[whatmodel.sh](../../README.md) / Qwen3.7 Plus
# Qwen3.7 Plus
Alibaba · released 2026-06 · closed weights
**score 70.8** ±0.6 · #72 of 732 · from 33 benchmark results across 6 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Qwen3.7 Plus among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅●▄▂▁ #66 of 313 23% 7
coding ▁▁▁▃▅▇█▇▅●▃▁ #53 of 469 11% 4
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #49 of 402* 4.2% 1
math ▁▁▃▅█▆▄●▄▂▁▁ #79 of 431* 21% 2
vision & documents ▁▁▂▂▁▃▄▂▆▇●▄ #27 of 153 14% 7
agent tasks · #66 of 313 · 23% of its score
Beats [Gemini 3.6 Flash](gemini-3-6-flash.md), [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [GPT-5 Pro](gpt-5-pro.md) here, though they rank higher overall. Trails [GLM-5.1](glm-5-1.md), [Beam 1.0 501B A23B](beam-1-0-501b-a23b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… -0.0737 IPS ▁▁▁●▃▂█▁▂▂▁▁ beats 10/59 avg -0.379 IPS 11% sharp
SWE-bench Pro public set, Qwen-re… 57.6% ▂·····▂·▂█●▅ beats 9/12 hard · avg 21% 3.6% at its level · several runs
Terminal-Bench 2.1 (maker) 64.0% ▁·▁·▁▁▄▃▆●▅█ beats 31/63 hard · avg 17% 2.6% sharp · at its level · small run
SWE-bench Multilingual (maker) 75.8% ▁··▁▃▁▅█●▂▂▁ beats 47/65 hard · avg 29% 2.6% at its level
OSWorld-Verified (maker) 73.3% █▃·▂▅▂▃▂●▃▆▆ beats 15/27 hard · avg 27% 1.6% at its level · small run
WebArena-Verified (maker) 55.3% █···●······█ beats 1/2 hard · avg 24% 0.9% at its level · small run
DeepSWE v1.1 (resolved) (maker) 16.5% ●····▂▁▁▆███ beats 0/31 hard · avg 16% 0.6% at its level · noisy
coding · #53 of 469 · 11% 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.
test result vs the others in its run difficulty counts why
LiveCodeBench v6 (window unspecif… 89.6% ▃▅█▅▅▃▅···●█ beats 13/16 medium · avg 47% 4.3% small run · several runs
SWE-bench Pro public set, Qwen-re… 57.6% ▂·····▂·▂█●▅ beats 9/12 hard · avg 21% 3.6% at its level · several runs
SWE-bench Multilingual (maker) 75.8% ▁··▁▃▁▅█●▂▂▁ beats 47/65 hard · avg 29% 2.6% at its level
DeepSWE v1.1 (resolved) (maker) 16.5% ●····▂▁▁▆███ beats 0/31 hard · avg 16% 0.6% at its level · noisy
chat & writing · #49 of 402 · 4.2% of its score
Beats [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [Claude Haiku 5.5](claude-haiku-5-5.md), [GPT-6 Luna](gpt-6-luna.md) here, though they rank higher overall. Trails [Gemini 3.5 Flash-Lite](gemini-3-5-flash-lite.md), [MiMo-V2.5-Pro (1T-A42B)](mimo-v2-5-pro.md), [GLM-5](glm-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1455.7 ▁▁▁▂▃▃▃▆▆▇●▃ beats 329/385 avg 1260 4.2% blunt
math · #79 of 431 · 21% of its score
Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Claude Opus 4.5](claude-opus-4-5.md) here, though they rank higher overall. Trails [Granite 4.2 30B](granite-4-2-30b.md), [Qwen3-Next 80B-A3B Instruct](qwen3-next-80b-a3b-instruct.md), [INTELLECT-3](intellect-3.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 34.4% ▂··▁▁▃●▄█▆▄▃ beats 24/86 very hard · avg 1.2% 20% sharp · at its level
OTIS mock AIME 2024–2025 93.3% ▃▃▄▃▃▃▇▇●▃▂▅ beats 154/197 very hard · avg 7.2% 1.1% sharp · noisy
