GPT-5.6 Tops DeepSWE Leaderboard with Superior Cost-Efficiency

The latest DeepSWE 1.1 benchmark results reveal that OpenAI's GPT-5.6 model family has achieved a significant breakthrough in the realm of coding agents. It not only took the top spot on the leaderboard in absolute performance but also established a lead in operational costs and efficiency. This performance has sparked widespread attention in the AI community, signaling that the competition among large language models for coding tasks has shifted from mere benchmark scores to overall price-to-performance ratios.

Key Performance and Cost Details

According to test data, GPT-5.6 Sol scored around 72% to 73% on the DeepSWE benchmark, surpassing Fable 5's best score of approximately 70%. In terms of cost control, the average cost per task for GPT-5.6 Sol is about $8.4, whereas Claude/Fable-5's cost per task ranges from $13 to $22. Furthermore, the GPT-5.6 Sol max and xhigh versions managed to achieve fewer output tokens and agent steps while maintaining lower costs.

Reactions and Evaluations

Several industry observers have highly praised GPT-5.6's performance. Authors such as @MatthewBerman and @scaling01 pointed out that GPT-5.6 Sol is considered by external reviewers to be one of the best models for price/performance ratio. @rohanpaulai and @danielmac8 believe that the model has achieved a comprehensive breakthrough in capability, efficiency, and cost in agentic coding. A viewpoint reposted by @soumitrashukla9 also emphasized that instead of obsessing over benchmark scores, the cost reduction and efficiency gains brought by GPT-5.6 in practical applications are what truly matter. Additionally, it was officially noted that the Terra and Luna versions of the GPT-5.6 family also performed excellently.

2026-07-10 ~ 2026-07-11 · 11 related posts

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