AI Capabilities Matrix
Compare AI model features — streaming, function calling, vision, JSON mode, and more
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What Are AI Model Capabilities?
Not all AI models are created equal. While they may all generate text, different models support different features like streaming responses, processing images, calling functions, or outputting structured JSON. This matrix helps you quickly identify which models support the capabilities you need for your application.
Choosing a model with the right capabilities is essential for building reliable AI applications. Using a model that lacks a required feature means you'll need complex workarounds or will face unexpected limitations in production.
This data is fetched from provider APIs and updated regularly to reflect the latest model features and capabilities across all major AI providers.
How to Use This Tool
Identify Required Capabilities
Before comparing, list the features your application needs: streaming for chat UIs, function calling for AI agents, vision for image processing, JSON mode for structured data extraction.
Filter by Capability
Use the dropdown to filter to only models with a specific capability. This quickly narrows the list to viable options for your requirements.
Compare Across Providers
Filter by provider to see available capabilities within a specific ecosystem, or compare how different providers implement the same features.
Copy as Markdown
Export the capability matrix as Markdown for documentation or to share with your team during model selection discussions.
Capability Definitions
Streaming
Receive response tokens as they're generated instead of waiting for the complete response. Essential for chat interfaces and real-time applications.
Function Calling (Tools)
Model can identify when to call external tools and format required parameters. Core feature for AI agents and integrations with external services.
Vision
Process and understand images alongside text. Required for image analysis, document processing, OCR-like tasks, or describing visual content.
JSON Mode
Guarantee that the model outputs valid JSON. Critical for applications that need to parse structured data from model responses reliably.
Audio
Process audio input or generate audio output natively. Enables voice assistants and audio transcription without separate ASR/TTS services.
Fine-Tuning
Train a custom version of the model on your own data. Enables specialized behavior without complex prompting for domain-specific tasks.
Batching
Send multiple requests for async processing together, often at reduced cost (up to 50% off). Ideal for bulk processing non-time-sensitive tasks.
Pro Tip: Minimum Viable Capabilities
Don't over-spec! Each additional capability typically comes with higher costs or fewer model options. Identify your true must-haves vs nice-to-haves. For example, if you only need JSON occasionally, robust prompting may be cheaper than limiting yourself to models with native JSON mode.
Capabilities by Use Case
| Use Case | Required Capabilities | Recommended Models |
|---|---|---|
| Chat Interface | Streaming | All major models |
| AI Agent | Function Calling + Streaming | GPT-4o, Claude 3.5, Gemini |
| Document Processing | Vision + JSON Mode | GPT-4o, Claude 3, Gemini 1.5 |
| Data Extraction | JSON Mode | GPT-4o-mini, Claude, Gemini |
| Voice Assistant | Audio + Streaming | GPT-4o, Gemini 2.0 |
Important: Implementation Varies
Even when two models support the same capability, implementations differ. OpenAI and Anthropic have different function calling formats. Vision quality varies between models. Always test capabilities with your actual use cases before committing to a model in production.
Frequently Asked Questions
Which capability is most important?
It depends entirely on your use case. For chatbots, streaming is essential for UX. For AI agents, function calling is critical. For image-related tasks, vision is required. For structured data extraction, JSON mode prevents parsing errors. Identify your must-haves before choosing a model.
Can I use function calling with all providers?
Not all models support function calling equally. OpenAI and Anthropic have robust implementations. Google Gemini also supports it. Some open-source models have function calling via community implementations. Implementation details and reliability vary, so always test thoroughly.
What if a model doesn't have JSON mode?
You can still ask for JSON in your prompt, but there's no guarantee the output will be valid. Best practices: use robust parsing with error handling, include JSON examples in your prompt, retry on parsing failures, or choose a model with native structured output support.
Do smaller models have the same capabilities?
Often yes, but quality may differ. GPT-4o-mini supports the same capabilities as GPT-4o but may have lower accuracy on complex tasks. Claude 3 Haiku has most Claude 3 Opus features. Test smaller models for your specific tasks — they're much cheaper and often good enough.
How do I handle capability differences across providers?
Use abstraction layers like LangChain, LiteLLM, or Vercel AI SDK that normalize API differences across providers. This lets you switch between models without rewriting integration code. Define capability requirements in config, and the library handles provider-specific implementations.
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