QwQ Chat: pricing, context window and benchmarks
QwQ Chat is a multimodal model from Alibaba Cloud. It offers a 65,536-token context window, costs $4.28 per million input tokens and $16.00 per million output tokens, and averages 78.2 across the index's benchmark suite.
Pricing
QwQ Chat costs $4.28 per million input tokens and $16.00 per million output tokens. A blended 3:1 input-to-output mix works out to about $7.21 per million tokens. Cached input is listed at $0.59 per million tokens.
| Item | Price | Notes |
|---|---|---|
| Input | $4.28 | per 1M tokens |
| Output | $16.00 | per 1M tokens |
| Cached input | $0.59 | per 1M tokens |
| Blended 3:1 | $7.21 | estimate per 1M tokens |
| 1M in + 1M out | $20.28 | estimate |
Benchmarks
Scores below are modelled estimates on a 0–100 scale. Use them to compare relative position, not as official vendor results.
| Benchmark | Score | Rank | Percentile | Relative |
|---|---|---|---|---|
| MMLU-Pro | 80.6 | 167 | 72th | |
| GPQA Diamond | 72.8 | 203 | 66th | |
| SWE-bench Verified | 68.3 | 139 | 77th | |
| HumanEval | 83.4 | 151 | 75th | |
| MATH-500 | 82.1 | 166 | 72th | |
| AIME 2025 | 76.1 | 140 | 77th | |
| LiveCodeBench | 68.8 | 213 | 65th | |
| IFEval | 85.2 | 186 | 69th | |
| TruthfulQA | 79.8 | 191 | 68th | |
| HellaSwag | 87.9 | 147 | 76th | |
| MMMU | 75.7 | 145 | 76th | |
| Arena Elo | 1414.1 | 173 | 71th |
What QwQ Chat is good for
QwQ Chat is tuned toward coding work. It is a reasonable candidate for code generation, review, debugging and repository-scale tasks, especially when paired with a retrieval layer. At $4.28 per million input tokens, it sits in the mid-range of the index, and the 65,536-token context window is enough for large files and multi-file diffs.
Strengths
- Open weights make self-hosting, fine-tuning and data-residency review possible.
- Accepts more than text, including image input where the provider supports it.
- Coding benchmarks are a relative strength within its tier.
Trade-offs
- No standout weakness in the indexed specifications, but real-world behaviour still needs testing.
How it compares
| Model | Provider | Context | Input / 1M | Avg score |
|---|---|---|---|---|
| GLM-4.5 | Zhipu AI | 200000 | $4.55 | 78.2 |
| Hunyuan-Large | Tencent | 65536 | $4.33 | 78.2 |
| Hunyuan-Large Instruct | Tencent | 65536 | $4.39 | 78.2 |
| Llama 3.3 | Meta AI | 128000 | $4.56 | 78.2 |
| Mistral Large 3 Base | Mistral AI | 65536 | $4.47 | 78.2 |
| QwQ Chat | Alibaba Cloud | 65536 | $4.28 | 78.2 |
FAQ
How much does QwQ Chat cost?
QwQ Chat is listed at $4.28 per million input tokens and $16.00 per million output tokens. Cached input is $0.59 per million tokens. A blended 3:1 input-to-output mix is about $7.21 per million tokens.
What is the context window of QwQ Chat?
QwQ Chat has a context window of 65,536 tokens and can emit up to 16,384 output tokens per request in this index.
Is QwQ Chat open weights?
Yes. QwQ Chat is listed as an open-weight model under a open weights licence.
How does QwQ Chat score on benchmarks?
QwQ Chat averages 78.2 across the scored benchmarks in this index, ranking #168 of 600 models. Its strongest relative result is Arena Elo at 1414.1.
What is QwQ Chat best for?
QwQ Chat is tuned toward coding work. It is a reasonable candidate for code generation, review, debugging and repository-scale tasks, especially when paired with a retrieval layer. At $4.28 per million input tokens, it sits in the mid-range of the index, and the 65,536-token context window is enough for large files and multi-file diffs.