DeepSeek-V3.2 Base vs Qwen3: which model should you choose?
We compared DeepSeek-V3.2 Base and Qwen3 on price, context window and 12 benchmarks. Qwen3 has the stronger average benchmark score; Qwen3 is cheaper on a blended 3:1 basis.
DeepSeek-V3.2 Base
DeepSeek-V3.2 Base is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.05 per million input tokens and $27.10 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $27.10 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
Qwen3
Qwen3 is a multimodal model from Alibaba Cloud. It offers a 128,000-token context window, costs $5.04 per million input tokens and $22.52 per million output tokens, and averages 80.3 across the index's benchmark suite.
- Input
- $5.04 / 1M
- Output
- $22.52 / 1M
- Context
- 128,000
- Avg score
- 80.3
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-V3.2 Base | Qwen3 | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $5.04 | B |
| Output price / 1M | $27.10 | $22.52 | B |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 128,000 | 8,192 | A |
| Average score | 80.2 | 80.3 | B |
| Arena Elo | 1417.9 | 1420.9 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-V3.2 Base wins 4, Qwen3 wins 7, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Base | Qwen3 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.3 | 81.2 | 3.1 | DeepSeek-V3.2 Base |
| GPQA Diamond | 77.1 | 77.7 | 0.6 | Qwen3 |
| SWE-bench Verified | 69.1 | 71.6 | 2.5 | Qwen3 |
| HumanEval | 82.8 | 87.0 | 4.2 | Qwen3 |
| MATH-500 | 83.7 | 86.5 | 2.8 | Qwen3 |
| AIME 2025 | 74.3 | 74.5 | 0.2 | Qwen3 |
| LiveCodeBench | 73.1 | 73.8 | 0.7 | Qwen3 |
| IFEval | 86.9 | 85.6 | 1.3 | DeepSeek-V3.2 Base |
| TruthfulQA | 83.5 | 80.4 | 3.1 | DeepSeek-V3.2 Base |
| HellaSwag | 90.2 | 88.1 | 2.1 | DeepSeek-V3.2 Base |
| MMMU | 76.7 | 76.7 | 0.0 | Tie |
| Arena Elo | 1417.9 | 1420.9 | 3.0 | Qwen3 |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Base costs about $10.56 per million tokens and Qwen3 costs about $9.41. Qwen3 is roughly 11% cheaper than DeepSeek-V3.2 Base. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 Base wins 4, Qwen3 wins 7, and 1 are tied. On the index average, Qwen3 leads DeepSeek-V3.2 Base by 0.1 points (80.3 versus 80.2).
The price picture favours Qwen3. The context picture is effectively a tie. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with Qwen3 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-V3.2 Base if
- You need the larger 128,000-token output limit.
Choose Qwen3 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
FAQ
Is DeepSeek-V3.2 Base better than Qwen3?
On the index average, Qwen3 leads DeepSeek-V3.2 Base by 0.1 points (80.3 versus 80.2). The better choice depends on price, context and your task. DeepSeek-V3.2 Base wins 4 benchmarks, Qwen3 wins 7.
Which is cheaper, DeepSeek-V3.2 Base or Qwen3?
Qwen3 is cheaper on a blended 3:1 basis at about $9.41 per million tokens, versus $10.56 for DeepSeek-V3.2 Base.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
DeepSeek-V3.2 Base is open weights; Qwen3 is open weights.
Which should I choose for production?
If you need a single default, start with Qwen3 for quality-sensitive work, and check whether its price holds at your expected volume before committing.