Choosing an AI Model | Apologist AI

The model is the engine that turns an Agent’s instructions and context into a response. Changing it can affect answer quality, instruction-following, response length, and the number of credits used for every response.

Purpose

Choose the lowest-cost model that reliably passes the questions your Agent needs to answer, then tune only the parameters that solve a specific, observed problem.

Prerequisites

An active Agent with baseline instructions. See Customizing Instructions.
A small test set of real questions, including straightforward, ambiguous, and difficult examples.
Permission to configure models on your team’s plan.

These settings belong to the Agent’s default Responder, the AI configuration used to answer prompts. If your Agent has the Responders capability turned on, the Behavior page is hidden and you configure the model on each Responder instead.

How the Setting Reaches the Agent

For each prompt, the Agent runtime resolves the Responder, loads its selected model, and sends only the parameters that model supports. The published Agent interface does not display the model name, so verify a change through representative questions rather than looking for a model badge in the chat window.

Model availability is live configuration. Names, recommendations, credit costs, and deprecation dates can change, so use the cards currently shown in Console rather than relying on a fixed list in documentation.

Steps

Open the Model Tab

Select Agents in the left-hand navigation, open your Agent, choose Behavior from the row of pages across the top, and then select the Model tab.

The Model tab of the Seeker Agent's Behavior page in Console, showing model classes and model cards with their per-response credit costs.

Console groups models into three classes:

Class	Console description	Good first use case
Limited	Inexpensive models with very limited reasoning abilities	Simple, tightly constrained answers where cost matters most
Standard	Economic models that balance lower cost with better responses	General-purpose Agents and the usual starting point for comparison
Premium	Higher quality output at a premium price point	Nuanced instructions, difficult questions, and higher-stakes responses

Selecting a class filters the model cards beneath it. Scroll within the page to see the full list for that class.

Choose a Model By Evidence And Cost

Each model card shows its credit cost per response. Additional feature charges, such as Live Web Search, are separate and can stack on top of this amount.

Use the card signals as a shortlist:

Recommended cards are starred and sorted toward the top.
A Deprecation label identifies a model that should be replaced; when available, the label includes the planned date.
An unlabelled card is still available, but Console is not specifically recommending it.

Start with the least expensive recommended model in the class that fits your use case. Do not assume a premium model is automatically the right choice: a cheaper model that consistently follows your instructions is the better production model.

Selecting a different model resets Max Output Tokens on the Advanced tab to that model’s maximum. Review the Advanced tab before saving, especially if your Agent should give short answers.

Review the Supported Advanced Parameters

Still on the Behavior page, select the Advanced tab.

The Advanced tab of the Seeker Agent's Behavior page in Console, showing the tuning controls supported by its selected model, including Max Output Tokens and Cache Duration.

Console hides parameters the selected model does not support. A missing control is therefore expected and does not mean the form failed to load.

Setting	What it controls	Practical guidance
Reasoning Effort	How much supported reasoning models deliberate before answering	Use Low for direct questions and compare Medium or High on genuinely multi-step tasks. Minimal is available only where the model supports it.
Verbosity	The amount of detail in supported model responses	Choose Low, Medium, or High only when prompt instructions alone do not produce the desired length. This field is hidden for models that do not support it.
Temperature	Response variability, from 0 to 2	Lower it for more consistent wording; raise it only when the Agent needs more variety.
Top P	How broadly the model samples likely next tokens, from 0 to 1	Lower values narrow the candidate set. Change this or Temperature, not both at once, so the effect remains measurable.
Frequency Penalty	Repeated use of the same tokens, from -2 to 2	Positive values discourage repetition. Leave it at 0 unless repetition is a demonstrated problem.
Presence Penalty	Reuse of topics already mentioned, from -2 to 2	Positive values encourage the response to introduce new topics. Leave it at 0 for focused answers.
Max Output Tokens	The maximum response-token budget	Reduce it to control overly long responses. The allowed maximum comes from the selected model.
Cache Duration	How long the same request and configuration may reuse a stored response	Leave it unselected while comparing repeated runs. Set it only when reuse is appropriate for the Agent’s content.

If Reasoning Effort or Verbosity shows Select an option, the Agent runtime falls back to Medium for a model that supports that parameter. Select an explicit value when you want the saved configuration to be unambiguous.

Change one parameter at a time and retest. Otherwise, you cannot tell which setting improved or degraded the response.

Save And Test On the Published Agent

Select Save Changes. Model and Advanced are tabs in the same Behavior form, so one save persists changes made on either tab.

Open the Agent’s published URL. In the standalone interface, enter a question in Ask a question here, select Submit, and use New Chat before each isolated test so conversation history does not affect the next result.

Run the same test set for every candidate model:

Test	What to compare
A straightforward, source-backed question	Factual accuracy, citation quality, and instruction-following
An ambiguous question	Whether the Agent asks for clarification instead of guessing
A multi-step or nuanced question	Reasoning quality and whether important constraints are missed
A requested short and long answer	Response length, completeness, and whether Max Output Tokens truncates the result

Record the model, parameter values, credits per response, and pass/fail result. Judge the complete set rather than the most impressive single answer; model output can vary between runs.

A configured Cache Duration can return the same stored response for repeated identical messages under the same model configuration. Leave it unselected while measuring response variation.

Test through the published Agent when validating Console settings. API clients can supply per-request model settings that override the saved values and are therefore a different test.

Expected Result

The Agent uses a non-deprecated model that passes its representative test set at an acceptable credit cost. Advanced parameters are explicit where needed, unsupported controls remain hidden, and the published Agent responds consistently with its instructions.

Troubleshooting

The Behavior page is missing. The Agent has the Responders capability turned on, so choose and configure the model on each Responder.
The Model or Advanced controls are disabled. Model configuration is not included in the team’s current plan, or editing is restricted for this Agent.
A model is not listed. Console shows active models allowed by the team’s plan. Availability changes as models and plans change.
An Advanced control disappeared after switching models. The new model does not support that parameter. Console intentionally hides it, and the Agent runtime does not send it.
Max Output Tokens changed unexpectedly. Choosing a model resets the value to that model’s maximum. Set the intended limit after selecting the model.
Reasoning Effort says Select an option. With no explicit value saved, the runtime uses Medium on supported reasoning models.
Repeated tests return exactly the same answer. Clear Cache Duration and save, then start a New Chat before testing again.
The response still seems to use the old behavior. Confirm Save Changes completed, start a New Chat, and test through the published Agent rather than an API request carrying overrides.