OpenAI

OpenAI's most advanced multimodal model, capable of processing and generating text, images, and potentially other data types in real-time. It features a 128,000-token context window, delivering improved reasoning, reduced latency, and enhanced instruction-following compared to previous models. GPT-4o achieves state-of-the-art performance across benchmarks like MMLU and excels in applications requiring real-time interaction, such as conversational agents, creative writing, and multimodal analysis.

A compact, cost-efficient variant of GPT-4o, retaining 70% of its multimodal performance with a 128,000-token context window. It supports text generation, image understanding, and code generation at a fraction of the cost, making it ideal for budget-conscious applications like lightweight chatbots, content generation, and educational tools. GPT-4o Mini balances performance and affordability while maintaining strong reasoning capabilities.

A highly efficient, affordable model designed for everyday tasks, featuring a 32,000-token context window. O3 Mini excels in rapid text and code generation with basic reasoning capabilities, making it perfect for high-volume applications such as customer support chatbots, automated responses, and simple scripting tasks. Its low cost and fast processing speed ensure scalability for routine operations.

OpenAI's powerful reasoning-focused model with exceptional understanding of complex tasks and nuanced instructions. With a massive context window of 1,000,000 tokens, it offers unprecedented depth in analysis and generation capabilities. O1 excels at multi-step reasoning, complex problem-solving, and handling extended conversations with retention of subtle details.

A preview release of OpenAI's next-generation model, offering enhanced reasoning, instruction following, and knowledge compared to GPT-4o. With a 128,000-token context window, it brings improved consistency in outputs and better handling of complex, multi-step tasks. This model represents an interim advancement toward future capabilities while maintaining API compatibility with existing applications.

A cost-effective embedding model designed for efficient text representation and semantic search applications. It converts text into numerical vectors that capture semantic meaning, enabling similarity comparisons, clustering, and classification. This compact model balances performance and efficiency for production-level semantic search, recommendation systems, and content organization.