whatmodel.sh / QwQ-Plus
QwQ-Plus
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QwQ-Plus is a closed-weights language model from Alibaba, released April 2025. Licence: proprietary.
Catalogue identity notes
Comparison model from the Epoch AI internal-evaluation CSVs (Epoch release date 2025-04-08). API model qwq-plus (Epoch run 2025-04-11). Not asserted to equal the open-weights QwQ-32B.
$ how it compares
The LLMs ranked either side of QwQ-Plus, out of 1132 with a score, then more from Alibaba.
| # | model | score | where | ||
|---|---|---|---|---|---|
| 262 |  GPT-5.1-Codex-Mini | 62±3 | comparison only | ||
| 263 |  MiniMax-M1 (40k) | 61±1 | comparison only | ||
| 264 | QwQ-Plus | 61±2 | api $1.20/M | ||
| 265 |  Grok 4 Fast | 61±2 | comparison only | ||
| 266 |  Cogito v2 Preview Llama 70B | 61±7 | local | ||
| 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
math▁▂▃▅▆▅▄█▄▁▁▁ #147 of 400 * · 8.2% of its score · 1 test
Beats Claude Sonnet 4.6, MiMo-V2.5 (310B-A15B), MiniMax-M2.7 here, though they rank higher overall. Trails Nemotron 3 Nano 30B-A3B, Olmo 3 32B Think, Olmo 3.1 32B Think here, though they rank lower overall.
| test | result | vs the others in its run | counts |
|---|---|---|---|
| THINK-Bench final-answer accuracy (reasoning models) | 89.6% ✎ | ▄█·▄····▄▄·█ beats 4/7 | 8.2% |
other tests1 not in a task · 89% of its score
| test | result | vs the others in its run | counts |
|---|---|---|---|
| GPQA Diamond | 65.4% ✎ | ▁▁▁▁▂▄▆▄▅▇█▅ beats 95/208 | 89% |
$ where to run it
Through an API
api · Alibaba Cloud Model Studio · $1.20/M · $0.8 in / $2.4 out per 1M tokens · 128k context · www.alibabacloud.com ↗ · price sourceUSD per 1M tokens, Singapore (International), qwq-plus, single tier: input $0.8, output $2.4. 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… 89%, THINK-Bench: Evaluating Think… 8%
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 | 1 | 89% |
 THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models (arXiv:2505.22113v1) | 1 | 8% |
| prior · pull toward the middle | 2% | |
| weighted average | = 61.3 |
$ 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