[whatmodel.sh](../../README.md) / Qwen Plus (2025-01-25) # Qwen Plus (2025-01-25) Alibaba · released 2025-01 · proprietary **score 56.6** ±1.3 · #252 of 732 · from 4 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 Qwen Plus (2025-01-25) among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃▆▄▆█●▇▇▃   #170 of 402*     19%      1
math            ▁▁▃▅█●▄█▄▂▁▁   #226 of 431*     51%      2
chat & writing · #170 of 402 · 19% of its score Beats [MiniMax-M2](minimax-m2.md), [MiniCPM5 2B](minicpm5-2b.md), [Granite 4.2 30B](granite-4-2-30b.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1345.9  ▁▁▁▂▃▃▃▆●▇█▃ beats 186/385  avg 1260       19%  blunt
math · #226 of 431 · 51% 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               65.3%  ▂▂▅▆▄█●▃▅▂▃▄ beats 57/92   hard · avg 28%           39%  sharp · at its level
OTIS mock AIME 2024–2025   17.8%  ▃▃▄▃●▃▇▇█▃▂▅ beats 57/197  very hard · avg 7.2%     12%  sharp · at its level · noisy
other tests (1) 
test          result  vs the others in its run   difficulty      counts  why
GPQA Diamond   48.1%  ▁▁▁▁▂▄●▄▅▇█▅ beats 63/211  hard · avg 39%     30%  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   81% ▇▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset          1   19% ▇▇
prior: pull toward the middle             1% ▇
weighted average                        = 56.6
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) · $0.60/M blended · $0.4 in / $1.2 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 qwen-plus-2025-01-25, no tiered pricing, non-thinking output: input $0.4, output $1.2. Batch and context-cache discounts, higher tiers and free quota excluded. Observed 2026-09-28. · [context source](https://www.alibabacloud.com/help/en/model-studio/qwen-plus): Context limit from the maker's documentation, observed 2026-10-06. [interactive version](https://whatmodel.sh/model/qwen-plus-2025-01-25) · [JSON](https://whatmodel.sh/api?format=json&all=1)