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Alibaba Cloud (Qwen)
Qwen2.5-Coder 32B
VS
DeepSeek
DeepSeek V4 Pro
Analysis By: The WhichLLMModel Editorial Team|Updated: June 2026
Live Hardware Compatibility Simulator
GPU Dedicated VRAM16 GB
System Host RAM32 GB
Qwen2.5-Coder 32BPartial CPU Offload
VRAM Footprint (Q4):20.81 GB
Active GPU Load:14.34 GB
DeepSeek V4 ProOOM / Exceeds Limit
VRAM Footprint (Q4):1037.31 GB
Active GPU Load:0 GB
Hardware & Architecture Shootout Matrix
| Metric / Spec | Qwen2.5-Coder 32B | DeepSeek V4 Pro |
|---|---|---|
| Parameter Scale | 32B Parameters | 1600B Parameters |
| Attention Architecture | Standard GQA / MHA | Standard GQA / MHA |
| Max Context Window | 131.07k | 1.05M |
| Available GGUF Quants | 2 Quant Formats | 0 Quant Formats |
| Full Model Page | Hardware Details | Hardware Details |
Intelligence & Benchmark Head-to-Head
Coding Benchmark (SWE-bench / HumanEval)
Qwen2.5-Coder 32B31.4%
DeepSeek V4 Pro52.1%
Reasoning Benchmark (GPQA / MMLU)
Qwen2.5-Coder 32BN/A%
DeepSeek V4 Pro88%
Frequently Asked Questions about Qwen2.5-Coder 32B vs DeepSeek V4 Pro
DeepSeek V4 Pro is cheaper than Qwen2.5-Coder 32B. DeepSeek V4 Pro has a blended cost of $NaN/1M tokens, which is about 1.0x cheaper than Qwen2.5-Coder 32B at $NaN/1M tokens.
For coding tasks, Qwen2.5-Coder 32B scores 31.4% on live-code-bench (scripting single-file apps or clearly defined functions), while DeepSeek V4 Pro scores 52.1% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning).
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