DeepSeek-V3.2 Base vs Qwen3 Instruct: which model should you choose?
We compared DeepSeek-V3.2 Base and Qwen3 Instruct on price, context window and 12 benchmarks. DeepSeek-V3.2 Base has the stronger average benchmark score; Qwen3 Instruct 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 Instruct
Qwen3 Instruct is a multimodal model from Alibaba Cloud. It offers a 1,000,000-token context window, costs $5.06 per million input tokens and $25.39 per million output tokens, and averages 79.8 across the index's benchmark suite.
- Input
- $5.06 / 1M
- Output
- $25.39 / 1M
- Context
- 1,000,000
- Avg score
- 79.8
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-V3.2 Base | Qwen3 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $5.06 | A |
| Output price / 1M | $27.10 | $25.39 | B |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 128,000 | 128,000 | Tie |
| Average score | 80.2 | 79.8 | A |
| Arena Elo | 1417.9 | 1419.2 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-V3.2 Base wins 6, Qwen3 Instruct wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Base | Qwen3 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.3 | 80.3 | 4.0 | DeepSeek-V3.2 Base |
| GPQA Diamond | 77.1 | 74.1 | 3.0 | DeepSeek-V3.2 Base |
| SWE-bench Verified | 69.1 | 68.5 | 0.6 | DeepSeek-V3.2 Base |
| HumanEval | 82.8 | 85.1 | 2.3 | Qwen3 Instruct |
| MATH-500 | 83.7 | 86.1 | 2.4 | Qwen3 Instruct |
| AIME 2025 | 74.3 | 78.5 | 4.2 | Qwen3 Instruct |
| LiveCodeBench | 73.1 | 71.1 | 2.0 | DeepSeek-V3.2 Base |
| IFEval | 86.9 | 89.1 | 2.2 | Qwen3 Instruct |
| TruthfulQA | 83.5 | 79.7 | 3.8 | DeepSeek-V3.2 Base |
| HellaSwag | 90.2 | 87.0 | 3.2 | DeepSeek-V3.2 Base |
| MMMU | 76.7 | 78.7 | 2.0 | Qwen3 Instruct |
| Arena Elo | 1417.9 | 1419.2 | 1.3 | Qwen3 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Base costs about $10.56 per million tokens and Qwen3 Instruct costs about $10.14. Qwen3 Instruct is roughly 4% 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.
Qwen3 Instruct has the larger context window at 1,000,000 tokens, compared with 128,000 for DeepSeek-V3.2 Base. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 Base wins 6, Qwen3 Instruct wins 6, and 0 are tied. On the index average, DeepSeek-V3.2 Base leads Qwen3 Instruct by 0.4 points (80.2 versus 79.8).
The price picture favours Qwen3 Instruct. The context picture favours Qwen3 Instruct. 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 DeepSeek-V3.2 Base for quality-sensitive work and Qwen3 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-V3.2 Base if
- You want the higher average benchmark score in this index.
Choose Qwen3 Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 1,000,000-token context window.
FAQ
Is DeepSeek-V3.2 Base better than Qwen3 Instruct?
On the index average, DeepSeek-V3.2 Base leads Qwen3 Instruct by 0.4 points (80.2 versus 79.8). The better choice depends on price, context and your task. DeepSeek-V3.2 Base wins 6 benchmarks, Qwen3 Instruct wins 6.
Which is cheaper, DeepSeek-V3.2 Base or Qwen3 Instruct?
Qwen3 Instruct is cheaper on a blended 3:1 basis at about $10.14 per million tokens, versus $10.56 for DeepSeek-V3.2 Base.
Which has the larger context window?
Qwen3 Instruct has the larger context window at 1,000,000 tokens. DeepSeek-V3.2 Base has 128,000 tokens.
Are these models open weights?
DeepSeek-V3.2 Base is open weights; Qwen3 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-V3.2 Base for quality-sensitive work and Qwen3 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.