[whatmodel.sh](../../README.md) / Qwen3.8-Omni-Flash # Qwen3.8-Omni-Flash Alibaba · released 2026-09 · proprietary **score 73.6** ±1.4 · #44 of 732 · from 8 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Qwen3.8-Omni-Flash among every model on that task or test, worst to best. 
task                vs the others         rank  counts  tests
agent tasks         ▁▁▃▆█▄▅▅●▄▂▁    #49 of 313     23%      3
coding              ▁▁▁▃▅▇█▇▅▄●▁    #30 of 469     31%      4
vision & documents  ▁▁▂▂▁▃▄▂▆▇●▄   #15 of 153*     21%      1
agent tasks · #49 of 313 · 23% of its score Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md) here, though they rank higher overall. Trails [Claude Sonnet 4.6](claude-sonnet-4-6.md), [GLM-5.2](glm-5-2.md), [GLM-5.3-Flash](glm-5-3-flash.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty      counts  why
SWE-bench Multilingual (maker)       80.5%  ▁··▁▃▁▅██●▂▁ beats 55/65  hard · avg 29%     12%  typical
SWE-bench Pro public set, Qwen-re…   63.3%  ▂·····▂·▂█▃● beats 12/12  hard · avg 21%    8.7%  at its level
DeepSWE v1.1 (resolved) (maker)      57.8%  ▂····▂▁▁●███ beats 6/31   hard · avg 16%    2.8%  at its level · noisy
coding · #30 of 469 · 31% of its score Beats [GPT-5.5](gpt-5-5.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [Claude Opus 4.8](claude-opus-4-8.md) here, though they rank higher overall. Trails [GLM-5.3-Flash](glm-5-3-flash.md), [Grok 4.7](grok-4-7.md), [Qwen3.8-Flash-Next](qwen3-8-flash-next.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty      counts  why
SWE-bench Multilingual (maker)       80.5%  ▁··▁▃▁▅██●▂▁ beats 55/65  hard · avg 29%     12%  typical
SWE-bench Pro public set, Qwen-re…   63.3%  ▂·····▂·▂█▃● beats 12/12  hard · avg 21%    8.7%  at its level
LiveCodeBench v6 (window unspecif…   92.6%  ▃▅█▅▅▃▅···▃● beats 16/16  hard · avg 35%    8.0%  typical
DeepSWE v1.1 (resolved) (maker)      57.8%  ▂····▂▁▁●███ beats 6/31   hard · avg 16%    2.8%  at its level · noisy
vision & documents · #15 of 153 · 21% of its score Beats [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md), [GPT-5.5](gpt-5-5.md) here, though they rank higher overall. Trails [Muse Spark 1.2](muse-spark-1-2.md), [Qwen3.8-Flash-Next](qwen3-8-flash-next.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
CharXiv (reasoning questions) (ma…   83.5%  ▄▂▄▆▄▄▆··▄●▂ beats 17/21  medium · avg 38%     21%  typical
other tests (3) 
test                                result  vs the others in its run    difficulty            counts  why
GPQA Diamond (maker)                 91.0%  ▁▁▁▁▂▄▅▅██●▄ beats 288/322  hard · avg 40%           26%  sharp
Humanity's Last Exam (maker)         36.5%  ▁▁▁▄▆▆▇█●▆▁▃ beats 90/111   very hard · avg 5.2%     14%  at its level
IFBench (aggregation unspecified)…   81.5%  ▂▆▂▄▄▄▂▄▄▆█● beats 85/86    medium · avg 43%        7.7%  typical
## 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
Qwen (submitted / maker-reported)        8   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                 1% ▇
weighted average                            = 73.6
## How to run it ### Through an API api (Alibaba Cloud Model Studio) · $0.23/M blended · $0.15 in / $0.47 out per 1M tokens · [alibabacloud.com](https://www.alibabacloud.com/help/en/model-studio/qwen-omni) [price source](https://www.alibabacloud.com/help/en/model-studio/model-pricing): Observed 2026-10-09 on Alibaba Cloud Model Studio 'Model pricing', Qwen-Omni table, Singapore region, model ID qwen3.8-omni-flash, deployment scope International: input USD 0.15, cache-hit input USD 0.016, output USD 0.47 per million tokens. Other regions (e.g. China (Beijing)) price differently; realtime API excluded. · [context source](https://www.alibabacloud.com/help/en/model-studio/qwen-omni): Alibaba Cloud Model Studio Qwen-Omni page: 'The context length is 1M tokens' for qwen3.8-omni-flash. Observed 2026-10-09. [interactive version](https://whatmodel.sh/model/qwen3-8-omni-flash) · [JSON](https://whatmodel.sh/api?format=json&all=1)