Advanced model settings per bot: temperature, Top P, output ceiling and reasoning effort

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:

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.

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
• DeepSeek provider
• Service sequence
• Turn verification and holding the reply
• What Maestro AI and the Account Brain are