# Venice List Text Models List all available text/LLM models from the [Venice.ai](https://venice.ai/) API with context windows, pricing, capabilities, and traits. ## Features - Lists all available LLM/text models - Shows context window sizes, input/output pricing per million tokens - Displays capabilities (vision, reasoning, function calling, code optimization, web search) - Filter by trait (e.g., `most_intelligent`, `default`, `most_uncensored`) - Capabilities summary across all models - Structured Pydantic models for programmatic use ## Prerequisites ```bash pip install requests pydantic export VENICE_API_KEY="your_venice_api_key" ``` ## Usage ### List all models ```bash python scripts/list_text_models.py ``` ### Filter by trait ```bash python scripts/list_text_models.py most_intelligent python scripts/list_text_models.py default ``` ## Output Displays a formatted table sorted by context window size: ``` Model ID Name Context In $/M Out $/M Traits ------------------------------------------------------------------------------------------------------------------------ qwen3-235b-a22b-thinking-2507 Qwen3 235B Thinking 250K 0.50 2.00 most_intelligent ... ``` Plus capabilities summary: ``` === Capabilities Summary === total: 15 with_reasoning: 4 with_vision: 6 with_function_calling: 8 with_web_search: 10 optimized_for_code: 3 ``` ## Python Import ```python from list_text_models import list_text_models, get_capabilities_summary # All models models = list_text_models() # Filtered by trait intelligent = list_text_models(filter_trait="most_intelligent") # Capabilities summary summary = get_capabilities_summary(models) print(f"Models with vision: {summary['with_vision']}") # Access individual models for m in models.data: cap = m.model_spec.capabilities print(f"{m.id}: vision={cap.supportsVision}, reasoning={cap.supportsReasoning}") ``` ## Environment Variables | Variable | Required | Description | |----------|----------|-------------| | `VENICE_API_KEY` | Yes | Venice.ai API key |