Gemma 4 31B Instruct (Venice) vs Llama 3.2 3B (local)
On the 6 use cases both models ran, Gemma 4 31B Instruct (Venice) won 6 to 0 (better outcome first, then lower cost).
Pass rate: Gemma 4 31B Instruct (Venice) 83% (6 runs) vs Llama 3.2 3B (local) 0% (6 runs). Average cost per run: $0.0042 vs $0 (local).
| Metric | Gemma 4 31B Instruct (Venice) | Llama 3.2 3B (local) |
|---|---|---|
| Runs on shared use cases | 6 | 6 |
| Pass rate | 83% (5/6) | 0% (0/6) |
| Avg cost per run | $0.0042 | $0 (local) |
| Avg wall time | 2m | 10s |
| Step-ins per run | 0.50 | 0.00 |
| Use cases won | 6 | 0 |
Use case by use case
- Build a 4-page bakery-cafe website (HTML/CSS): Gemma 4 31B Instruct (Venice) better (pass 100% vs 0%)
- Build a landing page from extracted design tokens: Gemma 4 31B Instruct (Venice) better (pass 100% vs 0%)
- Diagnose why GitHub Actions stopped running: Gemma 4 31B Instruct (Venice) better (pass 100% vs 0%)
- Implement 5 Python functions from a task list: Gemma 4 31B Instruct (Venice) better (pass 100% vs 0%)
- Report weekly active users when 'active' and 'week' are still undefined: Gemma 4 31B Instruct (Venice) better (pass 0% vs 0%)
- Reverse engineer a security patch for an exploited CVE: Gemma 4 31B Instruct (Venice) better (pass 100% vs 0%)
Only use cases both models ran are compared; all settings of each model are grouped. How we score.