GPT-5.4, Claude, Gemini split tasks

This is the most useful way to think about frontier models right now: not “best overall,” but “best for the job” — with the big caveat that many benchmark claims still come from different labs, harnesses, and reporting styles.

• –Claude’s coding lead holds up in both Anthropic’s launch materials and third-party comparisons, which is why it still feels like the safe default for serious software engineering workflows
• –Gemini’s case is strongest on reasoning breadth, long context, and price-performance, making it especially attractive for research-heavy pipelines and large-document analysis
• –GPT-5.4’s differentiator is less chat quality than operational behavior: computer use, multi-step task execution, and tool-driven workflows are where it appears to pull ahead
• –Developers should resist overfitting to tiny benchmark deltas, because SWE-Bench, GPQA, OSWorld, and vendor-specific evals measure very different things
• –The practical winning strategy is multi-model routing, not loyalty to a single flagship