Azure Provider

GPT Image 2 — OpenAI's next-generation image generation model. It delivers rapid, high-quality image generation and editing capabilities, with support for flexible image dimensions and high-fidelity image inputs for seamless creative workflows.

OpenAI's GPT Image 1.5 is the latest evolution of its AI image generation, offering superior instruction following, photorealism, text rendering, and editing control, making it ideal for detailed creative and production work with faster speeds and lower costs than predecessors like DALL-E 3. It excels at complex requests, maintaining character/style consistency, rendering crisp text in visuals, and understanding nuanced prompts through built-in reasoning, integrated into ChatGPT and available via API.

GPT Image 2.5 Sunburst is an image generation and editing model from OpenAI, positioned as the precision-oriented tier of the GPT Image 2.5 series. It is suited to detailed creative work where editing accuracy matters more than generation speed, via the dedicated Images API.

GPT Image 2.5 Flare is an image generation and editing model from OpenAI, positioned as the speed-oriented tier of the GPT Image 2.5 series. It is suited to high-volume everyday generation, creator content, and rapid prototyping via the dedicated Images API.

text-embedding-3-small is OpenAI's improved, more performant version of the ada embedding model. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces of text. Embeddings are useful for search, clustering, recommendations, anomaly detection, and classification tasks.

Mistral Large 3, is a state-of-the-art, open-weight, general-purpose multimodal model with a granular Mixture-of-Experts architecture. It features 41B active parameters and 675B total parameters.

GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard instruction evals, 35.8% on MultiChallenge, and 84.1% on IFEval. Mini also shows strong coding ability (e.g., 31.6% on Aider’s polyglot diff benchmark) and vision understanding, making it suitable for interactive applications with tight performance constraints.

GPT-4o mini is OpenAI's newest model after GPT-4 Omni, supporting both text and image inputs with text outputs.

As their most advanced small model, it is many multiples more affordable than other recent frontier models, and more than 60% cheaper than GPT-3.5 Turbo. It maintains SOTA intelligence, while being significantly more cost-effective.

GPT-4o mini achieves an 82% score on MMLU and presently ranks higher than GPT-4 on chat preferences common leaderboards.

GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and GPT-4.5 across coding (54.6% SWE-bench Verified), instruction compliance (87.4% IFEval), and multimodal understanding benchmarks. It is tuned for precise code diffs, agent reliability, and high recall in large document contexts, making it ideal for agents, IDE tooling, and enterprise knowledge retrieval.

GPT-4o ("o" for "omni") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of GPT-4 Turbowhile being twice as fast and 50% more cost-effective. GPT-4o also offers improved performance in processing non-English languages and enhanced visual capabilities.

For benchmarking against other models, it was briefly called "im-also-a-good-gpt2-chatbot"

OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains.

Despite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. Thanks to its efficient architecture and refined reinforcement learning training, o4-mini can chain tools, generate structured outputs, and solve multi-step tasks with minimal delay—often in under a minute.

For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million token context window, and scores 80.1% on MMLU, 50.3% on GPQA, and 9.8% on Aider polyglot coding – even higher than GPT‑4o mini. It’s ideal for tasks like classification or autocompletion.

Grok 4.7 is xAI’s most capable model for coding and knowledge work, designed to spend longer on difficult tasks and verify its work more carefully. It is optimized for complex agentic workflows, long-context reasoning, and high-reliability outputs in demanding professional use cases.

GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex

GPT-5.1-Codex is a specialized version of GPT-5.1 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks. The model supports building projects from scratch, feature development, debugging, large-scale refactoring, and code review. Compared to GPT-5.1, Codex is more steerable, adheres closely to developer instructions, and produces cleaner, higher-quality code outputs.

Codex integrates into developer environments including the CLI, IDE extensions, GitHub, and cloud tasks. It adapts reasoning effort dynamically—providing fast responses for small tasks while sustaining extended multi-hour runs for large projects. The model is trained to perform structured code reviews, catching critical flaws by reasoning over dependencies and validating behavior against tests. It also supports multimodal inputs such as images or screenshots for UI development and integrates tool use for search, dependency installation, and environment setup. Codex is intended specifically for agentic coding applications.

