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Model Management

This page answers the most practical first-time question:

what do you need to configure first so Prompt Optimizer can actually run?

If you are still going through first-time setup, read this together with Quick Start.

The entry point is the Model Management button in the top-right corner.

Note

For your first run, do not configure too many providers at once. One working text model is more useful than a long unfinished provider list.

First-time users: only do these 3 steps

  1. Add one text model
  2. Run one optimize / test / evaluate flow in a text workspace
  3. Only then decide whether you need a second text model or an image model

Most first-time users do not need a large model list.

Minimum working setup

Your goal Minimum setup
Start using text workspaces 1 text model
Compare results 2 text models
Use image workspaces 1 text model + 1 image model

This is enough to understand at first

Text model vs image model

  • Text models handle left-side analysis, optimization, iteration, and text-side testing/evaluation
  • Image models only handle actual image generation on the right side

Left-side model vs right-side model

In text workspaces:

  • left-side model: analyzes and improves prompts
  • right-side model: executes prompts and produces evidence

They can be the same model, but they do not have to be.

How to configure models for the first run

Case A: you just want the app to work

Configure one text model.

That one model is enough to start:

  • left-side analysis / optimization
  • right-side testing
  • right-side Result Evaluation
  • right-side Compare Evaluation

Case B: you want real result comparison

Configure two text models:

  • one main model
  • one comparison model

This makes it easier to tell whether the difference comes from the prompt or from the model.

Case C: you want image workspaces

Configure at least:

  • one text model
  • one image model

Because:

  • the left side still uses a text model to improve image prompts
  • the right side uses an image model to generate the actual image

Step 1: add one text model

Choose the provider you know best and can connect with the least friction.

Step 2: make sure connection testing succeeds

After you add the model, run Test Connection.

Step 3: run one text workspace

The simplest starting points are:

If you can complete:

  • left-side optimization
  • right-side testing
  • one evaluation

then your minimum setup is already good enough.

Step 4: add more models only when needed

Add a second text model only if you want comparison. Add an image model only if you are entering image workspaces.

Three common connection patterns

1. Public model platforms

Examples:

  • OpenAI
  • Gemini
  • DeepSeek
  • SiliconFlow

In most cases you only need:

  1. choose the provider
  2. paste the API key
  3. select the model
  4. run connection testing

2. Ollama

If you run Ollama locally, use the built-in Ollama provider.

Typical behavior:

  • default endpoint: http://localhost:11434/v1
  • API key often not required
  • model list can refresh from your installed local models

3. Custom

If your service is OpenAI-compatible, use Custom.

Typical cases:

  • LM Studio
  • internal company gateway
  • self-hosted OpenAI-compatible service
  • any service that needs a custom base URL

Example:

Provider: Custom
Base URL: https://your-api.example.com/v1
Model: your-model-name
API Key: fill based on your service

If connection fails, then check deployment and environment

Web / hosted version

The browser sends requests directly to your model service, so you may hit:

  • CORS
  • mixed content when HTTPS pages call local HTTP endpoints

Desktop app

Usually better for:

  • Ollama
  • LM Studio
  • local network services
  • internal APIs
  • custom gateways with browser restrictions

Docker

Docker packages the web UI and MCP together, but the page still runs in the browser, so browser restrictions still matter.

Related pages:

Supported text providers

The current codebase currently includes:

  • OpenAI
  • Gemini
  • Anthropic
  • DeepSeek
  • SiliconFlow
  • Zhipu AI
  • DashScope
  • OpenRouter
  • ModelScope
  • MiniMax
  • Ollama
  • Custom (OpenAI-compatible endpoints)

What the model manager can do

In addition to add / edit / delete, the text-side manager supports:

  • connection testing
  • cloning configs
  • refreshing model lists
  • advanced parameters
  • provider-specific API-key links for some providers

The image-side manager supports:

  • add / edit / clone / delete
  • enable / disable
  • connection testing
  • preview test image
  • provider / model / capability tags

How to tell whether setup is already good enough

You can stop tuning model setup for now if all three are true:

  1. at least one text model passes connection testing
  2. you can produce one real result in a text workspace
  3. you can run one evaluation on that result

Where configuration is stored

  • web / hosted version: current browser storage
  • desktop app: local application data
  • extension: extension-local storage

If you need backup or migration, use Data Management.

Common questions

Connection test passes, but real runs still fail

Common reasons:

  • quota or billing limits
  • wrong model name
  • browser-side CORS / mixed-content blocking
  • left-side model and right-side model are not what you thought they were

Do I need many models on day one?

No. In most cases:

  • one text model is enough for text workspaces
  • add a second text model only for comparison
  • add image models only for image workspaces

I configured a model, but the app still won’t run

Check these first:

  1. did connection testing actually succeed?
  2. is this a text model when the page expects text?
  3. are you in a browser trying to call a local HTTP endpoint?
  4. does this workspace also need an image model or additional inputs?