> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.
# Model Capabilities
> Comprehensive model feature detection, comparison, and capability matching for optimal AI model selection.
```mermaid theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
flowchart TB
subgraph Models
GPT4[GPT-4
Advanced Reasoning]
Claude[Claude 3
Long Context]
Gemini[Gemini Pro
Multimodal]
Llama[Llama 3
Open Source]
Mistral[Mistral
Efficient]
end
subgraph Capabilities
direction LR
C1[Context Length]
C2[Reasoning]
C3[Code Generation]
C4[Multimodal]
C5[Languages]
C6[Speed]
C7[Cost]
end
subgraph Detection
D1[Analyze Task]
D2[Match Capabilities]
D3[Score Models]
D4[Select Best]
end
Models --> Detection
Capabilities --> Detection
Detection --> Output[Optimal Model]
style Models fill:#2E8B57,color:#fff
style Capabilities fill:#4682B4,color:#fff
style Detection fill:#8B0000,color:#fff
style Output fill:#FFD700,color:#000
```
The Model Capabilities system provides comprehensive feature detection and comparison across different LLMs, enabling intelligent model selection based on specific task requirements.
## Quick Start
Install the PraisonAI Agents package:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
pip install praisonaiagents
```
Configure your model API keys:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export OPENAI_API_KEY=your_openai_key
export ANTHROPIC_API_KEY=your_anthropic_key
export GOOGLE_API_KEY=your_google_key
```
Create `model_capabilities.py`:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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}")
```
Execute the capabilities demo:
```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
python model_capabilities.py
```
## Understanding 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
Automatically detect model capabilities and limitations.
Track speed, accuracy, and cost metrics for each model.
Match task requirements with model strengths.
Compare models side-by-side for informed decisions.
## Model Capability Profiles
### GPT-4 Family
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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 theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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"
)
```
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# 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
Keep capability profiles up-to-date:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
# Schedule regular capability updates
detector.schedule_capability_refresh(
interval="weekly",
models="all",
notify_on_changes=True
)
```
Create detailed task profiles for better matching:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
task_profile = {
"name": "financial_analysis",
"required_capabilities": ["numerical_reasoning", "data_analysis"],
"performance_weights": {
"accuracy": 0.4,
"speed": 0.2,
"cost": 0.4
}
}
```
Track actual performance vs. expected capabilities:
```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
detector.enable_performance_monitoring(
track_accuracy=True,
track_latency=True,
track_cost=True,
alert_on_deviation=0.2 # Alert if 20% deviation
)
```
## Troubleshooting
If models aren't performing as expected:
* Update capability profiles
* Run fresh benchmarks
* Check API version changes
* Verify task requirements
If capability detection is slow:
* Use cached results
* Run async benchmarks
* Limit test scope
* Use sampling strategies
## Model-Specific Features
Models with image understanding:
* GPT-4V
* Gemini Pro Vision
* Claude 3 (all variants)
Optimized for programming:
* GPT-4
* DeepSeek Coder
* Code Llama
Extended context windows:
* Claude 3 (200k)
* GPT-4 Turbo (128k)
* Gemini 1.5 Pro (1M)
## Next Steps
Learn about intelligent model routing based on capabilities
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.