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 — and the model axis is sweepable: the experiment runner prices the same work across every model this workspace can actually serve, judged by the agent's own eval.
There is an automated experiment runner: each agent's Tuning page (under /u/agents) can sweep the model, memory, planning, retrieval and state axes on one fixed objective — the agent's own declared eval, which is never swept, while guardrails only observes. Its limits are stated plainly: a naive full grid, several runs per cell, stopped cleanly at a hard budget wall with partial results kept. It compares compositions; it does not invent them.