Model Capabilities

Comprehensive model feature detection, comparison, and capability matching for optimal AI model selection.

from praisonaiagents import Agent

agent = Agent(name="capability-router", instructions="Match tasks to model strengths.")
agent.start("Which model fits a 200k-token code review?")

The user describes a task; capability detection scores models and selects the best fit.

The Model Capabilities system provides comprehensive feature detection and comparison across different LLMs, enabling intelligent model selection based on specific task requirements.

Quick Start

1. Install Package

   Install the PraisonAI Agents package:

   pip install praisonaiagents
   
2. Set API Keys

   Configure your model API keys:

   export OPENAI_API_KEY="${OPENAI_API_KEY:?Set OPENAI_API_KEY in your shell}"
   export ANTHROPIC_API_KEY=your_anthropic_key
   export GOOGLE_API_KEY=your_google_key
   
3. Create Example

   Create model_capabilities.py:

   from praisonaiagents.llm import ModelCapabilities
   from praisonaiagents import Agent, Task, AgentTeam
   
   # Initialize model capabilities detector
   capabilities = ModelCapabilities()
   
   # Analyze available models
   print("Available Models and Their Capabilities:")
   print("=" * 50)
   
   models = capabilities.list_models()
   for model in models:
       caps = capabilities.get_capabilities(model)
       print(f"\n{model}:")
       print(f"  Context: {caps['max_context']} tokens")
       print(f"  Strengths: {', '.join(caps['strengths'])}")
       print(f"  Cost: ${caps['cost_per_1k_tokens']}/1k tokens")
   
   # Compare models for specific task
   task_requirements = {
       "type": "code_generation",
       "complexity": "high",
       "language": "python",
       "context_needed": 8000
   }
   
   best_model = capabilities.recommend_model(task_requirements)
   print(f"\nBest model for task: {best_model}")
   
   # Create agent with capability-aware model selection
   smart_agent = Agent(
       name="Capability-Aware Agent",
       role="Adaptive AI Assistant",
       goal="Use the best model based on task requirements",
       instructions="Leverage model capabilities for optimal performance",
       model_selector=capabilities
   )
   
   # Test with different task types
   coding_task = Task(
       description="Write a complex Python algorithm for graph traversal",
       expected_output="Optimized Python code with explanation",
       agent=smart_agent,
       task_type="coding"
   )
   
   analysis_task = Task(
       description="Analyze 100-page financial report",
       expected_output="Comprehensive financial analysis",
       agent=smart_agent,
       task_type="long_context_analysis"
   )
   
   creative_task = Task(
       description="Write a creative short story",
       expected_output="Engaging narrative",
       agent=smart_agent,
       task_type="creative_writing"
   )
   
   # Run workflow
   workflow = AgentTeam(
       agents=[smart_agent],
       tasks=[coding_task, analysis_task, creative_task],
       
   )
   
   results = workflow.start()
   
   # Show capability matching results
   print("\nCapability Matching Results:")
   for task, model in capabilities.get_task_model_mapping().items():
       print(f"{task}: {model}")
   
4. Run Example

   Execute the capabilities demo:

   python model_capabilities.py
   

How It Works

The user describes a task; the detector scores each model’s capabilities and returns the best match.

---

Understanding Model Capabilities

What are Model Capabilities?

Model Capabilities system:

• Detects and compares features across different LLMs
• Matches task requirements with model strengths
• Provides performance benchmarks and comparisons
• Enables data-driven model selection
• Tracks capability evolution over time

Core Features

Feature Detection

Automatically detect model capabilities and limitations.

Performance Metrics

Track speed, accuracy, and cost metrics for each model.

Capability Matching

Match task requirements with model strengths.

Comparison Tools

Compare models side-by-side for informed decisions.

Model Capability Profiles

GPT-4 Family

gpt4_capabilities = {
    "gpt-4": {
        "max_context": 128000,
        "strengths": ["reasoning", "coding", "analysis", "math"],
        "weaknesses": ["speed", "cost"],
        "languages": 95,
        "multimodal": False,
        "cost_per_1k_tokens": {"input": 0.03, "output": 0.06},
        "latency": "medium",
        "use_cases": ["complex reasoning", "code generation", "technical analysis"]
    },
    "gpt-4-turbo": {
        "max_context": 128000,
        "strengths": ["reasoning", "speed", "cost-efficiency"],
        "weaknesses": ["very long contexts"],
        "languages": 95,
        "multimodal": True,
        "cost_per_1k_tokens": {"input": 0.01, "output": 0.03},
        "latency": "low",
        "use_cases": ["general tasks", "multimodal analysis", "rapid responses"]
    }
}

Claude 3 Family

claude3_capabilities = {
    "claude-3-opus": {
        "max_context": 200000,
        "strengths": ["long_context", "reasoning", "writing", "safety"],
        "weaknesses": ["cost", "speed"],
        "languages": 80,
        "multimodal": True,
        "cost_per_1k_tokens": {"input": 0.015, "output": 0.075},
        "latency": "high",
        "use_cases": ["document analysis", "creative writing", "complex tasks"]
    },
    "claude-3-sonnet": {
        "max_context": 200000,
        "strengths": ["balance", "long_context", "efficiency"],
        "weaknesses": ["specialized tasks"],
        "languages": 80,
        "multimodal": True,
        "cost_per_1k_tokens": {"input": 0.003, "output": 0.015},
        "latency": "medium",
        "use_cases": ["general purpose", "document processing", "balanced tasks"]
    }
}

