For routing complex tasks across multiple AI models, I noticed that benchmark-based allocation (HLE, GPQA, IFBench, etc.) only explains part of the story. Each model defaults to a distinct cognitive register when handed the same task. Therefore, have this guide.
Claude Opus 4.7 is the lead and coordinator. Highest non-hallucination rate and 1M context. Methodology judgment, long-document reading, integration, final draft.
Codex (GPT-5.5) is the hard-reasoning specialist. Highest raw reasoning scores, most surgical precision in rewrites and cross-section consistency. Never let it independently produce factual claims.
Gemini 3.1 Pro is the knowledge anchor. Highest Omniscience and IFBench. Default first-stop for knowledge queries, figure extraction, plus reviewer-mind sniff tests.
Kimi K2.6 is the interactive ping-pong specialist with Sinophone audit. About 1 second first-chunk latency. Principle-and-category thinking, non-Western prior-work coverage.
Sent the guide to your main AI, then it dispatches all three external models in parallel, then has the lead integrate by using Kimi’s principle to structure the rewrite, Codex’s phrasing for the actual text, and Gemini’s heuristic for the final sniff test. Hard stop-doing rules cover the failure modes (Codex hallucinations, Gemini style-drift, Kimi context truncation, Claude format-drift).