GPT-6 Sol is OpenAI's reasoning model for complex coding and agentic workflows. It combines a 1,050,000-token context window with up to 128,000 output tokens, image understanding, structured outputs, function calling, and broad tool support through the Responses API. Configurable reasoning from none through max makes it suited to software engineering, multi-step automation, technical analysis, and long-running agents that must plan, execute, and verify their work.

GPT-6 Luna is OpenAI's efficient reasoning model for focused, high-volume workloads. It supports a 1,050,000-token context window, up to 128,000 output tokens, image understanding, structured outputs, function calling, and broad tool use through the Responses API. Configurable reasoning from none through max makes it a strong fit for classification, extraction, summarization, routing, workflow automation, and lightweight agent tasks.

GPT-Image-2.5 Sunburst is OpenAI's most capable model for image generation and editing, designed for premium workflows that need tighter control across detailed and iterative edits. It combines strong reference-subject preservation, localized changes, natural lighting and textures, complex layout handling, and consistent visual direction for production-ready campaign creative, polished product imagery, and other demanding visual design work.

GPT-Image-2.5 Flare is OpenAI's fastest model for high-quality image generation and editing, delivering higher-quality images than GPT Image 2 at 50% lower latency. It improves natural lighting, texture detail, reference-subject preservation, localized editing, and consistency across iterative edits, while handling complex layouts and transparent backgrounds. It is designed for creator content, product experiences, visual search, rapid prototyping, and high-volume generation.

GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 family for complex professional work. It combines the strongest reasoning tier in this family with a 1,050,000 token context window, up to 128,000 output tokens, image input, structured outputs, function calling, and broad tool use for demanding coding, research, analysis, and agent workflows.

GPT-5.6 Terra is the balanced model in OpenAI's GPT-5.6 family, designed for production workloads that need strong reasoning without the cost of the flagship tier. It supports a 1,050,000 token context window, up to 128,000 output tokens, image input, structured outputs, function calling, and broad tool use, making it well suited to coding, analysis, extraction, workflow automation, and general-purpose professional assistants.

GPT-5.6 Luna is the most cost-efficient model in OpenAI's GPT-5.6 family, aimed at high-volume and cost-sensitive workloads. It supports a 1,050,000 token context window, up to 128,000 output tokens, image input, structured outputs, function calling, and broad tool use for classification, extraction, summarization, routing, tagging, and lightweight agent tasks.

GPT-5.5 is OpenAI's newest frontier model for complex professional work, with strong performance in coding, reasoning, and tool-using workflows. It supports a 1,050,000 token context window, 128,000 max output tokens, configurable reasoning effort, image input, and a broad tool stack including web search, file search, code interpreter, hosted shell, apply patch, skills, MCP, tool search, and computer use.

GPT Image 2 is a general-purpose GPT Image family model for text-to-image generation and image editing. Its strengths include strong prompt adherence, readable embedded text, detailed edits, photorealistic rendering, and structured visual outputs such as posters, packaging, product comps, diagrams, and other layout-sensitive images.

GPT-5.4 Nano is the smallest and fastest variant of GPT-5.4, designed for high-throughput, low-latency tasks such as classification, data extraction, ranking, and lightweight automation. It prioritizes speed and cost efficiency for simple, high-volume workloads and is available exclusively via the API.

GPT-5.4 Mini is a compact, efficient variant of GPT-5.4 designed for coding assistants, subagent orchestration, and multimodal applications requiring faster responsiveness. It supports a 400K token context window and retains native computer use and configurable reasoning effort at a lower cost than the flagship model.

GPT-5.4 Pro is the high-performance variant of GPT-5.4, optimized for enterprise-grade professional tasks. It offers deeper reasoning, enhanced accuracy, and extended compute for complex multi-step workflows including document creation, spreadsheet analysis, and autonomous agent orchestration. It shares the 1 million token context window and native computer use capabilities of the standard GPT-5.4.

GPT-5.4 is OpenAI's flagship large language model, featuring a 1 million token context window, native computer use, and a 33% reduction in factual errors over GPT-5.2. It integrates coding capabilities from GPT-5.3-Codex, is 47% more token-efficient, and supports configurable reasoning effort for complex professional tasks.

GPT Image 1.5 is OpenAI’s newest flagship image model powering the latest ChatGPT Images. It delivers significantly faster image generation with stronger instruction following, more precise edits that preserve original details, more believable transformations, and improved rendering of dense or small text. It is suited for practical creative workflows, detailed design tasks, and production use cases.

Sora 2 Pro is the higher quality Sora 2 variant for precision video work. It supports text prompts and image inputs. It outputs synchronized video with sound, higher resolution frames, and stronger temporal consistency. Ideal for production clips and demanding pipelines.

Sora 2 is OpenAI’s flagship generative model for video and audio. It accepts text prompts and generates visually rich clips with synchronized dialogue and sound. It improves physical realism and scene control. It also supports editing and extension of existing video inputs.

GPT-5 Nano is OpenAI's fastest and cheapest GPT-5 variant, built for speed- and cost-sensitive workloads such as summarization, classification, extraction, and lightweight automation. It supports 400K context, 128K max output tokens, image input, reasoning token support, structured outputs, and function calling in a compact package optimized for high-throughput API use.

GPT-5 Mini is a faster, more cost-efficient variant of GPT-5 designed for well-defined tasks, precise prompts, and high-volume production use. OpenAI positions it for cost-sensitive, low-latency workloads while retaining strong reasoning ability, 400K context, 128K max output tokens, image input support, and compatibility with structured outputs and function calling.

gpt-oss-120b is OpenAI's flagship open-weight language model, designed for strong reasoning, coding, and agent-style text workflows outside OpenAI-hosted infrastructure. It uses a sparse mixture-of-experts architecture with 117B total parameters, 5.1B active parameters per token, a native 128K context window, adjustable reasoning effort, support for structured outputs, and strong performance in tool use, function calling, and local or self-hosted deployment setups.

GPT Image 1 Mini is a lighter variant of OpenAI's GPT Image 1 model. It offers faster generation at a lower cost while retaining core capabilities including text-to-image generation, image editing, and text rendering. It is suited for high-volume workflows, rapid prototyping, and cost-sensitive applications where the full GPT Image 1 model may be excessive.

GPT Image 1 is OpenAI’s native GPT 4o image model. It creates detailed visuals from text prompts. It supports diverse styles and precise layouts. It can edit existing images with masks. It renders readable text in scenes. It suits design tools and production workflows.

OpenAI CLIP ViT-L/14 is a contrastive vision-language model that embeds images and text into a shared representation space. It enables tasks like zero-shot image classification, semantic search, and similarity scoring by computing aligned feature vectors for images and texts.

gpt-oss-safeguard-20b is OpenAI’s open-weight safety classifier. Instead of a fixed taxonomy, it takes the policy in the prompt and labels each item against it, so the same model can serve teams whose rules differ. It reasons before answering, which leaves a trace of why an item was labelled the way it was, and its 8K context window holds a policy alongside the content under review. It fits moderation pipelines and review queues where the rules change per product or per customer.