# Choose a Model

For a built-in provider, start with `/login`, then choose a model with `/model`. Use custom model configuration only when Pi does not already include the provider or endpoint you need.

## Choose a connection

| What you have | Recommended setup |
|---|---|
| A supported subscription | Sign in through `/login` |
| A provider API key | Store it through `/login` or set its environment variable |
| A local GGUF model | Connect Pi to the llama.cpp router |
| An OpenAI-, Anthropic-, or Google-compatible endpoint | Add it to `models.json` |
| A provider with a custom protocol or authentication flow | Build or install a provider extension |

Browse the [model catalog](https://pi.dev/models) for current providers, model IDs, capabilities, context limits, and pricing. Pi starts with its bundled catalog and can overlay newer catalog data from pi.dev. Cached catalog data remains available offline; run `pi update --models` to force a refresh.

## Authenticate

Run `/login` and select a provider. Pi stores credentials in [`auth.json`](configuration.md#agent-directory). Run `/logout` to remove stored credentials for a provider.

You can instead provide an API key through the provider's environment variable. This is useful in CI and other environments where Pi should not write credentials. [Provider Authentication](providers.md) lists the variables and cloud-provider setup.

When several credential sources are configured, Pi uses a runtime `--api-key` first, then a stored `auth.json` credential, an `apiKey` from `models.json`, and finally the provider's environment variables or ambient cloud credentials. Provider extensions can define their own authentication behavior.

Keep `auth.json` and any credential commands private. Project settings and extensions can execute inside the Pi process after you trust a project. Review [Security](security.md) before loading configuration from an untrusted directory.

## Select a model

Run `/model` to search available models. The picker shows models whose providers have usable authentication. Press `Ctrl+S` on a model to save it as the default for new sessions.

Run `/thinking` to select the thinking level for the current model. Press `Ctrl+S` there to save the startup level. Pi limits the choices to levels supported by the selected model.

`Ctrl+P` cycles through available models. Use `/scoped-models` to control that cycle and save the selection, or configure model patterns through [Settings](settings.md#model-cycling).

A session records model and thinking-level changes. Resuming the session restores them without changing defaults for new sessions.

## Connect local models

Pi integrates directly with the llama.cpp router. The router discovers GGUF files and loads models on demand. Pi's `/llama` command manages the router, while `/model` selects one of its loaded models.

Follow [Local Models with llama.cpp](llama-cpp.md) for server startup, model layout, downloads, and connection troubleshooting.

For Ollama, LM Studio, vLLM, SGLang, and other compatible servers, [configure a compatible endpoint](#configure-a-compatible-endpoint) in `models.json`.

## Configure a compatible endpoint

Use [`models.json`](configuration.md#agent-directory) when an endpoint speaks an API Pi already supports. This includes most Ollama, LM Studio, vLLM, SGLang, and proxy deployments.

```json
{
  "providers": {
    "ollama": {
      "baseUrl": "http://localhost:11434/v1",
      "api": "openai-completions",
      "apiKey": "ollama",
      "models": [
        { "id": "qwen2.5-coder:7b" }
      ]
    }
  }
}
```

The dummy key makes the model available to Pi; Ollama ignores it. For an authenticated endpoint, `apiKey` and header values can use `$NAME` or `${NAME}` environment interpolation, a literal value, or a leading `!command`. Commands in `models.json` run at request time and are not cached by Pi.

Opening `/model` reloads the file. A `models` entry adds or replaces a model with the same ID on that provider. Use `modelOverrides` to change metadata for an existing built-in or extension-provided model without replacing the provider's model list. Unknown override IDs are ignored.

### Describe model input and caching

Use `inputLimits.images.resize` to control how Pi encodes new image attachments, `read` results, and tool-result images before storing them in conversation history:

```json
{
  "id": "vision-model",
  "input": ["text", "image"],
  "inputLimits": {
    "images": {
      "resize": {
        "maxWidth": 1568,
        "maxHeight": 1568,
        "maxBytes": 524288,
        "jpegQuality": 75
      }
    }
  }
}
```

`maxBytes` limits the base64-encoded payload. Omitted resize fields use conservative defaults of 2000 by 2000 pixels, 4.5 MiB encoded, and JPEG quality 80. Images are encoded once; changing models does not rewrite historical images. The catalog can also describe hard request limits with `inputLimits.maxRequestBytes`, `images.maxPerMessage`, and `images.maxPerRequest`, but Pi does not yet rewrite or reject history based on them.

