Model Capabilities
Detailed breakdown of what each AI model can do. Understand strengths, limitations, and the best use cases for every capability.
Core Capabilities
Text Generation
Generate high-quality text content for various purposes
| Model | Supported | Quality | Notes |
|---|---|---|---|
| GPT-4 Turbo | Excellent for complex writing tasks | ||
| Claude 3 Opus | Superior for creative and analytical writing | ||
| Claude 3 Sonnet | Balanced performance for most text tasks | ||
| Claude 3 Haiku | Good for simple text generation | ||
| Gemini 1.5 Pro | Strong multilingual text generation | ||
| GPT-3.5 Turbo | Fast and reliable for most text tasks |
Code Generation
Write, debug, and explain code in multiple programming languages
| Model | Supported | Quality | Notes |
|---|---|---|---|
| GPT-4 Turbo | Excellent across all programming languages | ||
| Claude 3 Opus | Superior code analysis and generation | ||
| Claude 3 Sonnet | Optimized for code tasks | ||
| Claude 3 Haiku | Basic code assistance | ||
| Gemini 1.5 Pro | Strong in Python and JavaScript | ||
| GPT-3.5 Turbo | Good for common programming tasks |
Vision & Image Analysis
Analyze, describe, and understand images and visual content
| Model | Supported | Quality | Notes |
|---|---|---|---|
| GPT-4 Turbo | Good image understanding and OCR | ||
| Claude 3 Opus | Excellent detailed image analysis | ||
| Claude 3 Sonnet | Solid image understanding | ||
| Claude 3 Haiku | Basic image description | ||
| Gemini 1.5 Pro | Strong multimodal understanding | ||
| GPT-3.5 Turbo | N/A | No vision capabilities |
Function Calling
Call external functions and APIs with structured parameters
| Model | Supported | Quality | Notes |
|---|---|---|---|
| GPT-4 Turbo | Excellent function calling with complex parameters | ||
| Claude 3 Opus | Good function calling capabilities | ||
| Claude 3 Sonnet | Reliable function calling | ||
| Claude 3 Haiku | N/A | No function calling support | |
| Gemini 1.5 Pro | N/A | Limited function calling | |
| GPT-3.5 Turbo | Good function calling for simple tasks |
Reasoning & Analysis
Complex logical reasoning, problem-solving, and analytical thinking
| Model | Supported | Quality | Notes |
|---|---|---|---|
| GPT-4 Turbo | Excellent complex reasoning | ||
| Claude 3 Opus | Superior analytical capabilities | ||
| Claude 3 Sonnet | Strong reasoning for most tasks | ||
| Claude 3 Haiku | Basic reasoning capabilities | ||
| Gemini 1.5 Pro | Strong mathematical reasoning | ||
| GPT-3.5 Turbo | Good for simple reasoning tasks |
Conversational AI
Natural, engaging conversations with context awareness
| Model | Supported | Quality | Notes |
|---|---|---|---|
| GPT-4 Turbo | Excellent conversational abilities | ||
| Claude 3 Opus | Natural and thoughtful conversations | ||
| Claude 3 Sonnet | Good conversational flow | ||
| Claude 3 Haiku | Fast, responsive conversations | ||
| Gemini 1.5 Pro | Good conversation with long context | ||
| GPT-3.5 Turbo | Reliable conversational partner |
Special Features
JSON Mode
Force structured JSON output for API responses
Supported Models:
Ensures valid JSON output format, perfect for API integrations and structured data extraction.
Streaming
Real-time token streaming for faster user experience
Supported Models:
All models support streaming responses for improved perceived performance in chat applications.
Large Context Windows
Handle very long inputs and maintain context
Supported Models:
Process entire documents, codebases, or long conversations without losing context.
Multimodal Input
Process multiple types of media in a single request
Supported Models:
Combine text, images, and other media types for rich, contextual understanding.
Context Window Comparison
Gemini 1.5 Pro
Up to 1 million tokens (~750,000 words)
Claude 3 Models
Up to 200,000 tokens (~150,000 words)
GPT-4 Turbo
Up to 128,000 tokens (~96,000 words)
GPT-3.5 Turbo
Up to 16,000 tokens (~12,000 words)
What does this mean?
Context window determines how much text the model can process at once. Larger context windows allow for: processing entire documents, maintaining longer conversations, analyzing large codebases, and handling complex multi-step tasks.
Capability Best Practices
Code Generation
- • Be specific about language and requirements
- • Include context about the project structure
- • Ask for explanations of complex logic
- • Request error handling and edge cases
Vision Tasks
- • Use high-quality, clear images
- • Be specific about what to analyze
- • Consider image resolution and format
- • Combine with text for better context
Function Calling
- • Define clear function schemas
- • Include parameter descriptions
- • Handle errors gracefully
- • Validate function outputs
Complex Reasoning
- • Break down complex problems
- • Ask for step-by-step solutions
- • Provide relevant context and constraints
- • Request explanations of reasoning
Long Context
- • Structure long inputs clearly
- • Use headers and sections
- • Be specific about what to focus on
- • Consider chunking very large texts
Conversations
- • Maintain consistent context
- • Set clear expectations upfront
- • Use system messages effectively
- • Handle conversation turns naturally
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