GPT-5 (high) (high)

The single most effective cost-control measure is to manage the model's natural verbosity. Since output tokens are 8x more expensive than input tokens, reducing output length provides direct and substantial savings. Use specific instructions in your prompts to guide the model toward conciseness.

• Add phrases like "Be concise," "Answer in one sentence," or "Provide a bulleted list of key points only."
• For structured data, ask for the output in a specific format like JSON with predefined fields to prevent extraneous conversational text.
• Experiment with temperature settings; lower temperatures often lead to more focused and less creative (and less verbose) responses.

Design your application's logic to minimize expensive output tokens and maximize cheaper input tokens. This involves reframing problems to be less generative and more analytical.

• Instead of asking an open-ended question like "Explain this document," provide the document and ask a series of targeted questions that elicit short answers, like "Does this document mention Topic X?" or "Extract the names of all people listed."
• For classification or routing tasks, instruct the model to return only a single category name or number instead of a full sentence explaining its choice.

Many applications receive repetitive queries. Caching the high-quality responses from GPT-5 (high) can dramatically reduce API calls, saving money and reducing latency for users.

• Implement a semantic caching layer that can identify when a new query is functionally identical to a previously answered one.
• Cache responses for common questions, product descriptions, or explanations. The initial cost to generate the canonical answer is quickly offset by the savings from not having to regenerate it.

Don't treat all API providers as equal. The performance differences are significant and have a direct impact on user experience and infrastructure choices.

• For real-time chat, prioritize low latency by choosing a provider like Microsoft Azure, even if it means a slightly different deployment process.
• For offline batch processing, prioritize maximum throughput to get jobs done faster, again pointing towards Azure's throughput-optimized endpoints.
• Only use a slower endpoint like the default OpenAI API if performance is not a primary concern for your application.

GPT-5 (high) is a state-of-the-art, proprietary large language model developed by OpenAI. It is characterized by its top-tier performance on intelligence benchmarks, a very large 400,000-token context window, and multimodal capabilities (text and image input). It is designed for complex reasoning tasks but is also notable for its high verbosity.

GPT-5 (high) represents a significant generational leap. Its Intelligence Index score of 68 indicates a major improvement in reasoning, problem-solving, and instruction-following capabilities over the GPT-4 family. It also features a much larger context window (400k vs. 128k for GPT-4 Turbo) and demonstrates higher throughput on optimized infrastructure, making it both smarter and faster for certain workloads.

High verbosity means the model has a strong tendency to provide longer, more detailed, and comprehensive answers than other models, even when not explicitly asked for them. While this can be beneficial for depth and explanation, it has two main drawbacks: it directly increases costs due to a higher number of output tokens, and it can sometimes overwhelm users with more information than they need.

The 400k context window is a powerful, specialized feature, not a tool for everyday use. It is most valuable for tasks that require the model to hold and reason over vast amounts of information at once, such as analyzing an entire book, a complex legal case file, or a large software repository. For most common tasks like simple chat or Q&A, this window is overkill, and the cost to fill it with tokens is prohibitive. It should be used strategically for specific, high-value use cases.

This pricing model is common for LLMs and reflects the underlying computational costs. Processing existing text provided in a prompt (input) is generally less computationally intensive than generating new, coherent, and contextually relevant text (output). The 8:1 ratio for GPT-5 (high) is a critical economic factor that developers must account for, as it heavily penalizes applications that generate a lot of text.

The best provider depends on your specific needs. According to our benchmarks, Microsoft Azure offers the best performance, with one configuration optimized for the lowest latency (best for chat) and another for the highest throughput (best for batch processing). Databricks offers a solid, balanced option. The direct OpenAI endpoint is currently the slowest and should only be used if performance is not a critical factor.