1   27% ▇▇▇
LMArena leaderboard dataset          1   13% ▇▇
WebApp1K models leaderboard          1    5% ▇
prior: pull toward the middle             1% ▇
weighted average                        = 57.4
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Aider polyglot benchmark runs ([Apache-2.0](../../licenses/aider-polyglot-runs-Apache-2.0.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) ## How to run it No verified API or local download yet. [interactive version](https://whatmodel.sh/model/qwen-max-2025-01-25) · [JSON](https://whatmodel.sh/api?format=json&all=1)