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Qwen3.6 Max Preview
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Qwen3.6 Max Preview is a closed-weights language model from Alibaba, released April 2026. Licence: proprietary.
Catalogue identity notes
Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2026-04-20). API model qwen3.6-max-preview (preview); distinct from Qwen3.7 Max and Qwen3.8 Max. Default runs used; the mystery-games result is the only published run ('_none').
$ how it compares
The LLMs ranked either side of Qwen3.6 Max Preview, out of 828 with a score, then more from Alibaba.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 64 |  Gemini 3 Pro (Preview) | 73±1 | comparison only | ||
| 65 |  GPT-5 | 72±1 | api $3.44/M | ||
| 66 | Qwen3.6 Max Preview | 72±1 | api $2.92/M | ||
| 67 |  Claude Sonnet 4.6 | 72±1 | api $6.00/M | ||
| 68 | Gemini 3 Flash (Preview) | 72±1 | api $1.13/M | ||
| more from Alibaba | |||||
| 18 | Qwen3.8 Max (2.4T-A95B) | 78±1 | api $3.00/M +local | ||
| 34 | Qwen3.8 Max (0902) | 76±1 | api $3.00/M | ||
| 37 | Qwen3.8-Flash-Next | 75±1 | local | ||
$ what it's good at
agent tasks▁▁▃▆█▆▅▅▅▄▃▁ #68 of 300 * · 24% of its score · 1 test
Beats Gemini 3.6 Flash, GPT-5 Pro, LongCat 2.0 (1.6T-A48B) here, though they rank higher overall. Trails MiMo-V2.5 (310B-A15B), MiniMax-M3, Ornith 1.5 35B-A3B here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| SWE-bench Verified | 76.7% ✎ | ▁··▁·▁▂▁▄█▄▁ beats 20/29 | 24% |
coding▁▁▁▃▆██▇▆▄▃▁ #55 of 458 * · 46% of its score · 2 tests
Beats GPT-5.4, DeepSeek V4 Flash Vision (Exp), Gemini 3.1 Pro (Preview) here, though they rank higher overall. Trails GLM-5.1, Mistral Large 4 (public preview), Qwen3.7 Plus here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| SWE-bench Verified | 76.7% ✎ | ▁··▁·▁▂▁▄█▄▁ beats 20/29 | 24% |
| Arena WebDev (Code Arena) | 1481.5 ✎ | ▁▂▄▃▅█▄▇▆▄▁▁ beats 66/119 | 22% |
chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #42 of 393 * · 12% of its score · 1 test
Beats GPT-6 Sol, MiMo-V2.6-Flash (309B-A15B), GPT-6 Luna here, though they rank higher overall. Trails MiMo-V2.5-Pro (1T-A42B), GLM-5.1, Grok 4.20 (0309 reasoning) here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| LMArena (text) | 1460.3 ✎ | ▁▁▁▂▃▃▃▆▆▇█▃ beats 330/379 | 12% |
math▁▂▃▅▆▅▄█▄▁▁▁ #64 of 398 * · 3.9% of its score · 1 test
Trails INTELLECT-3, Qwen3-Coder-Next (80B-A3B), GPT-5 mini here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| OTIS mock AIME 2024–2025 | 91.1% ✎ | ▃▃▄▃▃▃▇▇█▃▂▅ beats 145/193 | 3.9% |
other tests4 not in a task · 37% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GPQA Diamond | 87.4% ✎ | ▁▁▁▁▂▄▆▄▅▇█▅ beats 156/208 | 12% |
| Mystery Game Puzzles (Epoch) | 19.0% ✎ | █··▂▃▇▄█▂▃▁▁ beats 31/74 | 11% |
| Chess Puzzles (Epoch) | 20.0% ✎ | █▁▁▁▁▂▁▄▂▂▁▁ beats 77/135 | 8.5% |
| SimpleQA Verified | 52.0% ✎ | ▁▁▃▂▃▃▆▆█▃▂▅ beats 57/78 | 4.8% |
$ where to run it
Through an API
api · Alibaba Cloud Model Studio · $2.92/M · $1.3 in / $7.8 out per 1M tokens · 256k context · www.alibabacloud.com ↗ · price sourceUSD per 1M tokens, Singapore (International), qwen3.6-max-preview, 0<Token<=128K input tier (128K-256K tier $2/$12 excluded): input $1.3, output $7.8. 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… 65%, LMArena leaderboard dataset 35%
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
| source | results | share of the score |
|---|---|---|
 Epoch AI — internal evaluations | 6 | 65% |
 LMArena leaderboard dataset | 2 | 35% |
| prior · pull toward the middle | 1% | |
| weighted average | = 72.4 |
$ improve this page
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