Gemma 4 31B Instruct (Venice) vs GLM 5.3 (Venice)
On the 9 use cases both models ran, Gemma 4 31B Instruct (Venice) won 9 to 0 (better outcome first, then lower cost).
Pass rate: Gemma 4 31B Instruct (Venice) 100% (9 runs) vs GLM 5.3 (Venice) 89% (9 runs). Average cost per run: $0.0023 vs $0.05.
| Metric | Gemma 4 31B Instruct (Venice) | GLM 5.3 (Venice) |
|---|---|---|
| Runs on shared use cases | 9 | 9 |
| Pass rate | 100% (9/9) | 89% (8/9) |
| Avg cost per run | $0.0023 | $0.05 |
| Avg wall time | 2m | 1m |
| Step-ins per run | 0.00 | 0.00 |
| Use cases won | 9 | 0 |
Use case by use case
- Analyze five months of AI coding-agent session logs: Gemma 4 31B Instruct (Venice) better (pass 100% vs 100%)
- Build a landing page from extracted design tokens: Gemma 4 31B Instruct (Venice) better (pass 100% vs 100%)
- Diagnose why GitHub Actions stopped running: Gemma 4 31B Instruct (Venice) better (pass 100% vs 100%)
- Fill and submit job applications from a resume: Gemma 4 31B Instruct (Venice) better (pass 100% vs 100%)
- Fix three bugs in a Python repo so its tests pass: Gemma 4 31B Instruct (Venice) better (pass 100% vs 100%)
- Implement a spec exactly, with nothing extra: Gemma 4 31B Instruct (Venice) better (pass 100% vs 100%)
- Let agents run growth for a meeting-notetaker app: Gemma 4 31B Instruct (Venice) better (pass 100% vs 100%)
- One-shot a Minecraft-style voxel game in three.js: Gemma 4 31B Instruct (Venice) better (pass 100% vs 0%)
- Research use cases and build pitch slides: Gemma 4 31B Instruct (Venice) better (pass 100% vs 100%)
Only use cases both models ran are compared; all settings of each model are grouped. How we score.