Model Discovery

Umwelten provides comprehensive model discovery features to help you find and compare models across all supported providers (Google, Ollama, OpenRouter, LM Studio, GitHub Models).

Listing Models

Basic Model List

# List all available models
dotenvx run -- pnpm run cli -- models 

# List with JSON output for programmatic use
dotenvx run -- pnpm run cli -- models  --json

# Use dotenvx to load environment variables from .env
dotenvx run -- dotenvx run -- pnpm run cli -- models

Filter by Provider

# Filter by specific provider
# Use dotenvx to ensure API keys are loaded from .env
dotenvx run -- dotenvx run -- pnpm run cli -- models --provider openrouter      # requires OPENROUTER_API_KEY
dotenvx run -- pnpm run cli -- models  --provider ollama
dotenvx run -- dotenvx run -- pnpm run cli -- models  --provider google         # GOOGLE_GENERATIVE_AI_API_KEY
dotenvx run -- pnpm run cli -- models  --provider lmstudio
dotenvx run -- dotenvx run -- pnpm run cli -- models  --provider github-models  # GITHUB_TOKEN

# Or using pnpm cli (for local development)
dotenvx run -- pnpm run cli models --provider github-models

Filter by Cost

# Show only free models
dotenvx run -- pnpm run cli -- models --free

# Sort by cost (ascending by default)
dotenvx run -- pnpm run cli -- models --sort cost

Search Models

# Search for specific models
dotenvx run -- pnpm run cli -- models --search "gpt-4"
dotenvx run -- pnpm run cli -- models --search "gemini"
dotenvx run -- pnpm run cli -- models --search "llama"

Sorting Options

# Sort by different fields
dotenvx run -- pnpm run cli -- models --sort addedDate --desc
dotenvx run -- pnpm run cli -- models --sort contextLength
dotenvx run -- pnpm run cli -- models --sort cost
dotenvx run -- pnpm run cli -- models --sort name

Model Information

Detailed Model Info

Get comprehensive information about a specific model:

# Basic model info (finds first match across all providers)
dotenvx run -- pnpm run cli -- models --view info --id <model-id>

# Examples
dotenvx run -- pnpm run cli -- models --view info --id openai/gpt-4o
dotenvx run -- pnpm run cli -- models --view info --id gemini-3-flash-preview
dotenvx run -- pnpm run cli -- models --view info --id gemma3:12b

# Get info for a specific provider's version of a model
dotenvx run -- pnpm run cli -- models --provider github-models --view info --id openai/gpt-4.1
dotenvx run -- pnpm run cli -- models --provider openrouter --view info --id openai/gpt-4o
dotenvx run -- pnpm run cli -- models --provider google --view info --id gemini-3-flash-preview

Cost Analysis

View cost breakdown across all models:

# View all model costs
dotenvx run -- pnpm run cli -- models costs

# Sort by different cost metrics
dotenvx run -- pnpm run cli -- models costs --sort-by prompt
dotenvx run -- pnpm run cli -- models costs --sort-by completion
dotenvx run -- pnpm run cli -- models costs --sort-by total

The costs command shows:

• Model: Model identifier
• Provider: Which service hosts the model (ollama, openrouter, google, github-models, lmstudio)
• Prompt: Cost per 1M input tokens
• Completion: Cost per 1M output tokens
• Total: Combined cost per 1M tokens

Models are sorted by the specified metric (default: total cost).

Understanding the Output

Model List Table

Found 150+ models

┌───────────────────┬────────────┬─────────┬───────────────┬────────────────┬────────┐
│ ID                │ Provider   │ Context │ Input Cost/1M │ Output Cost/1M │ Added  │
├───────────────────┼────────────┼─────────┼───────────────┼────────────────┼────────┤
│ openai/gpt-4o     │ openrouter │ 128K    │ $2.5000       │ $10.0000       │ 5/12/24│
│ openai/gpt-4o-mini│ openrouter │ 128K    │ $0.1500       │ $0.6000        │ 7/17/24│
│ gemini-3-flash-preview  │ google     │ 1M      │ $0.0750       │ $0.3000        │ 12/11/24│
│ gemma3:12b        │ ollama     │ 8K      │ Free          │ Free           │ 7/15/25│
└───────────────────┴────────────┴─────────┴───────────────┴────────────────┴────────┘

Key Information

• ID: Model identifier used in commands
• Provider: Which service hosts the model
• Context: Maximum context window (8K, 128K, 1M, etc.)
• Input/Output Cost: Cost per 1 million tokens
• Added: When the model was added to our database

Model Selection Tips

For Development & Testing

• Free models: Ollama models (gemma3:12b, llama3.2:latest)
• Fast & cheap: Google Gemini 2.0 Flash, OpenAI GPT-4o-mini

For Production

• High quality: OpenAI GPT-4o, Google Gemini 2.5 Pro
• Cost-effective: Google Gemini 2.0 Flash, OpenAI GPT-4o-mini

For Specialized Tasks

• Vision: Google Gemini 2.0 Flash, Ollama qwen2.5vl:latest
• Code: Ollama codestral:latest, OpenAI GPT-4o
• Long context: Google Gemini models (up to 2M tokens)

Provider-Specific Notes

Ollama

• Requires local Ollama server running
• Models must be pulled locally first: ollama pull model-name
• Free to use (only compute costs)
• Best for privacy and local development

Google Gemini

• Excellent vision capabilities
• Very large context windows (up to 2M tokens)
• Competitive pricing
• Fast inference

OpenRouter

• Access to many different model providers
• Pay-per-use pricing
• Wide variety of models and capabilities
• Good for testing different providers

LM Studio

• Local model hosting
• No API key required
• Models must be downloaded and loaded in LM Studio
• Full privacy and control

GitHub Models

• Free access during preview period
• Requires GitHub Personal Access Token with models scope
• Access to models from OpenAI, Meta, DeepSeek, and other providers
• OpenAI-compatible API interface
• Models are fetched from https://models.github.ai/catalog/models
• Use dotenvx run to load GITHUB_TOKEN from .env:

  dotenvx run -- dotenvx run -- pnpm run cli -- models --provider github-models

Note: GitHub Models API (models.github.ai) is different from GitHub Copilot (the IDE tool).

• GitHub Copilot has access to Anthropic/Claude models (see GitHub Copilot supported models)
• GitHub Models API currently does not include Anthropic models - it only includes models from OpenAI, Meta, DeepSeek, AI21 Labs, Cohere, Mistral AI, xAI, and Microsoft
• To access Anthropic models, use the OpenRouter provider instead

Next Steps

Once you've discovered suitable models:

• Run basic prompts to test functionality
• Start interactive chat for extended conversations
• Start model evaluation for systematic comparison
• Use cost analysis to optimize spending