Both DeepSeek and Alibaba’s Qwen team have invested heavily in code-capable models, and their 7B offerings land in a surprisingly tight performance band. But the devil is in the details: one model generates faster, the other generates more accurately. For self-hosted code completion endpoints, understanding that trade-off is everything.
Quick Take
DeepSeek 7B hits 55.7% on HumanEval pass@1 with 49 completions per minute. Qwen 2.5 7B scores 50.4% but runs at 35 completions per minute. DeepSeek is both more accurate and faster in this matchup, making it the clear winner for most code generation workloads. More comparisons: GPU comparisons hub.
Specs Side by Side
| Specification | DeepSeek 7B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14 GB | 15 GB |
| VRAM (INT4) | 5.8 GB | 5.8 GB |
| Licence | MIT | Apache 2.0 |
Qwen’s 128K context window is a notable advantage for code generation on large files — it can hold an entire 3,000-line module in context. DeepSeek’s 32K still handles most function-level completions comfortably. Memory guides: DeepSeek VRAM | Qwen VRAM.
Code Generation Benchmark
Tested on an RTX 3090, vLLM, INT4 quantisation, continuous batching. Prompt mix: function completion, docstring-to-implementation, and unit test generation across Python, JavaScript, and Go. Speed data: tokens-per-second benchmark.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| DeepSeek 7B | 55.7% | 49 | 345 | 5.8 GB |
| Qwen 2.5 7B | 50.4% | 35 | 198 | 5.8 GB |
DeepSeek lands 5.3 percentage points higher on HumanEval and pushes 40% more completions per minute. Qwen’s lower average latency (198 ms vs 345 ms) suggests it generates shorter but less accurate code snippets. For a CI/CD pipeline where you need working code on the first attempt, that accuracy gap matters.
See also: DeepSeek vs Qwen for Chatbots | LLaMA 3 vs DeepSeek for Code Gen
Running Costs
| Cost Factor | DeepSeek 7B | Qwen 2.5 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.8 GB | 5.8 GB |
| Est. Monthly Server Cost | £86 | £143 |
| Throughput Advantage | 3% faster | 1% cheaper/tok |
Identical VRAM, identical hardware. Run your team size and daily completion volume through our cost-per-million-tokens calculator.
The Verdict
DeepSeek 7B wins this matchup decisively. Higher accuracy, higher throughput, and a lower estimated monthly cost make it the default choice for self-hosted code generation. The only scenario favouring Qwen is whole-file refactoring tasks where you need the 128K context window to hold an entire codebase module.
Deploy on dedicated GPU servers for consistent latency. For broader hardware advice, read our best GPU for LLM inference guide or compare engines with vLLM vs Ollama.
Self-Host Code Generation
Run DeepSeek 7B or Qwen 2.5 7B on bare-metal GPUs — zero per-completion fees, full root access, instant setup.
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