Praison AI

Model Capabilities

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

python
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.

Diagram

Model Capabilities

📋 Task

🔍 Detect Capabilities

⚙️ Score Models

✅ Optimal Model

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:

    bash
    pip install praisonaiagents
  2. Set API Keys

    Configure your model API keys:

    bash
    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:

    python
    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:

    bash
    python model_capabilities.py

How It Works

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

Diagram
ModelsDetectorUserModelsDetectorUserrecommend_model(requirements)Compare capabilitiesCapability scoresRank by match scoreBest-fit model

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

python
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

python
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

python
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

python
# 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

python
# 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

python
# 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

python
# 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:

python
# 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:

python
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:

python
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

Related

Card

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

 Card

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