Capability Detection Methods

from praisonaiagents.llm import ModelCapabilities, CapabilityTest

# Initialize capability detector
detector = ModelCapabilities()

# Run comprehensive capability tests
test_suite = CapabilityTest()

# Test reasoning capabilities
reasoning_score = test_suite.test_reasoning("gpt-4")
print(f"Reasoning score: {reasoning_score}/100")

# Test context handling
context_test = test_suite.test_context_length("claude-3-sonnet", tokens=150000)
print(f"Context handling: {'Passed' if context_test else 'Failed'}")

# Test multilingual support
languages = test_suite.test_languages("gpt-4")
print(f"Supported languages: {len(languages)}")

# Test specialized capabilities
capabilities_matrix = test_suite.run_full_test_suite([
    "gpt-4",
    "claude-3-opus",
    "gemini-pro",
    "llama-3-70b"
])

# Generate capability report
detector.generate_capability_report(capabilities_matrix, "model_comparison.html")

Advanced Capability Matching

Task-Based Selection

# Define task profiles
task_profiles = {
    "code_review": {
        "required": ["code_understanding", "bug_detection", "suggestions"],
        "preferred": ["fast_response", "cost_effective"],
        "context": 10000
    },
    "research_paper": {
        "required": ["long_context", "academic_writing", "citations"],
        "preferred": ["reasoning", "fact_checking"],
        "context": 50000
    },
    "real_time_chat": {
        "required": ["low_latency", "conversational"],
        "preferred": ["cost_effective", "multilingual"],
        "context": 4000
    }
}

# Match capabilities to tasks
for task_name, requirements in task_profiles.items():
    best_model = detector.match_task_to_model(requirements)
    score = detector.calculate_match_score(best_model, requirements)
    print(f"{task_name}: {best_model} (score: {score:.2f})")

Dynamic Capability Updates

# Monitor and update capabilities based on performance
detector.enable_performance_tracking()

# After task execution
detector.update_capability_metrics(
    model="gpt-4",
    task_type="coding",
    performance_metrics={
        "accuracy": 0.95,
        "speed": 2.3,  # seconds
        "token_efficiency": 0.85
    }
)

# Get updated recommendations based on real performance
updated_recommendation = detector.recommend_with_history(
    task_requirements,
    consider_performance=True
)

Capability Comparison Tools

Visual Comparison

# Generate visual capability comparison
from praisonaiagents.llm import CapabilityVisualizer

visualizer = CapabilityVisualizer()

# Create radar chart comparing models
visualizer.create_radar_chart(
    models=["gpt-4", "claude-3-opus", "gemini-pro"],
    capabilities=["reasoning", "speed", "cost", "context", "languages"],
    output="model_comparison.png"
)

# Create capability matrix heatmap
visualizer.create_heatmap(
    models=detector.list_models(),
    capabilities=detector.list_all_capabilities(),
    output="capability_matrix.png"
)

Benchmark Results

# Run standardized benchmarks
from praisonaiagents.llm import CapabilityBenchmark

benchmark = CapabilityBenchmark()

# Run comprehensive benchmark suite
results = benchmark.run_all_benchmarks([
    "gpt-4",
    "claude-3-opus",
    "gemini-pro",
    "llama-3-70b"
])

# Display results
benchmark.display_results(results, format="table")

# Export for analysis
benchmark.export_results(results, "benchmark_results.csv")

Best Practices

Regular Capability Assessment

Keep capability profiles up-to-date:

# Schedule regular capability updates
detector.schedule_capability_refresh(
    interval="weekly",
    models="all",
    notify_on_changes=True
)

Task-Specific Profiling

Create detailed task profiles for better matching:

task_profile = {
    "name": "financial_analysis",
    "required_capabilities": ["numerical_reasoning", "data_analysis"],
    "performance_weights": {
        "accuracy": 0.4,
        "speed": 0.2,
        "cost": 0.4
    }
}

Performance Monitoring

Track actual performance vs. expected capabilities:

detector.enable_performance_monitoring(
    track_accuracy=True,
    track_latency=True,
    track_cost=True,
    alert_on_deviation=0.2  # Alert if 20% deviation
)

Troubleshooting

Capability Mismatches

If models aren’t performing as expected:

• Update capability profiles
• Run fresh benchmarks
• Check API version changes
• Verify task requirements

Performance Issues

If capability detection is slow:

• Use cached results
• Run async benchmarks
• Limit test scope
• Use sampling strategies

Model-Specific Features

Vision Capabilities

Models with image understanding:

• GPT-4V
• Gemini Pro Vision
• Claude 3 (all variants)

Code Specialists

Optimized for programming:

• GPT-4
• DeepSeek Coder
• Code Llama

Long Context

Extended context windows:

• Claude 3 (200k)
• GPT-4 Turbo (128k)
• Gemini 1.5 Pro (1M)

Next Steps

Model Router

Learn about intelligent model routing based on capabilities

Advanced Memory

Explore memory systems optimized for different models

Model capabilities are constantly evolving. The system automatically updates capability profiles through regular testing and performance monitoring to ensure accurate recommendations.

Related

Route each request to the best model based on its capabilities.

Fall back to an alternate model when the primary one is unavailable.