Text-embedding-3 series models are the latest and most capable embedding model from OpenAI.

Text-embedding-3 series models are the latest and most capable embedding model from OpenAI.

Focused on advanced reasoning and solving complex problems, including math and science tasks. Ideal for applications that require deep contextual understanding and agentic workflows.

Smaller, faster, and 80% cheaper than o1-preview, performs well at code generation and small context operations.

Excels in image reasoning capabilities on high-res images for visual understanding apps.

gpt-4.1-mini outperform gpt-4o-mini across the board, with major gains in coding, instruction following, and long-context handling

gpt-4.1-nano provides gains in coding, instruction following, and long-context handling along with lower latency and cost

OpenAI's most advanced multimodal model in the gpt-4o family. Can handle both text and image inputs.

An affordable, efficient AI solution for diverse text and image tasks.

gpt-5 is designed for logic-heavy and multi-step tasks.

gpt-5-chat (preview) is an advanced, natural, multimodal, and context-aware conversations for enterprise applications.

o3 includes significant improvements on quality and safety while supporting the existing features of o1 and delivering comparable or better performance.

gpt-5-nano is optimized for speed, ideal for applications requiring low latency.

gpt-5-mini is a lightweight version for cost-sensitive applications.

Focused on advanced reasoning and solving complex problems, including math and science tasks. Ideal for applications that require deep contextual understanding and agentic workflows.

o3-mini includes the o1 features with significant cost-efficiencies for scenarios requiring high performance.

o4-mini includes significant improvements on quality and safety while supporting the existing features of o3-mini and delivering comparable or better performance.

A 398B parameters (94B active) multilingual model, offering a 256K long context window, function calling, structured output, and grounded generation.

Command A is a highly efficient generative model that excels at agentic and multilingual use cases.

Command R is a scalable generative model targeting RAG and Tool Use to enable production-scale AI for enterprise.