> ## Documentation Index
> Fetch the complete documentation index at: https://xura.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# Cost & performance

> Where tokens go, and how to keep spend sane.

Every Flow run costs tokens. Understanding where they go helps you tune.

## Where the cost lives

| Cost driver            | Impact                                                          | Tune it                                                      |
| ---------------------- | --------------------------------------------------------------- | ------------------------------------------------------------ |
| **Model choice**       | Opus is \~5× Sonnet per token                                   | AI Studio → LLM Configuration                                |
| **web\_search calls**  | Each search fetches + processes a page (\~2-4K tokens per call) | Only fires in Signals & research paths; capped at 5/run      |
| **max\_tokens output** | Direct output cost                                              | Set per feature — Flows use 2500, Signals use 2500           |
| **Continuations**      | Each pause\_turn continuation = another full-context LLM turn   | Capped at 1 for Signals, 1 for Flows                         |
| **Data node size**     | Query results feed the next reasoning turn                      | Cap `limit` in your QuerySpec; 20-100 rows is usually enough |
| **Prompt caching**     | Fixed instructions get \~90% discount on cache hits             | Enabled by default on Signals; opt-in for custom flows       |

## Practical tips

**Keep data queries tight.** A data node that returns 500 rows makes the next agent turn expensive. Add filters. Cap `limit` at 100 unless you truly need everything.

**Use approval gates.** They're free — the gate itself doesn't cost tokens. But they prevent the AI from cascading into expensive action loops without your say-so.

**Don't re-plan repeatedly.** The Plan-from-description button greys out after generating so you don't accidentally regenerate the same graph. If you need to tweak, edit nodes directly.

**Consider Sonnet for automation.** For structured JSON + tool use tasks (which is what Flows are), Sonnet is nearly as capable as Opus at 1/5 the cost.

## Where to see spend

**AI Studio → Activity log** — every LLM call is logged with token counts. Sort by source to see which features are burning tokens.

## Rate limits & cooldowns

* **Signals research** — 24h cooldown per user. One run per day.
* **Flow runs** — no cooldown, but you're gated by your own Anthropic API rate limits.
* **Planner** — no cooldown, but disabled when description is unchanged.