GPT-5-Codex is a specialized version of GPT-5 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks. The model supports building projects from scratch, feature development, debugging, large-scale refactoring, and code review. Compared to GPT-5, Codex is more steerable, adheres closely to developer instructions, and produces cleaner, higher-quality code outputs. Reasoning effort can be adjusted with the reasoning.effort parameter. Read the docs here

Codex integrates into developer environments including the CLI, IDE extensions, GitHub, and cloud tasks. It adapts reasoning effort dynamically—providing fast responses for small tasks while sustaining extended multi-hour runs for large projects. The model is trained to perform structured code reviews, catching critical flaws by reasoning over dependencies and validating behavior against tests. It also supports multimodal inputs such as images or screenshots for UI development and integrates tool use for search, dependency installation, and environment setup. Codex is intended specifically for agentic coding applications.

GPT-5.2-Codex is an upgraded version of GPT-5.2 optimized for agentic coding tasks in Codex or similar environments. GPT-5.2-Codex supports low, medium, high, and xhigh reasoning effort settings.

GPT-5 Pro is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy in high-stakes use cases. It supports test-time routing features and advanced prompt understanding, including user-specified intent like "think hard about this." Improvements include reductions in hallucination, sycophancy, and better performance in coding, writing, and health-related tasks.

GPT-6.1 Sol is a production-grade OpenAI model designed for agentic workloads, optimized for long-horizon and multi-step tasks such as continuous tool use, complex coding, computer use, and professional workflows. It delivers capabilities approaching GPT-6 Astra at significantly lower cost, including especially low cached-input pricing that makes it well suited for reusable-context agent systems and enterprise deployment scenarios.

GPT-5.4 Pro is OpenAI's most advanced model, building on GPT-5.4's unified architecture with enhanced reasoning capabilities for complex, high-stakes tasks. It features a 1M+ token context window (922K input, 128K output) with support for text and image inputs. Optimized for step-by-step reasoning, instruction following, and accuracy, GPT-5.4 Pro excels at agentic coding, long-context workflows, and multi-step problem solving.

chat-latest points to the latest Instant model currently used in ChatGPT. We recommend leveraging our latest-model for most API usage, but feel free to use this model to test our latest improvements for chat use cases. The underlying model snapshot will be regularly updated.

GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy in high-stakes use cases. It supports test-time routing features and advanced prompt understanding, including user-specified intent like "think hard about this." Improvements include reductions in hallucination, sycophancy, and better performance in coding, writing, and health-related tasks.

GPT-5-Nano is the smallest and fastest variant in the GPT-5 system, optimized for developer tools, rapid interactions, and ultra-low latency environments. While limited in reasoning depth compared to its larger counterparts, it retains key instruction-following and safety features. It is the successor to GPT-4.1-nano and offers a lightweight option for cost-sensitive or real-time applications.

GPT-5.6 Sol is the frontier model in the GPT-5.6 family. It roughly corresponds to the unsuffixed model tier used in earlier GPT-5 families. The gpt-5.6 alias routes requests to GPT-5.6 Sol.

GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost. GPT-5 Mini is the successor to OpenAI's o4-mini model.

GPT-5.1 is the latest frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. It uses adaptive reasoning to allocate computation dynamically, responding quickly to simple queries while spending more depth on complex tasks. The model produces clearer, more grounded explanations with reduced jargon, making it easier to follow even on technical or multi-step problems.

Built for broad task coverage, GPT-5.1 delivers consistent gains across math, coding, and structured analysis workloads, with more coherent long-form answers and improved tool-use reliability. It also features refined conversational alignment, enabling warmer, more intuitive responses without compromising precision. GPT-5.1 serves as the primary full-capability successor to GPT-5

GPT-5.4 nano is designed for tasks where speed and cost matter most like classification, data extraction, ranking, and sub-agents.

