DeepSeek V4.1 Flash (Venice) vs GLM 5.3 (Venice)
On the 9 use cases both models ran, DeepSeek V4.1 Flash (Venice) won 9 to 0 (better outcome first, then lower cost).
Pass rate: DeepSeek V4.1 Flash (Venice) 90% (10 runs) vs GLM 5.3 (Venice) 89% (9 runs). Average cost per run: $0.01 vs $0.05.
| Metric | DeepSeek V4.1 Flash (Venice) | GLM 5.3 (Venice) |
|---|---|---|
| Runs on shared use cases | 10 | 9 |
| Pass rate | 90% (9/10) | 89% (8/9) |
| Avg cost per run | $0.01 | $0.05 |
| Avg wall time | 1m | 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: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 100%)
- Build a landing page from extracted design tokens: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 100%)
- Diagnose why GitHub Actions stopped running: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 100%)
- Fill and submit job applications from a resume: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 100%)
- Fix three bugs in a Python repo so its tests pass: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 100%)
- Implement a spec exactly, with nothing extra: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 100%)
- Let agents run growth for a meeting-notetaker app: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 100%)
- One-shot a Minecraft-style voxel game in three.js: DeepSeek V4.1 Flash (Venice) better (pass 50% vs 0%)
- Research use cases and build pitch slides: DeepSeek V4.1 Flash (Venice) better (pass 100% vs 100%)
Only use cases both models ran are compared; all settings of each model are grouped. How we score.