Verification loop quadruples DeepSeek's performance, matching Opus at one‑seventh the cost

Researchers examined the impact of a verification loop on the DeepSeek coding agent. The loop increased DeepSeek's measured intelligence by a factor of four. After the boost, DeepSeek's performance matched that of the Opus model. The enhanced DeepSeek achieved this parity while using only one‑seventh of the computational cost. The cost reduction stems from the loop’s ability to reuse verification steps efficiently. This result highlights the potential of verification loops to improve AI efficiency. It suggests that lower‑cost models can compete with larger, more expensive systems. Future work may explore scaling the technique to other AI agents.