Which LLM should review your code?

Qodo evaluated 13 AI models across 38 model and reasoning-level configurations for code review using a proprietary benchmark of 125 real pull requests containing 703 bugs that were later discovered and fixed. The assessment measured precision, recall, F1 score, backtracked-bug recall, cost per pull request, and median review time while holding the review setup constant. Claude Opus 5.5 achieved the highest listed F1 score at 56.8%, closely followed by Claude Sonnet 5.5, Claude Fable 5.1, and the open-weight GLM 5.3, while several other models formed a competitive second tier. Results indicate that higher reasoning settings can improve bug recall but often reduce precision, increase costs, and lengthen review times, making the optimal setting model- and priority-dependent. Cost varied by more than 100-fold across configurations, and open-weight models such as GLM 5.3, DeepSeek V4 Pro, and Kimi K3 performed competitively with proprietary alternatives. Qodo says it continuously reruns the benchmark for new releases and uses the findings to select production review models, offering customers provider flexibility and adaptive choices based on desired trade-offs among coverage, confidence, cost, and speed.

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