DeepSeek V4.1 Flash (Venice) vs Llama 3.2 3B (local)
On the 8 use cases both models ran, DeepSeek V4.1 Flash (Venice) won 8 to 0 (better outcome first, then lower cost).
Pass rate: DeepSeek V4.1 Flash (Venice) 100% (8 runs) vs Llama 3.2 3B (local) 0% (8 runs). Average cost per run: $0.01 vs $0 (local).
| Metric | DeepSeek V4.1 Flash (Venice) | Llama 3.2 3B (local) |
|---|---|---|
| Runs on shared use cases | 8 | 8 |
| Pass rate | 100% (8/8) | 0% (0/8) |
| Avg cost per run | $0.01 | $0 (local) |
| Avg wall time | 2m | 8s |
| Step-ins per run | 1.00 | 0.00 |
| Use cases won | 8 | 0 |
Use case by use case
- Build a 4-page bakery-cafe website (HTML/CSS): DeepSeek V4.1 Flash (Venice) better (pass 100% vs 0%)
- Build a landing page from extracted design tokens: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 0%)
- Carry out a customer data-erasure request without erasing a namesake: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 0%)
- Diagnose why GitHub Actions stopped running: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 0%)
- Implement 5 Python functions from a task list: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 0%)
- Rename a data field across a codebase with hidden coupling and a partner-facing export: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 0%)
- Reverse engineer a security patch for an exploited CVE: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 0%)
- Roll out catalog price changes with missing categories and sign-off for big increases: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 0%)
Only use cases both models ran are compared; all settings of each model are grouped. How we score.