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Model Database

Explore the world's most comprehensive database of 10,000+ LLM models from 40+ providers. Find the perfect model for your specific needs with advanced search and filtering capabilities.

Database Overview

Database Statistics

Our comprehensive model database contains:

  • 10,000+ Models across all major providers
  • 40+ Providers from OpenAI to open-source platforms
  • 25+ Capabilities tracked per model
  • Real-time Pricing updated daily
  • Performance Metrics from speed to quality ratings
  • Context Windows from 2K to 2M+ tokens

Model Distribution by Provider

Provider Category Model Count Example Models
Premium 2,500+ GPT-4o, Claude 3.5, Gemini 1.5
Open Source 4,200+ Llama 3, Mistral, CodeLlama
Specialized 1,800+ DeepSeek-Coder, Stable Code
Fine-tuned 1,500+ Domain-specific variants

Capability Distribution

pie title Model Capabilities
    "Text Generation" : 10000
    "Chat Completion" : 9500
    "Code Generation" : 6200
    "Function Calling" : 3800
    "Vision/Multimodal" : 2100
    "Reasoning" : 1600
    "Embeddings" : 1200
    "Image Generation" : 800

Quick Model Finder

Find by Use Case

Best Overall: GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro Budget: GPT-4o-mini, Claude 3 Haiku, Gemini 1.5 Flash Free: Llama 3.1 8B, Mistral 7B, Gemma 2 9B

from claif_knollm import ModelRegistry, ModelCapability

registry = ModelRegistry()
models = registry.search_models(
    required_capabilities=[ModelCapability.CHAT_COMPLETION],
    max_cost_per_1k_tokens=0.01,
    min_quality_score=0.8
)

Best: GPT-4o, Claude 3.5 Sonnet, DeepSeek Coder V2 Specialized: CodeLlama 70B, StarCoder2, Codestral Fast: DeepSeek Coder 6.7B, Code Llama 13B

code_models = registry.search_models(
    required_capabilities=[ModelCapability.CODE_GENERATION],
    sort_by="quality_score",
    limit=10
)

Best: GPT-4o, Claude 3.5 Sonnet, Gemini 1.5 Pro Budget: GPT-4o-mini, Gemini 1.5 Flash Specialized: Llava 1.6, Idefics2

vision_models = registry.search_models(
    required_capabilities=[ModelCapability.VISION],
    min_context_window=32000
)

Ultra-long: Google Gemini 1.5 (2M tokens) Long: Claude 3 (200K tokens), GPT-4 Turbo (128K) Extended: Llama 3.1 (128K), Mistral Large (128K)

long_context = registry.search_models(
    min_context_window=100000,
    sort_by="context_window"
)

Find by Budget

Budget Range Cost/1K Tokens Recommended Models
Free $0.0000 Hugging Face models, Ollama
Ultra Budget \(0.0001-\)0.0005 Groq models, DeepSeek
Budget \(0.0005-\)0.002 GPT-4o-mini, Gemini Flash
Standard \(0.002-\)0.01 Claude Haiku, Mistral models
Premium $0.01+ GPT-4o, Claude Sonnet, o1

Model Search Examples

# Basic search
knollm models search --query "gpt-4"

# Advanced filtering
knollm models search \
  --capability code_generation \
  --capability function_calling \
  --max-cost 0.01 \
  --min-context 32000 \
  --provider openai anthropic

# Find cheapest models
knollm models cheapest --capability vision --limit 5

# Compare specific models
knollm models compare gpt-4o-mini claude-3-haiku gemini-1.5-flash

from claif_knollm import ModelRegistry, SearchFilter, ModelCapability
from decimal import Decimal

registry = ModelRegistry()

