How to choose an LLM for debugging
A practical guide to debugging, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.
What to optimize for in debugging
The right model for debugging is not simply the one with the highest average score. Start by writing down the task's failure modes: wrong answers, missed instructions, slow responses, high unit cost or weak handling of long inputs. Each failure mode points to a different benchmark and a different price tier.
For debugging, a mid-range model with a large context window and strong instruction-following often beats a frontier model that is ten times more expensive. Use the index to set a shortlist, then test the shortlist on real examples from your workflow.
How to shortlist models
A practical shortlist has three slots: a quality candidate, a value candidate and a fallback. The quality candidate protects the hard cases. The value candidate handles volume. The fallback keeps the product alive if a provider has an outage or changes its price.
- Filter by context window first if your inputs are long.
- Check the relevant benchmark, not just the overall average.
- Compare blended 3:1 cost, not only the headline input price.
- Confirm licence and data-handling terms before testing.
- Run a fixed evaluation set through each candidate.
What the index says
The strongest candidates for debugging in this index are GPT-5.2 Pro, GPT-5.2 Max, Claude Opus 4.5 Pro. Their input prices range from $19.42 to $21.39 per million tokens, so the quality gap has to be real to justify the top of that range.
If cost is the binding constraint, start with Claude Opus 4.5 at $19.42 per million input tokens and test whether it clears your quality bar. If it does, you have room to spend more on retrieval or evaluation instead of tokens.
Common mistakes
- Choosing on a single benchmark score without checking the task shape.
- Ignoring output price, which often dominates long-form workloads.
- Assuming a large context window means the model uses it well.
- Skipping a fallback provider until the first outage.
Top debugging candidates in the index
| Model | Provider | Input / 1M | Context | Avg score |
|---|---|---|---|---|
| GPT-5.2 Pro | OpenAI | $21.32 | 2,000,000 | 89.6 |
| GPT-5.2 Max | OpenAI | $21.39 | 1,000,000 | 89.2 |
| Claude Opus 4.5 Pro | Anthropic | $20.98 | 200,000 | 89.1 |
| Grok 4 Reasoning | xAI | $19.67 | 2,000,000 | 87.8 |
| Claude Opus 4.5 | Anthropic | $19.42 | 400,000 | 87.7 |
Related
FAQ
What is the most important factor in debugging?
Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.
How many models should I evaluate?
Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.
How do I control cost?
Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.