Models
The model is the reasoning engine behind a run. It is chosen per agent, and it is priced per run, so it is the axis with the most direct line to your bill.
It changes behavior in the ways you would expect and one you might not. Stronger models follow multi-clause instructions more reliably, hold longer chains of reasoning, and are better at noticing that a tool result contradicts an assumption. Smaller models are faster and cheaper and are often indistinguishable on a well-scoped job — extract these fields, summarise this page, decide which of three branches applies.
The one that surprises people: the expensive step is usually not the frequent step. A single hard step can dominate a bill while most calls are routine. That is why this is per-agent rather than an account-wide setting — you are choosing per job, not once.
Models in this deployment
Grouped by provider, read from the live model catalog at page load.
| gemini | Gemini 2.5 Flash · Gemini 2.5 Pro | available |
| openai | GPT-4o · GPT-4o Mini | available |
| anthropic | Claude Sonnet 4.5 · Claude Haiku 4.5 | available |
How to choose
Pick the cheapest model that passes the agent's eval, then leave it. That ordering matters: without an eval, "which model is better" has no answer and the honest default is the expensive one, which is how bills get built.
Move up a tier when you see the specific failures a bigger model fixes — instructions half-followed, a plan that loses the thread, a tool result misread. Moving up for a vaguely disappointing answer usually just costs more.
Cost per run is visible per agent, so the comparison is a real one you can make after the fact. What you cannot do yet is have that comparison run itself.
There is no automated experiment runner yet. Nothing here sweeps this axis for you — changing it means editing the agent and reading the next run. A compare mode that runs one task across several compositions is named on the index as not built, and that is still true.