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Alibaba Cloud (Qwen)
Qwen3.6-27B
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
Qwen3.6-27BPartial CPU Offload
VRAM Footprint (Q4):17.99 GB
Active GPU Load:14.24 GB
DeepSeek V4 ProOOM / Exceeds Limit
VRAM Footprint (Q4):1037.31 GB
Active GPU Load:0 GB
Hardware & Architecture Shootout Matrix
| Metric / Spec | Qwen3.6-27B | DeepSeek V4 Pro |
|---|---|---|
| Parameter Scale | 27B Parameters | 1600B Parameters |
| Attention Architecture | GQA + DeltaNet Linear | Standard GQA / MHA |
| Max Context Window | 262.14k | 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)
Qwen3.6-27B53.5%
DeepSeek V4 Pro52.1%
Reasoning Benchmark (GPQA / MMLU)
Qwen3.6-27B87.8%
DeepSeek V4 Pro88%
Frequently Asked Questions about Qwen3.6-27B vs DeepSeek V4 Pro
DeepSeek V4 Pro is cheaper than Qwen3.6-27B. DeepSeek V4 Pro has a blended cost of $NaN/1M tokens, which is about 1.0x cheaper than Qwen3.6-27B at $NaN/1M tokens.
Qwen3.6-27B is better for coding tasks on this benchmark. It scores 53.5% on swe-bench-pro (complex codebases, multi-file repositories, and architectural planning) compared to DeepSeek V4 Pro which scores 52.1%.
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