DeepSeek V4 Pro edges out GPT-5.5 Pro 38-33 in precision benchmark

A head-to-head comparison scored DeepSeek V4 Pro at 38.0 against GPT-5.5 Pro’s 33.0, with the gap traced to disciplined adherence to constraints rather than raw capability. DeepSeek consistently produced tighter, more literal output, while GPT-5.5 Pro showed a tendency to improvise beyond what the prompt asked for.

The standout example was a Python log redactor task involving overlapping regex patterns. DeepSeek used a single regex with a unified replacer, correctly prioritizing matches and avoiding dropped cases. GPT-5.5 Pro split the work across multiple separate regexes, an approach that introduces ordering bugs and missed matches when patterns overlap.

The takeaway is that precision-oriented benchmarks reward models that resist the urge to elaborate. For tasks where correctness depends on respecting exact constraints, a more conservative model can outperform a more creative one — a useful signal for developers picking models for deterministic engineering work like data sanitization, parsing, or codegen under strict specifications.