Best Model for Debugging: Judge It on How It Handles Being Wrong

Ask a model to write a function and it either works or it does not. Ask a model to debug and you have started a search, in which the model will be wrong several times before it is right. What it does during those wrong turns determines whether the session takes four minutes or forty.

This is why the model that feels best at writing code is often not the one you want on a bug. Generation rewards confidence. Debugging punishes it.

Debugging is a search problem in disguise

A bug report gives you a symptom. The cause is somewhere in a space of possible explanations, and your job is to shrink that space. Every good debugging move is an experiment that eliminates candidates: add a log line, check an assumption, bisect the commit range, reproduce with a smaller input.

Models trained to be helpful have a strong pull towards skipping this. Given a stack trace, the immediate instinct is to propose a fix. Sometimes the instinct is right, and when it is, you save time. When it is wrong, you have now edited code based on a guess, and the codebase has drifted from the state where the bug was reproducible.

The behaviour you want is unglamorous: read the error properly, state a hypothesis, propose the cheapest experiment that would falsify it, and only then edit.

The three failure modes to watch for

Fixing the symptom. The null check that makes the exception go away is not a fix if the value was never supposed to be null. Models do this constantly because it satisfies the literal request — the error is gone — while leaving the actual defect in place, now silent.

Hypothesis lock-in. The model decides in turn one that the problem is a race condition. Turn two disproves it. Turn three restates the race condition theory with more confidence and different words. This is the single most expensive failure in an agent loop, because it burns tokens and wall-clock time without shrinking the search space at all.

Shotgun editing. Unable to isolate the cause, the model changes five things at once. If the symptom disappears you have learned nothing about why, and you now have four unexplained changes in your diff.

All three are recovery-behaviour problems. None of them show up on a coding benchmark, because benchmarks score the final artefact rather than the path taken to it.

What separates models on this task

Two capability axes matter, and they pull in different directions.

The first is reading comprehension under noise. A production stack trace is mostly framework frames, and the useful line is somewhere in the middle. Long-context models have an advantage here simply because you can paste the entire trace, the relevant source files and the last hundred lines of logs without triaging first. Kimi K3, GLM 5.2 and DeepSeek V4 Pro all offer a one-million-token context, which in practice means you stop deciding what to leave out.

The second is logical rigour — the ability to reason precisely about state, ordering and arithmetic. This is where the algorithm-and-maths-leaning models earn their place. DeepSeek V4 Pro, a 1.6T mixture-of-experts model with 49B active parameters under an MIT licence, sits around 44 on the Artificial Analysis Intelligence Index — below Kimi K3 at roughly 57 — but its reported strengths are concentrated in algorithms, maths and STEM reasoning, and it is notably cheaper per token. For a bug that comes down to an off-by-one, a floating-point comparison or an incorrect invariant, that profile is a good match and the price difference means you can afford to let it think for longer.

For bugs that come down to "this framework does not work the way you assume", breadth of knowledge matters more than rigour, and the higher-index models tend to win.

Build a debugging eval from your own bug tracker

This is the easiest eval in software, because your issue tracker is already a labelled dataset. Every closed bug has a symptom, a fixing commit and, if you are lucky, a comment explaining the cause.

Pick eight closed bugs of varying difficulty. Check out the commit before each fix. Give the model exactly what the original reporter gave you — no more. Then measure:

• Turns to root cause. Not turns to a passing test. Root cause, as judged against the fix commit. This is the headline number.
• Hypothesis diversity. Count distinct theories proposed. A model that produced three different theories across five turns is searching. One that produced one theory five times is stuck.
• Symptom-fix rate. How often did it produce something that makes the test pass without addressing the cause? Read the diff to judge this; the test suite will not tell you.
• Instrumentation instinct. Did it ever ask for more information — a log line, a value dump, a reproduction step — rather than guessing? Models that do this are markedly better on hard bugs and slightly slower on trivial ones.

Score the hard bugs separately from the easy ones. Nearly all models solve the easy ones, so averaging hides the difference you are trying to detect.

Set the loop up so the model can actually debug

Model choice is roughly half the outcome. The other half is whether your harness lets it run experiments.

A model with terminal access that can run the failing test, add a print statement, run it again and read the output is doing real debugging. A model that can only read files and propose diffs is doing informed guesswork, and no amount of capability fixes that.

Three things reliably improve results regardless of model:

1. Give it a reproduction. A failing test is worth more than any amount of prose describing the bug.
2. Ask for the hypothesis first. Requiring it to state what it thinks is wrong, and what evidence would disprove that, before touching code makes lock-in visible immediately.
3. Reset rather than continue. Once a session has three failed theories in its context, the context itself is now anchoring the model. Starting fresh with a summary of what has been ruled out works better than pushing on.

The takeaway

For debugging, rank candidates by turns to root cause on your own historical bugs, not by index scores. Prefer a model that asks for evidence over one that answers immediately. Give it the ability to run the failing test. And when a session stalls after three wrong theories, restart it with the eliminated candidates listed — that single habit saves more time than switching models.