> For the complete documentation index, see [llms.txt](https://docs.tabnine.com/main/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.tabnine.com/main/getting-started/tabnine-cli/features/model-selection.md).

# Model Selection

{% hint style="warning" %}
**Tabnine CLI is in maintenance mode.** As of 6.6, we have stopped active development. It will receive only critical updates through December 31, 2026. After that, we will fully deprecate it. We are transitioning CLI users to [Tabnine Plugin for OpenCode](/main/getting-started/tabnine-plugin-for-opencode.md).
{% endhint %}

Choose which AI model powers your Tabnine CLI interactions.

### Opening the Model Dialog

```bash
/model
```

This opens an interactive dialog showing available models.

### Available Models

Tabnine CLI uses the same models described on the [AI Models](/main/welcome/readme/ai-models.md) page.

### Changing Models

#### Via `/model` Command

{% stepper %}
{% step %}
**Open the model dialog**

Run:

```bash
/model
```

This opens the interactive model selection dialog.
{% endstep %}

{% step %}
**Navigate**

Use the arrow keys or `J` / `K` to move through available models.
{% endstep %}

{% step %}
**Select**

Press `Enter` to select the highlighted model.
{% endstep %}

{% step %}
**Immediate effect**

Model changes take effect immediately after selection.
{% endstep %}
{% endstepper %}

#### Via `/model set` (Non-Interactive)

You can also change the model without opening the dialog:

```bash
/model set <model-name>
```

This sets the model for the current session only. To persist the change across sessions, add the `--persist` flag:

```bash
/model set <model-name> --persist
```

#### Via `--model` Flag

Specify the model when launching Tabnine CLI:

```bash
tabnine --model <model-name>
```

The `-m` short form also works:

```bash
tabnine -m <model-name>
```

#### Via Environment Variable

Set the `TABNINE_MODEL` environment variable before launching:

```bash
TABNINE_MODEL=<model-name> tabnine
```

### Model Selection Precedence

When multiple sources specify a model, the following precedence applies (highest to lowest):

1. `--model` command-line flag (or `-m`)
2. `TABNINE_MODEL` environment variable
3. `model.name` in `settings.json`
4. Default model from Tabnine service

### Model Information

View current session statistics (including token usage):

```bash
/stats
```

View current model and context usage in the footer (shown if enabled in settings).

#### Token Limits

Tabnine CLI automatically manages token limits based on the selected model.

### Fallback Behavior

If your selected model is unavailable, Tabnine CLI may:

1. Prompt you to select another model
2. Automatically fall back to a default model (depending on settings)

### Model Persistence

Your model selection is saved and persists across sessions when using:

* `/model set <model-name> --persist` — writes `model.name` to `settings.json`
* The interactive `/model` dialog — persists your selection automatically

Persistent selections are stored in `.tabnine/agent/settings.json`.

### Fast Model Configuration

You can configure a lightweight model to handle background CLI tasks, reducing latency for operations the user never directly sees. To configure a fast model, add `"fastModel": "<model-name>"` to your `settings.json` or use the `/model fast` slash command in the CLI.

The following tasks are routed to the fast model when one is configured:

* Session summary
* Shell output summary
* Tool output summary
* Edit corrector
* Fast ack helper
* Loop detection (primary pass)
* LLM edit fixer

Loop detection double-check and next-speaker checker remain on the main model, as they require higher-confidence reasoning. When no fast model is configured, all tasks use the main model. The fast model setting is provider-agnostic and works with any model the backend supports.


---

# Agent Instructions
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## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation by asking a question.

Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters:

```
GET https://docs.tabnine.com/main/getting-started/tabnine-cli/features/model-selection.md?ask=<question>&goal=<user_goal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case.

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