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Models Command

List and filter available models across all providers with rich metadata.

Command: lc models (alias: lc m)​

Basic Usage​

List all available models with metadata:

lc models
lc m

Output shows models with capability indicators:

  • 🔧 tools - Function calling support
  • 👁 vision - Image processing
  • 🧠 reasoning - Advanced reasoning
  • 💻 code - Code generation
  • 🔊 audio - Audio processing
  • Context length (e.g., "200k ctx")
  • Human-readable names

Filtering Options​

Search by Name​

lc models -q <query>
lc m -q claude

Filter by Capabilities​

# Models with function calling
lc models --tools

# Vision models
lc models --vision

# Reasoning models
lc models --reasoning

# Code generation models
lc models --code

# Audio models
lc models --audio

Filter by Context Length​

# Minimum 128k context
lc models --ctx 128k

# Minimum 200k context
lc models --ctx 200k

Filter by Token Limits​

# Minimum input tokens
lc models --input 100k

# Minimum output tokens
lc models --output 32k

Filter by Price​

# Max input price per million tokens
lc models --input-price 10

# Max output price per million tokens
lc models --output-price 20

Combining Filters​

Filters can be combined for precise results:

# Vision models with 128k+ context
lc models --vision --ctx 128k

# Code models with function calling
lc models --code --tools

# Claude models with reasoning
lc models -q claude --reasoning

# Affordable models with large context
lc models --ctx 100k --input-price 5

Subcommands​

Refresh Cache​

Update the models cache:

lc models refresh
lc m r

Show Cache Info​

Display cache statistics:

lc models info
lc m i

Output shows:

  • Cache location
  • Last update time
  • Number of providers
  • Total models cached

Dump Raw Data​

Export raw provider responses:

lc models dump
lc m d

Outputs JSON data for debugging or analysis.

List Embedding Models​

Show only embedding models:

lc models embed
lc m e

Model Metadata Configuration​

Manage how model metadata is extracted from provider APIs:

Add Model Path​

Add a new JSON path for extracting models from provider responses:

lc models add-path ".results[]"
lc models add-path ".data.models[]"
Remove Model Path​

Remove a JSON path from the extraction configuration:

lc models remove-path ".results[]"
List Model Paths​

Show all configured model extraction paths:

lc models list-paths

Output shows the JQ-style paths used to extract model arrays from different provider API responses.

Add Tag Rule​

Add a new tag extraction rule for model metadata:

# Add a boolean tag
lc models add-tag "supports_streaming" ".streaming_enabled,.features.streaming" "bool"

# Add a numeric tag with transform
lc models add-tag "max_tokens" ".limits.max_tokens" "u32"

# Add a price tag with million multiplier
lc models add-tag "input_cost" ".pricing.input" "f64" "multiply_million"

Parameters:

  • name: Tag name (e.g., "supports_streaming")
  • paths: Comma-separated JSON paths to check
  • type: Value type ("bool", "u32", "f64", "string")
  • transform: Optional transform ("multiply_million")
List Tag Rules​

Show all configured tag extraction rules:

lc models list-tags

Output shows:

  • Tag names and types
  • JSON paths for each tag
  • Transform functions (if any)

Model Metadata Configuration​

The models command uses a configurable metadata extraction system that can be customized for different providers and API formats.

Configuration Files​

Two configuration files control metadata extraction:

model_paths.toml​

Defines JSON paths for extracting model arrays from provider API responses:

paths = [
    ".data[]",      # OpenAI format
    ".models[]",    # Anthropic format
    ".results[]",   # Custom provider format
    "."             # Single model response
]

tags.toml​

Defines rules for extracting metadata fields from model objects:

[tags.context_length]
paths = [".context_length", ".context_window", ".max_context_length"]
value_type = "u32"

[tags.supports_vision]
paths = [".supports_vision", ".capabilities.vision"]
value_type = "bool"

[tags.input_price_per_m]
paths = [".pricing.prompt", ".pricing.input.usd"]
value_type = "f64"
transform = "multiply_million"

Configuration Location​

Configuration files are automatically created in:

  • Linux/macOS: ~/.config/lc/
  • Windows: %APPDATA%\lc\

Adding New Providers​

To support a new provider's API format:

  1. Add model extraction path:

    lc models add-path ".your_provider_models[]"

  2. Add custom metadata tags (if needed):

    lc models add-tag "custom_field" ".provider_specific_field" "string"

  3. Test extraction:

    lc models refresh
    lc models -q your_provider

HuggingFace Support​

The system includes special handling for HuggingFace models that have multiple providers. Models with a providers array are automatically expanded into separate entries for each provider.

