GPT-5.2

What is GPT-5.2?

GPT-5.2 is a reasoning-focused language model from OpenAI for complex professional and technical work. It is designed to handle tasks that require more than a short answer, including long-document analysis, software development, spreadsheet work, visual interpretation, planning, and multi-step workflows that use external tools.

In OpenAI’s current catalog, GPT-5.2 is available through the API under the canonical model identifier gpt-5.2. OpenAI describes it as a previous flagship model because newer generations are available, but the model remains accessible through the API according to the supplied research. OpenAI also lists the dated snapshot gpt-5.2-2025-12-11, which can be useful when an application needs a more stable model reference.

The model is intended for developers and organizations that need a broad general-purpose system with substantial context capacity and deliberate reasoning. It is not a specialized image, speech, video, embedding, or moderation model.

GPT-5.2 specifications at a glance

The context window is the amount of text and other supported input the model can consider in one interaction. At 400,000 tokens, GPT-5.2 can work with very large collections of documents or extended conversations, subject to the limits and behavior of the specific API endpoint and application.

Input and output modalities

GPT-5.2 accepts text and images and returns text. Image input allows it to interpret material such as screenshots, diagrams, charts, scanned documents, and other visual content alongside written instructions. The model does not natively produce images, audio, video, speech, music, or embeddings.

This distinction matters when selecting a model. GPT-5.2 can describe or analyze a chart, but it is not an image-generation model. It can process a screenshot of an interface or document, but it does not return a spoken response or a generated video. Applications requiring those outputs need a separate service or model designed for that modality.

The model supports structured outputs, which can help an application request responses that follow a defined schema. The supplied research does not independently verify a separate legacy JSON-mode capability, so structured outputs and legacy JSON mode should not be treated as the same feature without checking the relevant API documentation.

Reasoning, coding, and performance

GPT-5.2 is built for tasks where the model must connect multiple pieces of information, make a plan, inspect intermediate details, or follow a longer chain of instructions. Its configurable reasoning effort gives developers a way to trade response depth against latency and token consumption. The available settings range from none through xhigh.

Lower reasoning settings can be appropriate for straightforward transformations, classification, or routine text generation where fast responses matter. Higher settings are better suited to difficult analysis, planning, mathematical or scientific work, complex code review, and workflows where the additional reasoning is worth the extra time and cost. The supplied research does not provide benchmark scores, so performance claims here describe the model’s documented positioning rather than an independent ranking.

For software work, GPT-5.2 can generate and explain code, review existing code, reason about implementation choices, and participate in tool-using development workflows. Its combination of a large context window, reasoning controls, structured outputs, and tool calling is particularly relevant when a task involves a sizeable codebase, lengthy technical requirements, or several stages of investigation.

The model is also suitable for spreadsheet and document workflows. For example, an application could provide a lengthy report and supporting tables, ask for a structured risk summary, and then use a tool call to retrieve additional information or perform a follow-up operation. These are practical use cases for the model’s long context and agentic workflow support, not evidence of a guaranteed result on every document or dataset.

Tool calling and agentic workflows

GPT-5.2 supports function and tool calling. In simple terms, this allows an application to give the model access to defined operations, such as querying a database, looking up an internal record, running a calculation, or invoking another software service. The model can decide when a tool is relevant and produce arguments in the format expected by the application.

OpenAI supports GPT-5.2 through both the Responses API and the Chat Completions API. OpenAI recommends the Responses API for new GPT-5.2 integrations because it can preserve reasoning context across turns and improve token efficiency, caching, and latency in multi-turn workflows. The model also supports allowed-tool controls, custom tools, reasoning summaries, streaming responses, and context-management features such as compaction according to the supplied research.

Web search can be used through supported OpenAI web-search tool integrations. That gives an application a way to retrieve current information, but it does not change GPT-5.2’s underlying knowledge cutoff, which is August 31, 2025. Retrieved information should still be checked for accuracy, relevance, and source quality.

