The gap between a useful code assistant and a frustrating one often comes down to a single metric: does the suggested function actually pass its tests? Qwen 2.5 7B outscores Mistral 7B on HumanEval by nearly 14 percentage points, but Mistral compensates with raw speed. Here is what that trade-off looks like on real GPU hardware.
Model Architecture
| Specification | Mistral 7B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 7B | 7B |
| Architecture | Dense Transformer + SWA | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14.5 GB | 15 GB |
| VRAM (INT4) | 5.5 GB | 5.8 GB |
| Licence | Apache 2.0 | Apache 2.0 |
Qwen’s 128K context is a genuine advantage for code generation — it can hold an entire large module plus test files simultaneously. Mistral’s 32K is sufficient for most function-level completions. VRAM details: Mistral | Qwen.
Code Generation Numbers
Hardware: RTX 3090, vLLM, INT4, continuous batching. Prompts: function completion, bug fixes, and test generation in Python and TypeScript. Speed reference: tokens-per-second benchmark.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| Mistral 7B | 46.2% | 38 | 213 | 5.5 GB |
| Qwen 2.5 7B | 59.8% | 33 | 221 | 5.8 GB |
Qwen’s 59.8% pass@1 versus Mistral’s 46.2% is a 13.6 point gap — that translates to roughly 1 in 7 suggestions where Qwen gets it right and Mistral does not. However, Mistral delivers 15% more completions per minute (38 vs 33) with slightly lower latency. For rapid-fire IDE tab completions where developers treat suggestions as hints, Mistral’s speed can feel better. For automated pipelines where correctness drives value, Qwen’s accuracy is worth the wait.
Related: Mistral vs Qwen for Chatbots | LLaMA 3 vs Mistral for Code Gen
Cost Comparison
| Cost Factor | Mistral 7B | Qwen 2.5 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.5 GB | 5.8 GB |
| Est. Monthly Server Cost | £179 | £141 |
| Throughput Advantage | 13% faster | 8% cheaper/tok |
Both fit on a single GPU. Use our cost-per-million-tokens calculator to model your developer count and daily completion volume.
Which One for Your Dev Team?
Qwen 2.5 7B for code correctness. If your workflow depends on generated code being right — think automated test generation, CI/CD pipeline integrations, or code review bots — the 59.8% pass@1 saves developer time on reviews and fixes. The 128K context also means it can reason about entire files during refactoring tasks.
Mistral 7B for developer experience. If your primary use case is IDE autocomplete where suggestions are advisory, the 15% speed boost makes interactions feel snappier. Developers who accept/reject suggestions quickly will prefer the faster feedback loop.
Deploy on dedicated GPU servers for consistent latency. For hardware selection: best GPU for LLM inference. For engine choice: vLLM vs Ollama.
Host Your Code Assistant
Run Mistral 7B or Qwen 2.5 7B on bare-metal GPUs — no per-completion charges, full root access.
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