devstral vs GPT-4o for Real Coding Tasks: An Honest Comparison From Someone Who Uses Both Daily
devstral vs GPT-4o honest comparison for real coding tasks
Specific task categories. Here's what that actually involved.
The Setup
Latency comparison
What I Built
Specific task categories
Specific task categories. This is where most implementations go wrong — not in the concept, but in the specifics that documentation tends to gloss over. The version that's actually running in production looks different from what the README describes.
Latency comparison
Latency comparison. This is where most implementations go wrong — not in the concept, but in the specifics that documentation tends to gloss over. The version that's actually running in production looks different from what the README describes.
Code quality examples
Code quality examples. This is where most implementations go wrong — not in the concept, but in the specifics that documentation tends to gloss over. The version that's actually running in production looks different from what the README describes.
What Broke First
When to use which. This is the part that took the most time to figure out — not the implementation itself, but the failure mode you only encounter at the wrong moment. The fix is in the tooling, not the concept.
What's Running in Production
The stack: ai, llm, devstral, gpt4o. Monitoring via Zabbix with a custom alert template. The rule I apply to everything in production: if it can't self-report a failure, it doesn't go in.
Key Takeaways
- Specific — specific task categories
- Latency — latency comparison
- Code — code quality examples
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Live Life Automated covers the broader philosophy behind how I think about automation and building systems that don't require constant babysitting. Available at mfitz.net.
