whatmodel.sh / Qwen Turbo (2024-11-01)

Qwen Turbo (2024-11-01)

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Qwen Turbo (2024-11-01) is a closed-weights language model from Alibaba, released November 2024. Licence: proprietary.

Catalogue identity notes

Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2024-11-01). Dated API snapshot qwen-turbo-2024-11-01. Not listed on Model Studio's text-generation or pricing pages (observed 2026-09-28), so api is null.

53.1±1.5comparison only: no current run option verifiedfrom 4 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of Qwen Turbo (2024-11-01), out of 828 with a score, then more from Alibaba.

#modelscorewhereRAMreleased
354Gemini 1.5 Pro (001) Google53±1comparison only2024-05
355Llama 3.1 Tulu 3 70B DPO Ai253±1local~22GB2024-11
356Qwen Turbo (2024-11-01) Alibaba53±2comparison only2024-11
357Qwen2.5-VL 32B Instruct Alibaba53±2local~22GB2025-03
358DeepSeek-Coder-V2 Instruct (0614) DeepSeek53±1local~92GB2024-06
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▁▂▃▅▇▅▅█▄▁▁▁ #269 of 462 * · 64% of its score · 2 tests

Beats GPT-4o (2024-08-06), Command A (03-2025), GPT-4o (2024-11-20) here, though they rank higher overall. Trails OpenThinker 7B, Olmo 3 7B Instruct, Tulu 2 70B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
MATH level 556.2% ✎▂▂▅▆▄█▆▃▅▂▃▄ beats 49/92hard · avg 32%▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 53%sharp · at its level
OTIS mock AIME 2024–20256.1% ✎▃▃▄▃▃▃▇▇█▃▂▅ beats 33/193very hard · avg 8.5%▇▇▇▇ 11%sharp · noisy
other tests2 not in a task · 35% of its score
testresultvs the others in its rundifficultycountswhy
GPQA Diamond41.8% ✎▁▁▁▁▂▄▆▄▅▇█▅ beats 43/208hard · avg 40%▇▇▇▇▇▇▇▇▇▇▇▇ 31%sharp · at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)76.5% ✎▃▁▁▁▁▁▁▁▂█▅▁ beats 234/276medium · avg 73%▇▇ 4.3%at its level · noisy

$ where to run it

No verified API or download recorded yet · add one

$ how the score was worked out · Epoch AI — internal evaluatio… 95%, Open Portuguese LLM Leaderboa… 4%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇ 4%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 53.1

$ 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