GPT-5.2 pricing and availability

GPT-5.2 API pricing is listed at $1.75 per million input tokens and $14.00 per million output tokens. Cached input tokens cost $0.175 per million tokens. Batch API processing may provide additional savings compared with standard processing.

Input and output tokens are not equally priced: generated output costs substantially more than input, so applications that request very long responses or use high reasoning settings may incur higher costs. Caching can reduce the cost of repeatedly sending unchanged context, while batch processing may be preferable for workloads that do not require immediate responses.

As of September 23, 2026, the supplied research describes GPT-5.2 as still accessible through the OpenAI API, while positioning it as a previous flagship model. Availability, pricing, rate limits, and model access can change, so developers should confirm current details in OpenAI’s model and pricing documentation before deployment.

Best use cases for GPT-5.2

GPT-5.2 is a strong fit when a task combines substantial context, deliberate reasoning, and the need to produce a useful textual result. Appropriate use cases include:

• Complex professional knowledge work: Analyze policies, reports, contracts, technical documents, or research material and produce a structured explanation.
• Long-document synthesis: Compare multiple documents, identify inconsistencies, extract requirements, or prepare a concise briefing from a large source set.
• Software engineering: Generate code, review implementation details, explain errors, plan changes, or reason across a larger code context.
• Spreadsheet and data workflows: Interpret tables, explain trends, create structured summaries, and coordinate calculations or other tools.
• Visual question answering: Inspect charts, screenshots, diagrams, and scanned documents together with written instructions.
• Research and planning: Break down multi-step questions, use supported tools, and produce a reasoned plan or decision-support document.
• Agentic applications: Coordinate several tool calls while maintaining context across a longer workflow.

Limitations and trade-offs

GPT-5.2’s main advantage is depth and context capacity, but those features can also make it less suitable for simple, latency-sensitive, or highly cost-constrained workloads. A smaller or cost-optimized model may be a better choice when the task is repetitive, short, and easy to verify. GPT-5.2’s documented pricing also makes output-heavy workloads more expensive than workloads that generate only brief responses.

Reasoning does not guarantee correctness. The model can produce inaccurate or overconfident answers, particularly when instructions are ambiguous, source material is incomplete, or the task requires information beyond its knowledge cutoff. Important legal, financial, medical, operational, and security decisions require appropriate human review and verification.

The model’s modality limitations are also significant. It accepts images but does not generate images, audio, video, speech, music, or embeddings. It is therefore not the right standalone choice for media-generation pipelines, speech interfaces, vector search, or content moderation. Fine-tuning is not supported according to the supplied documentation, so applications needing a customized trained model may need another approach.

Finally, GPT-5.2 is described as a previous flagship model. Newer OpenAI models may be more appropriate for new deployments if they offer better speed, lower cost, or capabilities that match the workload more closely. The relevant comparison is not simply whether GPT-5.2 can perform a task, but whether its context capacity and reasoning depth justify its latency and token costs.

When to choose GPT-5.2

Choose GPT-5.2 when the work benefits from a large context window, careful multi-step reasoning, image understanding, and tool-enabled workflows in one text-producing model. It is especially reasonable for professional analysis, complex coding, long documents, spreadsheet interpretation, visual question answering, and agents that need to preserve context over several steps.

Choose a different option when the priority is the lowest possible cost, minimum latency, native media generation, speech output, embeddings, moderation, or fine-tuning. A newer model may also be preferable for a new application if it delivers a better capability-to-cost ratio or has replaced GPT-5.2 for the target workload. Within the OpenAI lineup, GPT-5.2 should therefore be evaluated as a capable previous flagship rather than automatically treated as the default model for every new project.

For a practical evaluation, test representative prompts using the intended reasoning setting, context size, tool pattern, and output length. Measure not only answer quality, but also latency, input and output token usage, tool-call reliability, and the amount of human correction required. That process will show whether GPT-5.2’s deeper reasoning and large context justify its cost for the specific application.