Best Models for n8n AI Automation
Use low-cost chat models for extraction/classification, stronger reasoning models for decisions, and embeddings for retrieval steps.
Last verified: 2026-06-05
Current recommendation
Use low-cost chat models for extraction/classification, stronger reasoning models for decisions, and embeddings for retrieval steps.
- Use these as starting candidates, not universal winners.
- Run a short test in your own workflow before moving production volume.
- Use Usage Logs to compare request IDs, latency, usage, and billed amount.
Methodology
MetisRouter should publish measured benchmark values only when the same task set has been run across the compared models.
- Track task success rate, first-token latency, total latency, retry rate, and cost per successful task.
- Measure tool-calling reliability and JSON/schema adherence for agent workflows.
- For image and video, measure output quality, supported parameters, duration, resolution, and cost unit.
What to log
A useful benchmark needs enough operational detail to be reproducible and commercially meaningful.
- Model ID, endpoint, prompt or task class, input size, output limit, and timestamp.
- p50/p95 latency, error rate, billed usage, and retry count.
- Known failure modes and when the model should not be used.
FAQ
Why are there no fake leaderboard scores?
Publishing unmeasured scores would hurt trust. These pages publish recommendations and methodology until real benchmark runs exist.
How do I compare two models today?
Run the same bounded task with both exact model IDs, then compare Usage Logs for latency, tokens/assets, cost, and output quality.