Grok 4.3 (Venice) vs Llama 3.2 3B (local)
On the 5 use cases both models ran, Grok 4.3 (Venice) won 5 to 0 (better outcome first, then lower cost).
Pass rate: Grok 4.3 (Venice) 80% (5 runs) vs Llama 3.2 3B (local) 0% (5 runs). Average cost per run: $0.0099 vs $0 (local).
| Metric | Grok 4.3 (Venice) | Llama 3.2 3B (local) |
|---|---|---|
| Runs on shared use cases | 5 | 5 |
| Pass rate | 80% (4/5) | 0% (0/5) |
| Avg cost per run | $0.0099 | $0 (local) |
| Avg wall time | 18s | 9s |
| Step-ins per run | 0.00 | 0.00 |
| Use cases won | 5 | 0 |
Use case by use case
- Build a 4-page bakery-cafe website (HTML/CSS): Grok 4.3 (Venice) better (pass 100% vs 0%)
- Build a landing page from extracted design tokens: Grok 4.3 (Venice) better (pass 0% vs 0%)
- Diagnose why GitHub Actions stopped running: Grok 4.3 (Venice) better (pass 100% vs 0%)
- Implement 5 Python functions from a task list: Grok 4.3 (Venice) better (pass 100% vs 0%)
- Reverse engineer a security patch for an exploited CVE: Grok 4.3 (Venice) better (pass 100% vs 0%)
Only use cases both models ran are compared; all settings of each model are grouped. How we score.