Compare
Qwen2.5 Coder 32B Instruct vs DeepSeek-R1-Distill-Qwen-32B
Same hardware, same arithmetic, side by side. Every memory figure is calculated from published model geometry rather than quoted from a marketing page.
Estimated
Side by side
On GeForce RTX 4090
| Property | Qwen2.5 Coder 32B Instruct | DeepSeek-R1-Distill-Qwen-32B |
|---|---|---|
| Organization | Alibaba Qwen | DeepSeek |
| Parameters | 32.8B | 32.8B |
| Architecture | Qwen2.5 · dense | Qwen2.5 · dense |
| Context | 32k | 128k |
| Layers | 64 | 64 |
| Hidden size | 5,120 | 5,120 |
| KV heads | 8 of 40 | 8 of 40 |
| Licence | Apache 2.0 | MIT |
| Commercial use | Yes | Yes |
| Modalities | text | text |
| Released | 2024-11-12 | 2025-01-20 |
| Recommended quantization | Q4_K_M | Q4_K_M |
| Memory needed | 21 GB | 21 GB |
| Estimated tok/s | 39 | 39 |
| Fit | Tight fit | Tight fit |
| Fine-tune here | Yes | Yes |
| HUMANEVAL (reported) | 92.7% | — |
Benchmark rows are figures the model's authors published, not ModelLM measurements, and the two models may not have been evaluated under identical conditions.
Trade-offs
Qwen2.5 Coder 32B Instruct
The strongest open code model that still fits one 24 GB card. The reason a lot of people buy a 4090.
Strengths
- Best open-weight coding quality at this size
- Apache 2.0
- Strong multi-file reasoning
Limitations
- Needs Q4_K_M on 24 GB
- Too slow for keystroke-latency completion on most cards
DeepSeek-R1-Distill-Qwen-32B
The strongest open reasoning model that fits a single 24 GB card. A genuinely different capability class on hard problems.
Strengths
- Best local reasoning under 70B
- MIT licensed
- Excellent at maths and proofs
Limitations
- Very verbose
- Tight on 24 GB
- Slow for interactive use
Common comparisons