Reasoning Models

Basic usage

Cecli is configured to work with most popular reasoning models out of the box. You can use them like this:

# Sonnet uses a thinking token budget
cecli --model claude-sonnet-5 --thinking-tokens 8k

# o3-mini uses low/medium/high reasoning effort
cecli --model gpt-5.6-terra --reasoning-effort high

Inside the cecli chat, you can use /thinking-tokens 4k or /reasoning-effort low to change the amount of reasoning. Use /thinking-tokens 0 to disable thinking tokens.

The rest of this document describes more advanced details which are mainly needed if you’re configuring cecli to work with a lesser known reasoning model or one served via an unusual provider.

Reasoning settings

Different models support different reasoning settings. cecli provides several ways to control reasoning behavior:

Reasoning effort

You can use the --reasoning-effort switch to control the reasoning effort of models which support this setting. This switch is useful for OpenAI’s reasoning models, which accept “low”, “medium” and “high”.

Thinking tokens

You can use the --thinking-tokens switch to request the model use a certain number of thinking tokens. You can specify the token budget like “1024”, “1k”, “8k” or “0.01M”. Use “0” to disable thinking tokens.

Model compatibility and settings

Not all models support these two settings. cecli uses the model’s metadata to determine which settings each model accepts:

- name: gpt-5-mini
  ...
  accepts_settings: ["reasoning_effort"]

If you try to use a setting that a model doesn’t explicitly support, cecli will warn you:

Warning: gpt-5-mini does not support 'thinking_tokens', ignoring.
Use --no-check-model-accepts-settings to force the 'thinking_tokens' setting.

The warning informs you that:

1. The setting won’t be applied because the model doesn’t list it in accepts_settings
2. You can use --no-check-model-accepts-settings to force the setting anyway

This functionality helps prevent API errors while still allowing you to experiment with settings when needed.

Each model has a predefined list of supported settings in its configuration. For example:

• OpenAI-compatible model APIs generally support reasoning_effort
• Anthropic-compatible model APIs generally support thinking_tokens