<a id="prompt-cache-lifetimes"></a>

Use `promptCache` to declare the provider's best-effort cache lifetime in seconds for the `short` or `long` retention tier:

```json
{ "id": "claude-sonnet-5", "promptCache": { "short": 300, "long": 3600 } }
```

Choose the conservative end of any published range. A model without a lifetime for the active tier is not eligible for cache warming. A `modelOverrides` entry can set `inputLimits` or `promptCache` for a built-in or extension model, including a model accessed through a validated proxy. See [`cacheWarming`](settings.md#model-and-thinking).

Compatibility settings should describe verified differences in the endpoint's request or response behavior. Do not enable them based only on an endpoint advertising OpenAI or Anthropic compatibility.

## Use classifier models

Classifier models do not chat. They answer typed questions about JSON state: pick one of several choices, answer yes or no, or give a score, each with probabilities. Pi includes TypeSafe's Jev model from these providers:

| Provider | Model IDs | Authentication |
|---|---|---|
| `typesafe` | `jev-latest` | `TYPESAFE_API_KEY` |
| `openrouter` | `typesafe/jev-1.13`, `~typesafe/jev-latest` | `OPENROUTER_API_KEY` or `/login` |
| `cloudflare-workers-ai` | `typesafe/jev` | `CLOUDFLARE_API_KEY` and `CLOUDFLARE_ACCOUNT_ID` |
| `vercel-ai-gateway` | `typesafe-ai/jev` | `AI_GATEWAY_API_KEY` |
| `opencode` | `jev-1.13`, `jev-1.13-free` | `OPENCODE_API_KEY` |

Chat models on a [llama.cpp router](llama-cpp.md#classification) are also listed as classifier models.

Classifier models do not appear in `/model`. The model reaches them through the [`codemode`](cli.md#enable-codemode) tool, which is off unless an MCP server turned it on. Enable it with `"defaultTools": ["+codemode"]` in [settings](settings.md#tools). Scripts then list classifier models with `models.getAvailableOfType("classifier")` and call `models.classify(model, { state, questions })`:

```js
const jev = await models.getModelOfType("classifier", "typesafe", "jev-latest");
const result = await models.classify(jev, {
  state: { message: "The change works, thanks." },
  questions: {
    approved: {
      type: "bool",
      instructions: "Does the user approve of the result?",
      criteria: { true: "Approval", false: "No approval" },
    },
  },
});
return result.answers;
```

When the service reports token counts, as all System One services do, `result.usage` carries them with their cost. Pi adds the usage of a script's classifier calls to the `codemode` tool result, so it counts toward the session cost in the footer and `/session`. The cost uses the model's catalog price; models without one, such as TypeSafe's direct `jev-latest`, report tokens at no cost.

Extensions call classifiers through `ctx.modelRegistry.classify()`, without codemode. [Virtual models](virtual-models.md#route-requests) can use them to route requests; see the `jev-router.ts` example.

## Add a custom provider

Use an extension when the provider needs custom streaming, model discovery, or authentication behavior. See [Custom Providers](custom-provider.md) for the extension workflow.

## Troubleshooting

### A model does not appear

Confirm that its provider has usable authentication. Custom models can load from `models.json` but remain unavailable in `/model` until Pi can resolve credentials. For llama.cpp, only models currently loaded by the router appear.

### Authentication works in one shell only

Check whether the key came from an environment variable rather than `auth.json`. Environment variables must be present in the process that starts Pi.

### Sign-in opens a browser on a remote machine

Complete the provider's headless authentication flow when available. Some providers let you paste the final redirect URL or authorization code back into Pi. See [Authenticate interactively](providers.md#authenticate-interactively).

### A compatible endpoint rejects requests

Check its API type and compatibility settings in `models.json`. The upstream server must support the corresponding request fields and behavior.