The per-task
AI routing guide

Take a hypothetical 100,000-query/month workload. Each model has a different per-query cost (driven by token pricing). Each task category has a different optimal model.

Strategy A — always use Claude Opus. Claude wins 27.9% of our benchmark queries; for the other 72.1% you're paying premium prices for a non-winning answer. Estimated monthly cost: $1,140 (at Q2 2026 Anthropic rates for ~500-token responses).

Strategy B — always use ChatGPT. Similar story — wins 28.7% of queries but you pay GPT-5.5 prices for the 71.3% where it isn't optimal. Estimated monthly cost: $980.

Strategy C — per-task routing. Use the table above. For each task, the best model runs. For high-volume low-stakes batch work, Grok runs at one-half the price. Estimated monthly cost: ~$590 with quality going UP, not down, on the categories where the per-task winner differs from your default.

The routing strategy costs about half as much AND delivers measurably better answers on most categories. The only reason most teams don't do it is because manually picking a different model for each query is annoying. The fix is automation — letting a router send each query to the right model automatically.

If you're in a regulated industry. The cost of an error is higher than the cost of always running a verifier. Use Auto-Verification on every query, regardless of the routing winner. The 2-4 second latency tax is irrelevant against the cost of a published hallucination.

If your team has standardized on one model. The friction of switching may outweigh the marginal quality gain. In that case, pick the model that wins the most of your most common tasks, and use it everywhere. For typical knowledge-work teams, that's usually ChatGPT or Claude.

If you're processing personal data. Data residency, privacy and contract terms become the routing constraint, not capability. Pick the model whose data handling matches your compliance posture.

If you're optimizing for latency, not quality. Grok and Gemini Flash are the fastest. The strongest models (Opus, GPT-5.5 thinking, Gemini Pro) are slower. For real-time UX, the quality-optimal model may not be the latency-optimal one.