whatmodel.sh / Qwen Plus (2025-01-25)

Qwen Plus (2025-01-25)

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Qwen Plus (2025-01-25) is a closed-weights language model from Alibaba, released January 2025. Licence: proprietary.

Catalogue identity notes

Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2025-01-25). Dated API snapshot qwen-plus-2025-01-25 (arena label 'qwen-plus-0125' is probably the same snapshot; not asserted here). Listed on Model Studio's pricing page (observed 2026-09-28).

56.1±1.4#243 of 719 LLMsfrom 4 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Qwen Plus (2025-01-25), out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
291Gemini 2.0 Flash Experimental Google56±1comparison only2024-12
292Sky-T1 32B Preview NovaSky (UC Berkeley)56±5local~21GB2025-01
293Qwen Plus (2025-01-25) Alibaba56±1api $0.60/M2025-01
294Grok 2 (1212) SpaceXAI (xAI)56±1comparison only2024-12
295Qwen3-VL 4B Thinking Alibaba56±1local~4.1GB2025-10
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

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #163 of 393 * · 17% of its score · 1 test

Beats MiniMax-M2, MiniCPM5 2B, Granite 4.2 30B here, though they rank higher overall. Trails LFM2.5 1.2B Instruct, LFM2.5 1.2B Thinking, LFM2.5 8B-A1B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
LMArena (text)1345.9 ✎▁▁▁▂▃▃▃▆▆▇█▃ beats 186/379avg 1268▇▇▇▇▇▇▇ 17%blunt
math▁▂▃▅▆▅▄█▄▁▁▁ #216 of 398 * · 53% of its score · 2 tests

Beats DeepSeek V3 (Dec '24), GPT-4o (2024-08-06), Command A (03-2025) here, though they rank higher overall. Trails Olmo 3 7B Instruct, DeepSeek R1 Distill Llama 8B, Phi-4-mini-reasoning 3.8B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
MATH level 565.3% ✎▂▂▅▆▄█▆▃▅▂▃▄ beats 57/92hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 40%sharp · at its level
OTIS mock AIME 2024–202517.8% ✎▃▃▄▃▃▃▇▇█▃▂▅ beats 57/193very hard · avg 8.5%▇▇▇▇▇ 13%sharp · at its level · noisy
other tests1 not in a task · 29% of its score
testresultvs the others in its rundifficultycountswhy
GPQA Diamond48.1% ✎▁▁▁▁▂▄▆▄▅▇█▅ beats 63/208hard · avg 40%▇▇▇▇▇▇▇▇▇▇▇▇ 29%sharp · at its level

$ where to run it

Through an API

api · Alibaba Cloud Model Studio · $0.60/M · $0.4 in / $1.2 out per 1M tokens · 131k context · www.alibabacloud.com ↗ · price sourceUSD 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.

$ how the score was worked out · Epoch AI — internal evaluatio… 82%, LMArena leaderboard dataset 17%

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
Epoch AI — internal evaluations3▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 82%CC BY 4.0
LMArena leaderboard dataset1▇▇▇▇▇ 17%CC BY 4.0
prior · pull toward the middle▇ 1%
weighted average= 56.1

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json