Here is a number that should settle most debates: LLaMA 3 8B scores 68.1% on HumanEval pass@1 while Mistral 7B manages 52.6%. That is a 15.5-point gap — not a rounding error, but a genuine generational improvement in code generation capability. LLaMA also produces completions 41% faster. So why would anyone still choose Mistral for code?
The Numbers That Matter
Tested on an RTX 3090, vLLM, INT4 quantisation, continuous batching. Live benchmark data here.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 68.1% | 55 | 205 | 6.5 GB |
| Mistral 7B | 52.6% | 39 | 342 | 5.5 GB |
LLaMA dominates on every metric. Faster completions, better accuracy, lower latency. The only column where Mistral shows a lead is VRAM usage at 5.5 GB versus 6.5 GB. That one-gigabyte difference rarely changes deployment decisions.
Architecture Comparison
| Specification | LLaMA 3 8B | Mistral 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Architecture | Dense Transformer | Dense Transformer + SWA |
| Context Length | 8K | 32K |
| VRAM (FP16) | 16 GB | 14.5 GB |
| VRAM (INT4) | 6.5 GB | 5.5 GB |
| Licence | Meta Community | Apache 2.0 |
Mistral’s 32K context window is technically useful for processing entire files, but for typical code completion tasks (function bodies, class methods, short scripts), 8K is more than sufficient. The sliding window attention that gives Mistral its efficiency advantage actually works against it in code generation — it can lose track of imports and type definitions declared far above the cursor position. Details in the LLaMA VRAM guide and Mistral VRAM guide.
The Cost Equation
| Cost Factor | LLaMA 3 8B | Mistral 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 5.5 GB |
| Est. Monthly Server Cost | £131 | £105 |
| Throughput Advantage | 11% faster | 5% cheaper/tok |
Same GPU, similar monthly cost. LLaMA’s throughput advantage means each pound buys more completions. Check the cost calculator for precise per-token economics at your volume.
The Honest Answer
LLaMA 3 8B is the clear winner for code generation. The accuracy lead is too large to argue with, and it is faster too. Unless your legal team specifically requires Apache 2.0 licensing and cannot work with Meta’s community licence, there is no technical reason to choose Mistral for this workload. See the best GPU for inference guide for hardware planning.
The one scenario where Mistral remains interesting: if you need to process very long code files (beyond 8K tokens) in a single context window. Mistral’s 32K limit handles that without chunking. For standard IDE completions, CI/CD integrations, and code review automation, LLaMA is the pick. More options in the comparison index.
See also: LLaMA 3 vs Mistral for Chatbots | LLaMA 3 vs DeepSeek for Code Generation
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