The top 10 models our router actually uses, and what each one is for

Over a 30-day period, Weave routed about 235,000 requests across roughly 40 AI models using complexity clusters—fast, balanced, high, and maximum—alongside hard-pinned utility tasks, user-selected models, and legacy routing rules. DeepSeek-V4-Flash handled the largest share of requests by serving inexpensive fast tasks, subagent bootstraps, and tool-result follow-ups, while Gemini 3.1 Flash Lite was used exclusively for pinned classification, title generation, and health checks. Minimax-M3 dominated routine balanced tasks under a newer XGBoost-based selector, whereas Claude Sonnet 5 led high-complexity coding work and consumed the most tokens. The maximum tier, including Claude Opus 5, Claude Fable 5, GPT-5.5, and GPT-5.6-Sol, handled fewer but more costly and often longer-running sessions, with premium models retaining users across many turns. The data shows a large cost imbalance: the cheapest models processed about 38% of requests for roughly 1% of spending, while Claude models represented about 89% of total spend despite handling about 38% of requests, partly offset by higher cache-read rates. Model usage changes quickly as models are introduced, replaced, or gain learned routing preference, illustrating that the router continuously adapts rather than relying on fixed assignments.

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