Table of Contents
Quick Verdict
Mixtral 8x7B scores 50.9% on HumanEval and produces 41 completions per minute. Qwen 72B scores 42.8% but manages only 35 completions per minute. That is an 8-point accuracy advantage and a 17% throughput lead for Mixtral — it wins on both fronts for code generation on a dedicated GPU server.
Qwen 72B’s saving grace is its 128K context window, which lets it process entire codebases in a single pass for tasks like cross-file refactoring. But for standard function-level completions, Mixtral is the clear winner.
Details below. More at our GPU comparisons hub.
Specs Comparison
Mixtral’s MoE architecture provides an unusual combination for code generation: it uses substantially less VRAM than Qwen while delivering better accuracy, because code tasks tend to activate specific expert modules efficiently.
| Specification | Mixtral 8x7B | Qwen 72B |
|---|---|---|
| Parameters | 46.7B (12.9B active) | 72B |
| Architecture | Mixture of Experts | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 93 GB | 145 GB |
| VRAM (INT4) | 26 GB | 42 GB |
| Licence | Apache 2.0 | Qwen |
Guides: Mixtral 8x7B VRAM requirements and Qwen 72B VRAM requirements.
Code Generation Benchmark
Benchmarked on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Tasks covered Python, JavaScript, and TypeScript completions. Live data at our tokens-per-second benchmark.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| Mixtral 8x7B | 50.9% | 41 | 304 | 26 GB |
| Qwen 72B | 42.8% | 35 | 283 | 42 GB |
Qwen 72B’s slightly lower average latency (283 ms vs 304 ms) does not compensate for its lower throughput — fewer completions per minute means longer batch jobs overall. See our best GPU for LLM inference guide.
See also: Mixtral 8x7B vs Qwen 72B for Chatbot / Conversational AI for a related comparison.
See also: LLaMA 3 70B vs Mixtral 8x7B for Code Generation for a related comparison.
Cost Analysis
Mixtral’s 16 GB VRAM savings at INT4 can mean the difference between a single-GPU and dual-GPU setup, which roughly halves hardware cost.
| Cost Factor | Mixtral 8x7B | Qwen 72B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 26 GB | 42 GB |
| Est. Monthly Server Cost | £177 | £106 |
| Throughput Advantage | 14% faster | 2% cheaper/tok |
See the cost-per-million-tokens calculator for precise modelling.
Recommendation
Choose Mixtral 8x7B for most code generation tasks. It is faster, more accurate, and uses less memory. For IDE plugins, CI/CD automation, and batch code review, Mixtral is the straightforward pick.
Choose Qwen 72B only if your code generation tasks require processing inputs longer than 32K tokens — entire repository snapshots, for example — where Qwen’s 128K context window is a hard requirement.
Deploy on dedicated GPU servers for consistent code generation performance.
Deploy the Winner
Run Mixtral 8x7B or Qwen 72B on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
