Mistral 7B vs Gemma 2 9B for Code Generation: GPU Benchmark

Quick Verdict

Two points separate these models on HumanEval: Gemma 2 9B scores 61.2% pass@1 versus Mistral 7B’s 59.0%. That gap is narrow enough that the practical difference between them is not accuracy — it is everything else. Mistral 7B’s 32K context window sees four times more surrounding code than Gemma 2 9B’s 8K, which matters enormously for real-world code generation where functions depend on imports, types, and classes defined hundreds of lines away. On a dedicated GPU server, this architectural advantage makes Mistral 7B the more capable model for repository-scale code tasks, even if it loses on isolated benchmarks.

For broader model comparisons, see our GPU comparisons hub.

Specs Comparison

The 32K vs 8K context gap is the dominant spec difference for code generation. Real code files frequently exceed 8K tokens, and codebases certainly do. Mistral 7B can hold an entire file in context; Gemma 2 9B may need to work with truncated input on self-hosted infrastructure.

Mistral 7B’s Apache 2.0 licence also simplifies commercial code generation tool deployment. For detailed VRAM breakdowns, see our guides on Mistral 7B VRAM requirements and Gemma 2 9B VRAM requirements.

Code Generation Benchmark

We tested both models on an NVIDIA RTX 3090 (24 GB VRAM) using vLLM with INT4 quantisation. HumanEval tests isolated function generation — note that this benchmark does not exercise the context length advantage that would benefit Mistral 7B on real-world code tasks. For live speed data, check our tokens-per-second benchmark.

Mistral 7B delivers 53 completions per minute versus Gemma 2 9B’s 48 — a 10% throughput advantage that compounds in IDE integrations where developers trigger dozens of completions per session. Gemma 2 9B’s lower average latency (230 ms vs 317 ms) means each individual completion arrives faster, but the total volume per minute favours Mistral. Visit our best GPU for LLM inference guide for hardware-level comparisons.

See also: Mistral 7B vs Gemma 2 9B for Chatbot / Conversational AI for a related comparison.

See also: LLaMA 3 8B vs Mistral 7B for Code Generation for a related comparison.

Cost Analysis

Code generation costs are best measured per correct completion, not per token. At near-identical accuracy, the throughput difference directly determines cost efficiency on the same dedicated GPU server.

Factoring in both accuracy and throughput, the two models are remarkably close in cost per correct completion. The decision comes down to other factors. Use our cost-per-million-tokens calculator to model costs for your specific pipeline volume.

Recommendation

Choose Mistral 7B for code generation in large codebases where context matters — multi-file refactoring, whole-class generation, or any task where the model benefits from seeing 32K tokens of surrounding code. The 10% throughput advantage also makes it better suited for high-frequency IDE integrations where developers expect near-instant suggestions.

Choose Gemma 2 9B for isolated function generation, algorithm implementation, or code review tasks where the 8K context is sufficient and the 2.2-point accuracy edge on HumanEval translates into fewer broken completions. Gemma 2 9B’s cautious output style also produces code with more comments and better error handling by default.

Both models fit on a single RTX 3090 at INT4 quantisation. Deploy on dedicated GPU servers for consistent performance without noisy-neighbour issues.