At half the parameter count, Phi-3 Mini still manages to beat Mistral 7B on HumanEval. That is the headline. But code generation is not just about pass@1 scores — developer productivity depends on how fast suggestions arrive and how many you can generate per minute. We dug into the full picture on dedicated GPU servers.
Specs at a Glance
| Specification | Mistral 7B | Phi-3 Mini |
|---|---|---|
| Parameters | 7B | 3.8B |
| Architecture | Dense Transformer + SWA | Dense Transformer |
| Context Length | 32K | 128K |
| VRAM (FP16) | 14.5 GB | 7.6 GB |
| VRAM (INT4) | 5.5 GB | 3.2 GB |
| Licence | Apache 2.0 | MIT |
Phi-3 Mini’s 128K context lets it hold an entire codebase module in context while using less than half the VRAM. That is a compelling combination for code tasks. Memory planning: Mistral VRAM | Phi-3 VRAM.
Code Generation Results
RTX 3090, vLLM, INT4, continuous batching. Task mix: function completion, refactoring suggestions, and docstring-to-code in Python and JavaScript. Live metrics: tokens-per-second benchmark.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| Mistral 7B | 49.3% | 50 | 207 | 5.5 GB |
| Phi-3 Mini | 52.7% | 26 | 300 | 3.2 GB |
Phi-3 Mini edges ahead on accuracy (52.7% vs 49.3%), meaning it writes correct code slightly more often. But Mistral nearly doubles the completions per minute (50 vs 26) and delivers each one 45% faster (207 ms vs 300 ms). The throughput gap is stark: a team of 20 developers sharing a Mistral instance will rarely wait, while Phi-3 could bottleneck during peak hours.
Related: Mistral vs Phi-3 for Chatbots | LLaMA 3 vs Mistral for Code Gen
Cost Comparison
| Cost Factor | Mistral 7B | Phi-3 Mini |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 5.5 GB | 3.2 GB |
| Est. Monthly Server Cost | £113 | £110 |
| Throughput Advantage | 14% faster | 5% cheaper/tok |
Same hardware, similar monthly spend. The cost calculator shows the real difference at scale: cost-per-million-tokens.
The Verdict
Mistral 7B for team-facing code assistants. When multiple developers share an endpoint, the 50 completions/min throughput ensures nobody waits. The 3.4-point accuracy gap versus Phi-3 is unlikely to matter for tab-completion workflows where developers review every suggestion anyway.
Phi-3 Mini for accuracy-first pipelines. If you are running automated code generation in a CI/CD pipeline where each suggestion must compile and pass tests, Phi-3’s higher pass@1 reduces failed builds. Its tiny footprint also means you can deploy it on a budget GPU and still have headroom.
Deploy on dedicated GPU servers. Hardware advice: best GPU for LLM inference.
Self-Host Your Code Model
Run Mistral 7B or Phi-3 Mini on bare-metal GPUs — zero per-completion fees, full root access, instant deployment.
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