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Qwen3.7 Flash

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Qwen3.7 Flash is a closed-weights language model from Alibaba, released July 2026.

Catalogue identity notes

Comparison-only. Epoch model_metadata.csv: 'Qwen3.7 Flash', Alibaba, 2026-07-27, 'API access'. Default (thinking) runs used; '_none' runs are not mixed in.

67.0±1.3#114 of 719 LLMsfrom 5 benchmark results across 1 source

$ how it compares

The LLMs ranked either side of Qwen3.7 Flash, out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
129Kimi K2 Thinking Moonshot AI67±1api $1.07/M +local~160GB2025-11
130Laguna M.1 Poolside67±2api price unknown +local~135GB2026-04
131Qwen3.7 Flash Alibaba67±1api $0.055/M2026-07
132Step 3.7 Flash (198B-A11B) StepFun67±1api $0.44/M +local~104GB2026-05
133Gemma 4 31B Google67±1api $0.19/M +local~21GB2026-04
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

math▁▂▃▅▆▅▄█▄▁▁▁ #132 of 398 * · 66% of its score · 2 tests

Beats Claude Sonnet 4.6, MiniMax-M3, MiMo-V2.5 (310B-A15B) here, though they rank higher overall. Trails Olmo 3.1 32B Think, Nemotron-Cascade-8B, Qwen3-VL 30B-A3B Thinking here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
FrontierMath tiers 1–3 v219.3% ✎▂··▁▁▃▄▄█▅▃▃ beats 13/82very hard · avg 1.2%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 55%sharp · at its level
OTIS mock AIME 2024–202586.7% ✎▃▃▄▃▃▃▇▇█▃▂▅ beats 130/193very hard · avg 8.5%▇▇▇▇ 10%sharp · noisy
other tests3 not in a task · 33% of its score
testresultvs the others in its rundifficultycountswhy
GPQA Diamond82.3% ✎▁▁▁▁▂▄▆▄▅▇█▅ beats 133/208hard · avg 40%▇▇▇▇▇▇▇▇▇ 22%sharp · at its level
Chess Puzzles (Epoch)23.0% ✎█▁▁▁▁▂▁▄▂▂▁▁ beats 91/135very hard · avg 5.9%▇▇ 5.7%sharp · noisy
Mystery Game Puzzles (Epoch)15.0% ✎█··▂▃▇▄█▂▃▁▁ beats 21/74very hard · avg 9.4%▇▇ 5.7%sharp · too hard for it · noisy

$ where to run it

Through an API

api · Alibaba Cloud Model Studio · $0.055/M · $0.03 in / $0.13 out per 1M tokens · 1M context · www.alibabacloud.com ↗ · price sourceUSD per 1M tokens, Singapore (International), qwen3.7-flash (currently equivalent to qwen3.7-flash-2026-07-15), 0<Token<=32K input tier: input $0.030, output $0.130; the 32K-256K ($0.100/$0.400) and 256K-1M ($0.200/$0.800) tiers are priced higher. Batch (50%) and context-cache discounts and free quota excluded. Observed 2026-09-25.

$ how the score was worked out · Epoch AI — internal evaluatio… 99%

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 evaluations5▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 99%CC BY 4.0
prior · pull toward the middle▇ 1%
weighted average= 67.0

$ 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