The Race for the Fastest Model Is a Trap for Debugging Work
90% of the time, the faster model does the same job.
It's the other 10% that eats your afternoon.
The shallow loop
You're debugging a test failure that touches three services. The model responds in seconds. It suggests a fix based on the error message. You apply it. The test still fails, but now with a different error.
You prompt again. Another fast response. Another surface-level suggestion. The model is answering quickly, but it's not investigating. It's pattern-matching from the stack trace without checking what the code actually does.
This is the trap: speed feels like progress. But for debugging work, speed without depth is just faster wrong answers.
What "slow" actually means
GPT-5 Codex takes longer to respond because it's doing something different. Instead of pattern-matching from the prompt, it uses tools: codebase indexing to find references, file reads to understand context, and actual reasoning through the connections.
"GPT-5 on medium reasoning is just the opposite. Like, you ask a question, it's going to use every tool it has, mostly codebase indexing, to actually answer and give you references."
Dan,
The difference isn't capability. It's methodology. A model that takes 30 seconds to find the actual dependency chain is doing different work than a model that takes 3 seconds to guess based on the error message.
The 90/10 tradeoff
This isn't "always use the slow model." The tradeoff is real, and it's favorable for speed most of the time.
"Yes, it's faster and 90% of the time it'll do the same job faster, but that 10% is what matters to me. I'll just wait."
GosuCoder,
For straightforward tasks, the fast model works fine. Renaming a variable, writing a test for a pure function, generating boilerplate: these don't require deep investigation. The failure mode is obvious and the fix is quick.
But debugging is different. Debugging means the failure mode isn't obvious. That's the whole problem. When you need a model to trace connections across files, understand implicit dependencies, and reason through why something that should work doesn't, speed is the wrong optimization.
The service OpenAI did
The pressure to optimize for time-to-first-token is real. Users want fast responses. Benchmarks reward latency. But for agentic coding work, caving to that pressure would mean trading investigation depth for response speed.
"This whole race for the fastest model: I know people are like, I want it fast, I want it fast. But I feel that OpenAI has done a service to us by not caving and actually letting the model do what it needs to do in their Codex version."
GosuCoder,
The service is refusing to optimize for the wrong metric. A model that takes time to use its tools, that actually indexes the codebase and finds references before answering, is doing the work that matters for debugging.
Why this matters for your team
For a Series A to C team shipping production code, that 10% isn't just inconvenient. It's where bugs become incidents.
The issues that eat an afternoon, the ones where three developers end up in a Slack thread debugging the same thing, are rarely the obvious ones. They're the interconnected ones. The ones where the root cause is three files away from the symptom. The ones where the model needs to actually investigate instead of pattern-match.
If your tooling is optimized purely for speed, you'll get fast responses for the 90% of tasks that didn't need help anyway. And you'll get confident, shallow, wrong responses for the 10% that actually mattered.
The practical move
Accept slower time-to-response for tasks that require genuine investigation. When you're debugging something complex, switch to a model that uses its tools instead of just pattern-matching.
The wait is part of the work. A model that takes 30 seconds to find the actual answer is still faster than three rounds of wrong guesses.
How Roo Code closes the loop on debugging
Roo Code is designed for exactly this tradeoff. Because it closes the loop - proposing changes, running tests, and iterating based on results - the agent can actually investigate rather than just respond. When debugging fails the first time, Roo Code sees the new error, reads the relevant files, and tries again with real context.
With BYOK (bring your own key), you choose the model that fits the task. Use a fast model for boilerplate. Switch to a reasoning-heavy model when you're three hours into a bug that touches multiple services. The agent adapts to your decision without locking you into one speed-versus-depth tradeoff.
Roo Code lets developers match model depth to task complexity, so debugging work gets investigation instead of guesses.
Fast models vs. investigation-capable models
| Dimension | Fast model approach | Investigation-capable approach |
|---|---|---|
| Response time | 2-5 seconds | 20-60 seconds |
| Method | Pattern-match from error message | Index codebase, read files, trace dependencies |
| Best for | Boilerplate, renames, simple refactors | Multi-file bugs, implicit dependencies, root cause analysis |
| Failure mode | Confident wrong answers | Slower but grounded answers |
| Net time cost | Fast per response, but compounds with wrong guesses | Slower per response, but fewer iterations needed |
Frequently asked questions
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