Models

	Provider	Model	Context	Capabilities
	OpenAI	GPT-5 Mini	400k	👁️🔧
	OpenAI	GPT-5 Nano	400k	👁️🔧
	OpenAI	GPT-4.1 Nano	1M	👁️🔧
	Anthropic	Claude 3.5 Haiku	200k	👁️🔧
	xAI	Grok 4 Fast	2M	👁️🔧
	xAI	Grok 3 Mini	131k	🔧
	Google	Gemini 2.5 Flash Preview	1M	👁️🔧
	Google	Gemini 2.0 Flash Lite	1M	👁️🔧
	Google	Gemma 3 27B	131k	👁️
	Meta	Llama 3.3 70B Instruct	131k	🔧
	Meta	Llama 3.3 8B Instruct	128k	🔧
	Mistral	Mistral Small 3.2 24B	131k	👁️🔧
	DeepSeek	DeepSeek R1	164k	🔧
	Qwen	Qwen3 235B A22B	41k	🔧
	NVIDIA	Nemotron Nano 12B 2 VL	128k	👁️🔧

And more…

​

Vision

Vision capabilities allow AI models to understand and analyze images. Models with vision support (indicated by 👁️ in the capabilities column) can:

• Analyze and describe images
• Extract text from images (OCR)
• Answer questions about visual content
• Understand charts, graphs, and diagrams
• Process screenshots and documents

Vision-enabled models are ideal for tasks like document analysis, image-based Q&A, and visual content understanding.

​

Tools

Tools (also known as function calling) enable AI models to interact with external systems and perform actions beyond text generation. Models with tool support (indicated by 🔧 in the capabilities column) can:

• Call external APIs and services
• Execute code and calculations
• Access real-time data
• Perform structured data operations
• Integrate with third-party applications

Tool-enabled models are essential for building AI agents, automation workflows, and applications that require dynamic interactions with external systems.

​

Web Search

Web search capabilities allow AI models to access real-time information from the internet, providing up-to-date answers and current data beyond their training knowledge. Key features:

• Real-time information: Access the latest news, events, and data
• Current facts: Get information that may have changed since the model’s training
• Comprehensive results: Search across multiple sources for accurate answers
• Citation support: Models can provide sources for their information

Web search is particularly useful for:

• Current events and news
• Real-time data (stock prices, weather, etc.)
• Recent developments in technology, science, and other fields
• Verifying information that may have changed

When enabled, the model will automatically search the web when it needs current information to answer your questions accurately.

​

Context Window

The context window represents the maximum number of tokens (words or word pieces) a model can process in a single conversation. This includes both your input and the model’s output. Why it matters:

• Larger context windows (1M, 2M) allow you to work with longer documents, maintain longer conversations, and process more information at once
• Smaller context windows (128k, 200k) are more cost-effective and faster for shorter tasks

Choose a model with an appropriate context window based on your use case:

• Short tasks: 128k-200k is sufficient
• Document analysis: 400k-1M recommended
• Long conversations or large documents: 1M+ preferred

​

Model Parameters

Model parameters refer to the size and complexity of the neural network. While not always directly visible, parameter count affects:

• Model capability: Larger models generally have better reasoning and knowledge
• Response quality: More parameters often mean more nuanced and accurate responses
• Speed: Smaller models respond faster
• Cost: Larger models typically cost more per token

Common parameter sizes:

• Nano/Mini (8B-24B): Fast, cost-effective, good for simple tasks
• Small/Medium (24B-70B): Balanced performance and speed
• Large (70B+): Best quality, slower, higher cost

The parameter count is often reflected in the model name (e.g., “Llama 3.3 70B” has 70 billion parameters).