Weak-Model Critic-Comparator
GPT-5.4 nano wrapped in a critic-comparator orchestration loop reaches 76.4% on SWE-bench Verified, matching standalone Gemini 3 Pro and Claude Opus 4.5 Thinking. The trick is to sample k=8 candidate patches from the weak model and select the winner using execution and proof signals rather than asking the model to self-rank.
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k=8 candidates plus verifier beats frontier model: A weak model's top-k often already contains a correct patch. The selector is the limiting factor, not the base model's capability.
Execution and proof signals as selection: Candidates are run and verified rather than scored by an LLM judge. The critic and comparator are separate roles inside the loop, each with a narrow task.
Matches frontier performance at lower per-call cost: Selecting among nano-tier proposals is cheaper than calling a frontier model once, even after accounting for the 8x sampling, because the dominant cost driver is model size rather than call count.
Why it matters: This is a reproducible recipe for getting frontier-level coding-agent results out of cheaper models. The result also reframes where SWE-bench progress is coming from: orchestration quality, not just stronger base models.