vision & documents · #27 of 153 · 14% of its score
Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md), [MiMo-V2.6-Pro](mimo-v2-6-pro.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
CharXiv (reasoning questions) (ma… 84.4% ▄▂▄▆▄▄▆··▄●▂ beats 19/21 medium · avg 38% 6.7% several runs
Arena vision (style controlled) 1263.3 ▁▁▂▂▂▂▃▄▅▅●▇ beats 118/152 avg 1061 2.8% blunt
Arena document (style controlled) 1452.5 ▁▁▃▃▄▃●█▅▇▄▅ beats 13/37 avg 1331 1.4% blunt
SciDocBench scientific-document w… 55.9% ▃▃···█▃▅●·▃▅ beats 8/11 hard · avg 25% 1.4% blunt · at its level
OmniDocBench 1.5 overall (higher … 91.4% ▂··▂▂▂··▂▂█● beats 13/13 easy · avg 70% 1.1% small run
SpaceCast-Bench predictive spatia… 55.3% ▃·▅▃▅▃··▅··● beats 9/11 very hard · avg 33% 0.4% at its level · noisy
RealWorldQA (maker) 86.9% ▁▃▁▇▃▇▇▅█▁▅● beats 39/39 easy · avg 72% 0.4% blunt · small run
other tests (8)
test result vs the others in its run difficulty counts why
GPQA Diamond 87.9% ▁▁▁▁▂▄▆▄▅▇●▅ beats 161/211 hard · avg 40% 15% sharp · several runs
Humanity's Last Exam (maker) 34.7% ▁▁▁▄▆▆▇█●▆▁▃ beats 86/111 very hard · avg 5.2% 4.8% at its level · several runs
Mystery Game Puzzles (Epoch) 17.0% █··▂▃●▄█▂▄▂▁ beats 28/77 very hard · avg 9.4% 4.1% sharp · noisy
Chess Puzzles (Epoch) 24.0% █▁▁▁▁▂▃●▃▂▁▁ beats 93/137 very hard · avg 5.8% 2.9% sharp · noisy
IFBench (aggregation unspecified)… 79.1% ▂▆▂▄▄▄▂▄▄▆█● beats 77/86 medium · avg 43% 2.9% several runs
NL2Repo (maker) 41.1% ▂▂▂·▃▃▅●▃▂▅▃ beats 10/22 very hard · avg 11% 1.7% at its level · small run
ERQA (maker) 69.8% ▃··▃··█·▃█▅● beats 12/12 medium · avg 36% 0.9% at its level · small run
AndroidWorld (maker) 81.0% ▁·▁▁▂▃█▅▂▂·● beats 23/24 medium · avg 47% 0.8% small run
source results share of the score
Epoch AI 5 32% ▇▇▇▇
Qwen/Qwen3.8-27B model card (submitted … 14 24% ▇▇▇
Qwen (submitted / maker-reported) 8 23% ▇▇▇
LMArena leaderboard dataset 4 19% ▇▇
SciDocBench: A Workflow-Centered Benchm… 1 1% ▇
SpaceCast-Bench: Evaluating Predictive … 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 70.8
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Qwen/Qwen3.8-27B model card ([apache-2.0](../../licenses/Qwen--Qwen3.8-27B.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SpaceCast-Bench: Evaluating Predictive Spatial Reasoning in Vision-Language Models ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (Alibaba Cloud Model Studio) · $0.70/M blended · $0.4 in / $1.6 out per 1M tokens · [alibabacloud.com](https://www.alibabacloud.com/help/en/model-studio/models)
[price source](https://www.alibabacloud.com/help/en/model-studio/model-pricing): USD per 1M tokens, Singapore (International), qwen3.7-plus, ≤256K input tier list price (a limited-time 20% discount is also advertised and not applied); 256K–1M input tier priced higher. Observed 2026-09-25. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/alibaba/qwen3.7-plus.toml)
[interactive version](https://whatmodel.sh/model/qwen3-7-plus) · [JSON](https://whatmodel.sh/api?format=json&all=1)