Model Metadata Display​

Each model displays rich metadata when available:

openai:
  • gpt-4-turbo-preview 🔧 👁 💻 (128k ctx) (GPT-4 Turbo Preview)
  • gpt-4 🔧 💻 (8k ctx) (GPT-4)
  • gpt-3.5-turbo 🔧 (16k ctx) (GPT-3.5 Turbo)

Capability Indicators​

  • 🔧 tools - Supports function calling/tool use
  • 👁 vision - Can process images
  • 🧠 reasoning - Advanced reasoning capabilities
  • 💻 code - Optimized for code generation
  • 🔊 audio - Can process audio input

Context Information​

Shows maximum context window:

  • (8k ctx) - 8,000 tokens
  • (128k ctx) - 128,000 tokens
  • (200k ctx) - 200,000 tokens

Display Names​

Human-readable names in parentheses:

  • (GPT-4 Turbo) - Marketing name
  • (Claude 3.5 Sonnet) - Version info

Examples​

Find Specific Models​

# All GPT models
lc models -q gpt

# Claude models
lc models -q claude

# Llama models
lc models -q llama

Find Models by Use Case​

# For code review (code + reasoning)
lc models --code --reasoning

# For image analysis
lc models --vision

# For long documents
lc models --ctx 100k

# For production (with tools)
lc models --tools

Budget-Conscious Selection​

# Cheap models for testing
lc models --input-price 1 --output-price 2

# Best value for large context
lc models --ctx 32k --input-price 5

Configuration Examples​

Adding Support for a New Provider​

# 1. Add the provider's model extraction path
lc models add-path ".models.available[]"

# 2. Add custom metadata fields
lc models add-tag "max_context" ".context.maximum" "u32"
lc models add-tag "supports_json" ".features.json_mode" "bool"

# 3. Refresh and test
lc models refresh
lc models list-tags

Customizing Existing Tags​

# Add alternative paths for context length
lc models add-tag "context_length" ".ctx_len,.context_size,.max_tokens" "u32"

# Add pricing with custom transform
lc models add-tag "cost_per_token" ".pricing.per_token" "f64" "multiply_million"

Managing Configuration​

# View current model paths
lc models list-paths

# View current tag rules
lc models list-tags

# Remove unused paths
lc models remove-path ".deprecated_format[]"

Cache Management​

The models command uses a local cache to improve performance:

  • Location: Platform-specific config directory
  • Automatic refresh: When cache is stale
  • Manual refresh: lc models refresh
  • Cache duration: 24 hours (configurable)

Troubleshooting​

"No models found"​

  1. Refresh the cache:

    lc models refresh

  2. Check provider configuration:

    lc providers list

  3. Verify API keys are set

  4. Check model extraction paths:

    lc models list-paths

"Cache error"​

  1. Clear cache and refresh:

    rm ~/.config/lc/models_cache.json
    lc models refresh

  2. Check disk space and permissions

Missing Models​

Some providers may not expose all models via API:

  • Check provider documentation
  • Use lc providers models <provider> for direct query
  • Some models may require special access

Missing Metadata​

If models appear but lack metadata (no capability icons):

  1. Check tag configuration:

    lc models list-tags

  2. Add missing tag rules:

    lc models add-tag "supports_tools" ".tools_enabled,.capabilities.functions" "bool"

  3. Refresh cache:

    lc models refresh

New Provider Not Working​

If a new provider's models aren't appearing:

  1. Check API response format (use lc models dump):

    lc models dump | jq '.your_provider'

  2. Add appropriate model path:

    # If models are in .data.models array
    lc models add-path ".data.models[]"

    # If models are in .results array
    lc models add-path ".results[]"

  3. Test extraction:

    lc models refresh
    lc models -q your_provider

Configuration Issues​

  1. Reset to defaults:

    rm ~/.config/lc/model_paths.toml
    rm ~/.config/lc/tags.toml
    lc models refresh  # Will recreate with defaults

  2. Check configuration location:

    • Linux/macOS: ~/.config/lc/
    • Windows: %APPDATA%\lc\

  3. Validate TOML syntax:

    # Check if files are valid TOML
    cat ~/.config/lc/tags.toml

See Also​

Next Steps​