## Overview

Every bot already picks a **default model** and a **fallback chain**. The **Advanced model settings**
sit one level below that: four knobs that change **how** the chosen model answers, without changing
**which** model it is.

Four fields, all optional:

| Field | Range | What it changes |
|---|---|---|
| **Reasoning effort** | Inherit / None / Low / Medium / High | how much the model "thinks" before replying, in families that have reasoning |
| **Temperature** | 0 to 2 | higher values make replies more varied; lower values, more predictable |
| **Top P** | above 0 up to 1 | nucleus sampling — usually tuned **instead of** temperature |
| **Max output tokens** | 1 or more | ceiling on tokens generated per reply |

> **Blank (or "Inherit") keeps the current behavior.** A bot that never opens this section stays
> exactly as it is: the platform only sends a knob to the provider when you chose one. Leaving a field
> blank is **not** "zero" — it is "send nothing, use the provider's default".

## Prerequisites

- **Maestro enabled** and a configured bot.
- **Administrator** permission to edit the bot's configuration.
- Knowing which **model** the bot uses: the knobs only apply where that model's provider accepts them
  (see the compatibility table below).

## Step by step

1. Open the bot's configuration.
2. Go to **Advanced model settings**.
3. Fill in **only** the fields you want to change. Leave the rest blank.
4. Save.
5. Run a test conversation with that bot and compare the result before applying the same setting to
   others.

## Settings & options

### Temperature and Top P — tune one, not both

Both control the same thing along different routes: how much variation a reply may have. The practice
the providers themselves recommend is to **move one and leave the other blank**. Tuning both at once
makes the result hard to predict and even harder to compare between two versions of the same bot.

As a practical reference:

- **Low temperature (0 to 0.3)**: procedures, technical answers, data extraction — anything that
  should come out the same every time.
- **Medium temperature (0.4 to 0.6)**: ordinary support, natural conversation without becoming
  unpredictable.
- **High temperature (0.7 or above)**: creative writing, deliberate message variation. Rarely what you
  want in customer service.

### Max output tokens

This is a **ceiling per reply**, not a target. It exists to hold back replies that are too long for
the channel (WhatsApp especially) and to cap cost per turn. A ceiling set too low **truncates** the
reply instead of summarizing it — if messages start ending mid-sentence, the ceiling is too tight.

### Reasoning effort

It applies to reasoning-capable models (OpenAI's o-series/gpt-5.x families, DeepSeek and the like).
More effort usually means a better answer on hard tasks, more waiting and more cost.

This field has a second effect, which is why it exists in the form: choosing a value here also
**stops the router from forcing an automatic endpoint switch** when the model is a reasoning one
**and** has tools bound to it. If you have already decided how the model should reason, the platform
honors your decision instead of deciding for you.

### Where each knob applies — and where it is dropped

Each provider spells these parameters differently, and some simply do not have the parameter. The
platform translates the value into that provider's correct name and, when the provider **does not
have** it, **drops the knob** rather than inventing a name the call would reject.

| Knob | Where it is dropped |
|---|---|
| **Temperature** | on OpenAI reasoning-family models (the family does not accept it) |
| **Top P** | on OpenAI reasoning-family models; on Groq and Cohere, which do not expose the parameter |
| **Max output tokens** | on Cohere |
| **Reasoning effort** | on Anthropic, Google, Cohere, Ollama, xAI and OpenRouter |

About the first row: on OpenAI reasoning models, **temperature and Top P are dropped together, on
purpose**. Previously temperature vanished quietly while Top P went on to fail the request — the same
panel value behaved two different ways depending on which field you happened to fill in.

**Dropping is silent.** It does not become an error in the conversation nor a warning on this screen.
If a knob seems to have no effect, check the table above before looking for a fault.

### An invalid value: the provider is the one that refuses

The platform validates the ranges in the form (temperature 0 to 2, Top P above 0 up to 1, output
ceiling 1 or more). Beyond that, whoever decides if a value is acceptable is **the model's provider** —
and any error message you see comes from there, not from the platform. When in doubt, leave it blank.

## Use cases

- **Support bot with a fixed procedure**: temperature 0.1, so the same question always gets the same
  answer.
- **Sales bot**: temperature 0.5 to 0.6 — natural conversation without inventing variations every
  turn.
- **A bot that only extracts data** (recording an order, filling in a record): temperature 0.1 and a
  low output ceiling; the reply is short by nature.
- **A bot on a channel with a size limit**: an output ceiling so messages do not come out enormous.
- **A hard task with no rush** (analysis, diagnosis): reasoning effort **High**, accepting more time
  and more cost.
- **A reasoning model with tools** where you want to control the behavior: set the effort explicitly
  instead of letting the platform decide.

## Tips, limits & best practices

- **Change one field at a time** and test. Two knobs at once make it impossible to know which one
  caused the difference.
- **Temperature OR Top P.** Not both.
- **Blank is not zero.** Clearing the field hands the decision back to the provider; typing `0` is a
  choice of yours, with a real effect (temperature 0 = as predictable as it gets).
- **Reasoning effort costs time and money.** On WhatsApp support, a high effort can make the reply
  slow enough for the contact to give up.
- **These knobs do not change the model.** If quality does not improve here, the next step is to
  change the default model, not to raise the temperature.
- **The fallback chain still applies.** The knobs are applied on top of whichever model actually
  serves the turn — including a fallback, when the main one fails.
- **A bot with none of these fields filled in is not a badly configured bot.** The provider defaults
  are good for most conversations.

## Troubleshooting

- **"I changed the temperature and nothing changed"**: the bot's model is an OpenAI reasoning one — in
  that family temperature and Top P are dropped. Change the model or use reasoning effort.
- **"I set Top P and it is still the same"**: besides the case above, Groq and Cohere do not expose
  that parameter.
- **"Replies are being cut off mid-sentence"**: the **Max output tokens** ceiling is too low. Raise it
  or leave it blank.
- **"The provider rejected the call"**: the value is outside what that model accepts. The message
  comes from the provider; clear the field and try again.
- **"The bot got slow"**: high reasoning effort, or a reasoning model where an ordinary one would do.
- **"I picked a reasoning effort and the model is not a reasoning one"**: the knob is dropped. It
  breaks nothing, it just has no effect.

## See also

- [Portable models: one bot, several providers](/hc/ajuda/articles/maestro-brain-modelos-portateis-en)
- [DeepSeek provider](/hc/ajuda/articles/maestro-brain-provedor-deepseek-en)
- [Service sequence](/hc/ajuda/articles/maestro-brain-sequencia-de-atendimento-en)
- [Turn verification and holding the reply](/hc/ajuda/articles/maestro-brain-verificacao-de-turno-en)
- [What Maestro AI and the Account Brain are](/hc/ajuda/articles/maestro-brain-overview-en)