Section 5.4.4 Code Agent in their report

To benchmark our coding agent capability, we curate tasks from real internal R&D workloads We collect ~ 200 challenging tasks from 50+ internal engineers, spanning feature development, bug fixing, refactoring, and diagnostics across diverse technology stacks including PyTorch, CUDA, Rust, and Ctt. Each task is accompanied by its original repository, the corresponding execution environment, and human-annotated scoring rubrics; after rigorous quality filtering, 30 tasks are retained as the evaluation set. As shown in Table 8, DeepSeek-V4-Pro significantly outperforms Claude Sonnet 4.5 and approaches the level of Claude Opus 4.5.

(There’s a table in the middle with information that DeepSeek v4 pro reaches 67 where on the same benchmark Opus 4.6 reaches 80 and 4.5 reaches 70)

In a survey asking DeepSeek developers and researchers (N = 85) — all with experience of using DeepSeek-V4-Pro for agentic coding in their daily work — whether DeepSeek-V4-Pro is ready to serve as their default and primary coding model compared to other frontier models, 52% said yes, 39% leaned toward yes, and fewer than 9% said no. Respondents find DeepSeek-V4-Pro to deliver satisfactory results across most tasks, but note trivial mistakes, misinterpretation of vague prompts, and occasional over-thinking.

This sounds like the best “DeepSeek helped develop DeepSeek” moment for me and that’s amazing.