# Complex search with multiple criteria
search_filter = SearchFilter(
    query="coding assistant",
    required_capabilities=[
        ModelCapability.CODE_GENERATION,
        ModelCapability.FUNCTION_CALLING
    ],
    max_cost_per_1k_tokens=Decimal("0.005"),
    min_context_window=32000,
    providers=["openai", "anthropic", "mistral"],
    active_only=True,
    limit=10
)

results = registry.search_models(search_filter)

print(f"Found {len(results.models)} models:")
for model in results.models:
    print(f"  {model.id} ({model.provider}) - ${model.metrics.cost_per_1k_input_tokens}")

Model Quality Metrics

Performance Scoring

Each model is evaluated across multiple dimensions:

Metric Description Range Weight
Quality Overall response quality 0.0-1.0 40%
Speed Tokens per second Measured 25%
Cost Price per 1K tokens USD 20%
Reliability Uptime & consistency 0.0-1.0 10%
Features Capability breadth Count 5%

Quality Ratings

  • 🌟🌟🌟🌟🌟 Excellent (0.9-1.0) - Top-tier models like GPT-4o, Claude 3.5
  • 🌟🌟🌟🌟 Very Good (0.8-0.89) - High-quality models like GPT-4o-mini
  • 🌟🌟🌟 Good (0.7-0.79) - Solid performers like Llama 3.1 70B
  • 🌟🌟 Fair (0.6-0.69) - Decent models like Mistral 7B
  • 🌟 Basic (0.5-0.59) - Entry-level models

Cost Optimization Tools

Automatic Cost Optimization

from claif_knollm import ModelRegistry

registry = ModelRegistry()

# Find the cheapest model meeting your requirements
optimal_model = registry.find_optimal_model(
    required_capabilities=[ModelCapability.CHAT_COMPLETION],
    min_quality_score=0.8,
    max_cost_per_1k_tokens=0.005
)

print(f"Optimal model: {optimal_model.id}")
print(f"Cost: ${optimal_model.metrics.cost_per_1k_input_tokens}")
print(f"Quality: {optimal_model.metrics.quality_score}")

Cost Comparison

# Compare costs across providers
cost_comparison = registry.compare_model_costs([
    "gpt-4o-mini",
    "claude-3-haiku", 
    "gemini-1.5-flash",
    "llama-3.1-8b-instant"
])

for model, cost in cost_comparison.items():
    print(f"{model}: ${cost}/1K tokens")

Real-Time Data

Live Updates

The model database is continuously updated with:

  • Daily Price Updates - Latest pricing from all providers
  • New Model Detection - Automatic discovery of new releases
  • Performance Monitoring - Real-time speed and availability tracking
  • Capability Analysis - Automated testing of model capabilities

Data Sources

Our data comes from:

  • Official Provider APIs - Direct integration with provider endpoints
  • Community Benchmarks - Crowd-sourced performance data
  • Automated Testing - Regular capability and quality assessments
  • Manual Curation - Expert review and validation

Advanced Features

Smart Recommendations

# Get personalized recommendations based on usage history
recommendations = registry.get_recommendations(
    usage_history=user_requests,
    preferences={"cost_weight": 0.6, "quality_weight": 0.4}
)

# Find similar models to one you like
similar_models = registry.find_similar_models(
    "gpt-4o-mini", 
    similarity_threshold=0.8
)

Batch Analysis

# Analyze multiple models at once
models_to_analyze = ["gpt-4o", "claude-3-5-sonnet", "gemini-1.5-pro"]
analysis = registry.batch_analyze_models(
    models_to_analyze,
    criteria=["cost", "speed", "quality", "context_window"]
)

Getting Started

Ready to explore the model database?

  1. Search Models → - Find models with advanced filters
  2. Model Capabilities → - Understand what models can do
  3. Pricing Guide → - Optimize your costs
  4. Full Database → - Browse all available models

🚀 Quick Start

Try this in your Python environment:

from claif_knollm import ModelRegistry

registry = ModelRegistry()
cheap_models = registry.get_cheapest_models(limit=5)

for model in cheap_models:
    print(f"{model.id}: ${model.metrics.cost_per_1k_input_tokens}")