DeepSWE Benchmark 2026: Which LLMs Write the Best Code

DeepSWE, a contamination-free coding benchmark developed by Datacurve and released in May 2026, evaluates frontier large language models (LLMs) through 113 software engineering tasks across 91 repositories and five programming languages. Unlike other benchmarks, DeepSWE emphasizes contamination control by ensuring tasks are original and not derived from existing codebases, which helps prevent models from benefiting from prior exposure. The benchmark highlights the performance disparities among models, with Claude Fable 5 leading at a 70% pass rate but with high costs, while GPT-5.5 offers a similar performance at a significantly lower cost, making it the best value option. DeepSWE's rigorous testing environment, which focuses on real engineering tasks and behavior-based verifiers, provides clearer distinctions between models than previous benchmarks like SWE-bench Pro, which often showed overlapping confidence intervals. The benchmark exposes the varying capabilities and cost-effectiveness of each model, underlining the importance of selecting the right LLM based on task requirements and leveraging tools like Eden AI, which allow seamless transitions between different providers to optimize performance and cost.

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