DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism tha…
Model details →DeepSeek: DeepSeek V3.2 vs Qwen: Qwen-Turbo
DeepSeek: DeepSeek V3.2 and Qwen: Qwen-Turbo are evenly matched on 6 axes — pick the one whose provider, latency, or licensing fits your stack.
Qwen-Turbo, based on Qwen2.5, is a 1M context model that provides fast speed and low cost, suitable for simple tasks.
Model details →Side-by-side comparison
| Capability | DeepSeek: DeepSeek V3.2 | Qwen: Qwen-Turbo | Winner |
|---|---|---|---|
| Context window Maximum number of input tokens the model can attend to in a single request. | 164K | 131K | 🏆 DeepSeek: DeepSeek V3.2 |
| Input price (per 1M) Cost per million input tokens billed by the provider. | $0.26 | $0.03 | 🏆 Qwen: Qwen-Turbo |
| Output price (per 1M) Cost per million output tokens billed by the provider. | $0.38 | $0.13 | 🏆 Qwen: Qwen-Turbo |
| Tool / function calling First-class support for emitting structured tool calls. | Yes | Yes | Tie |
| Vision input Accepts image inputs alongside text. | — | — | Tie |
| Reasoning mode Internal chain-of-thought / extended-thinking support. | Yes | — | 🏆 DeepSeek: DeepSeek V3.2 |
Frequently asked questions
Is DeepSeek: DeepSeek V3.2 better than Qwen: Qwen-Turbo?
DeepSeek: DeepSeek V3.2 and Qwen: Qwen-Turbo are evenly matched on 6 axes — pick the one whose provider, latency, or licensing fits your stack.
What's the price difference between DeepSeek: DeepSeek V3.2 and Qwen: Qwen-Turbo?
Input: $0.26 vs $0.03 per 1M tokens. Output: $0.38 vs $0.13 per 1M tokens.
What context windows do DeepSeek: DeepSeek V3.2 and Qwen: Qwen-Turbo support?
DeepSeek: DeepSeek V3.2 supports up to 164K tokens. Qwen: Qwen-Turbo supports up to 131K tokens.
Do both DeepSeek: DeepSeek V3.2 and Qwen: Qwen-Turbo support tool calling?
DeepSeek: DeepSeek V3.2: yes. Qwen: Qwen-Turbo: yes.