Multi Model Workflows

Multi-model workflows

Models differ in speed, cost, context handling, and behaviour on specific task types. Using several deliberately can beat any single one, provided the split is based on measurement rather than impression.

Method

1. Measure before splitting. A defined comparison on your own tasks is the only reliable basis, since general benchmarks may not reflect your work (see agent-eval-design).
2. Split by task type, not by preference. Long-context summarisation, precise code editing, and open-ended design have genuinely different demands (see agent-specialist-router).
3. Keep prompts portable. Instructions tuned to one model's quirks do not transfer, so favour clear structure over model-specific tricks (see prompt-structure).
4. Use a second model for verification. An independent model reviewing another's output catches errors a self-review will not (see agent-generate-and-verify).
5. Standardise the handoff format. Structured intermediate output lets tools be swapped without rewriting the workflow (see agent-handoff-protocol).
6. Watch the coordination overhead. Moving context between tools costs tokens and time and can exceed the quality gain.
7. Re-evaluate periodically. Model capabilities change quickly, and a split decided a year ago is probably wrong now (see prompt-testing).

Boundaries

Multiple models mean multiple failure modes, prompt sets, and bills to maintain. Differences narrow as models improve, which erodes the justification. Consistency of behaviour within one workflow has value that switching sacrifices.