pi/packages/coding-agent/docs/models.md

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 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. 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 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 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.

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 for server startup, model layout, downloads, and connection troubleshooting.

For Ollama, LM Studio, vLLM, SGLang, and other compatible servers, configure a compatible endpoint in models.json.

Configure a compatible endpoint

Use models.json when an endpoint speaks an API Pi already supports. This includes most Ollama, LM Studio, vLLM, SGLang, and proxy deployments.

{
  "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:

{
  "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.

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

{ "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.

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 are also listed as classifier models.

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

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 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 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.

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.