GPT-5.2 (xhigh)

The single biggest driver of cost is the model's high verbosity combined with its expensive output price. Actively manage this by engineering your prompts to request concise answers.

• Use instructions like: "Be brief," "Answer in three sentences," or "Use bullet points."
• For data extraction, specify the exact output format, such as JSON, with a predefined schema. This prevents the model from adding conversational filler.
• Requesting a summary or key takeaways instead of a full explanation can drastically cut token counts.

Don't use a sledgehammer to crack a nut. A model cascade involves using cheaper, faster models for simpler tasks and only escalating to GPT-5.2 (xhigh) when necessary.

• Step 1 (Cheap Model): Use a smaller, more affordable model to handle initial user queries, classify intent, or perform basic data extraction.
• Step 2 (Escalation): If the initial model determines the task requires advanced reasoning, multi-modal understanding, or a large context, it then routes the query to GPT-5.2 (xhigh).
• This tiered approach reserves the expensive model for the high-value problems it's designed to solve.

While the 400k context window is powerful, filling it unnecessarily is a waste of money. Pre-process your input to be as efficient as possible.

• Instead of feeding a whole book, use an embedding-based search to find the most relevant chapters or paragraphs first, then submit only that selection to the model.
• Summarize long conversation histories before including them in the prompt, retaining key information while discarding conversational turns.
• Clean your data by removing boilerplate text, HTML tags, or irrelevant sections from documents before processing.

Many applications receive repetitive queries. Re-generating the same answer is an avoidable cost. Implement a caching layer to store and retrieve previous responses.

• Use a key-value store like Redis to cache responses, using a hash of the prompt (or a semantic equivalent) as the key.
• Set a reasonable time-to-live (TTL) for cached entries to ensure information stays fresh, especially if the underlying data changes.
• This is particularly effective for FAQ bots, documentation search, and other information retrieval tasks.

GPT-5.2 (xhigh) is a state-of-the-art, proprietary large language model developed by OpenAI. It is characterized by its exceptionally high performance on reasoning benchmarks, its ability to process both text and images (multi-modality), and its very large 400,000-token context window.

Multi-modality means the model can understand and generate information across different formats, or modes. For GPT-5.2 (xhigh), this specifically refers to its ability to process text and images as input and generate text and images as output. For example, you could upload a chart and ask for a textual analysis, or provide a text description and ask it to create an image.

A 400,000-token context window is massive. It's roughly equivalent to 300,000 words, or a 600-page book. This allows the model to ingest and reason about extremely long documents, such as financial reports, legal contracts, or entire software codebases, within a single prompt without losing context.

The premium price of GPT-5.2 (xhigh) is a reflection of the immense computational power (and therefore cost) required to train and run a model of its size and capability. The pricing strategy targets high-value enterprise and professional use cases where the model's advanced intelligence can provide a significant return on investment that justifies the higher operational expense.

It is one of the best. Ranking #2 on our Intelligence Index, it is objectively one of the most capable models for complex tasks. However, the "best" model always depends on the specific use case. For simpler tasks like summarization or classification, a cheaper, faster model is often a more efficient and cost-effective choice. GPT-5.2 (xhigh) is "best" for problems that other models cannot solve.

It depends on your priorities. Our benchmarks show that Microsoft Azure offers significantly better performance (higher speed, lower latency) for the same price. However, OpenAI's API is often simpler and faster to get started with for individual developers and smaller teams. For large enterprises, Azure's integration with its cloud ecosystem, security, and compliance features often make it the preferred choice.