GPT-6 Astra is OpenAI’s most capable model for complex reasoning, coding, computer use, research, and document creation, with strong performance on multistep workflows across code, browsers, and professional software. It supports reasoning.effort levels of low, medium, high, xhigh, and max, and achieves strong results while often using fewer output tokens for lower estimated cost per task than earlier models. It is also OpenAI’s most aligned model yet, designed to follow intent carefully, respect task boundaries, adapt to changing requirements, and communicate transparently.

GPT‑5.3‑Codex is OpenAI’s most capable agentic coding model, designed to handle not just code generation but longer, end‑to‑end work on a computer. It combines the frontier coding strength of GPT‑5.2‑Codex with the reasoning and professional knowledge capabilities of GPT‑5.2, and is engineered to run faster (about 25% faster in Codex). It’s built for long‑running, multi‑step tasks that can include research, tool use, debugging, and complex execution, while staying interactive—so you can steer it, ask questions, and refine direction as it works without losing context. In evaluations, it sets new highs on software engineering and agentic benchmarks (including SWE‑Bench Pro and Terminal‑Bench 2.0) and shows strong results on OSWorld‑Verified and GDPval. It’s also deployed with strengthened cybersecurity safeguards and is trained to help identify software vulnerabilities.

GPT‑5.4, released March 5, 2026, is OpenAI’s newest frontier model aimed at complex professional work. It’s available in the API as gpt-5.4 and in ChatGPT as GPT‑5.4 Thinking, with a higher-end GPT‑5.4 Pro tier for maximum performance. It supports adjustable deliberation through reasoning.effort, letting developers trade latency/cost for deeper reasoning. The model accepts text and image inputs and produces text output, with a 1,050,000‑token context window and up to 128,000 output tokens; its published knowledge cutoff is Aug 31, 2025. OpenAI also published a system card describing new cybersecurity-focused mitigations for the Thinking variant.

GPT-5.4 mini brings the strengths of GPT-5.4 to a faster, more efficient model designed for high-volume workloads.

GPT-5.2 is the newest frontier-grade model in the GPT-5 family, outperforming GPT-5.1 in agentic capabilities and long-context handling. It uses adaptive reasoning to dynamically allocate compute—responding quickly to straightforward questions while applying deeper analysis to more complex tasks. Designed for broad task coverage, GPT-5.2 delivers consistent improvements across math, coding, science, and tool-calling workloads, producing more coherent long-form responses and more reliable tool use.

GPT‑5.5 understands what you’re trying to do faster and can carry more of the work itself. It excels at writing and debugging code, researching online, analyzing data, creating documents and spreadsheets, operating software, and moving across tools until a task is finished. Instead of carefully managing every step, you can give GPT‑5.5 a messy, multi-part task and trust it to plan, use tools, check its work, navigate through ambiguity, and keep going.

GPT-6 Sol is the cost-efficient high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra and above the fast GPT-6 Luna tier. It is suited for demanding professional work, agentic coding, business workflow automation, and computer use, and is particularly strong at long-horizon software engineering tasks in real codebases. It approaches Astra-level factual reliability at a much lower cost and shares Astra's clearer, more concise communication style in technical and coding conversations.

GPT-5.6 Terra is a high-capability GPT-5.6 series model designed for long-context, tool-enabled workflows across writing, coding, analysis, and multi-step problem solving. It supports text, image, and file inputs with text output, and is optimized for strong instruction following, reliable reasoning, and precise execution on complex tasks.

GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for its price tier.

GPT-6 Luna is the fast, cost-efficient model in OpenAI's GPT-6 series, positioned below GPT-6 Sol. It is suited for high-volume and latency-sensitive workloads such as chat, classification, and lightweight agentic tasks, and at higher reasoning effort it can take on complex software engineering and computer-use tasks that previously called for a Sol-tier model. It shares the GPT-6 family's gains in factual reliability and its clearer, more concise communication style.