An exceptionally intelligent and multimodal model from OpenAI that excels at complex reasoning but carries a high price tag for its verbose, high-quality output.
GPT-5 mini (high) represents a significant leap in reasoning capability from OpenAI, positioning itself as a premier choice for tasks demanding deep understanding and complex problem-solving. Scoring an impressive 64 on the Artificial Analysis Intelligence Index, it ranks #2 out of 134 models, firmly establishing it in the top echelon of AI intelligence. This model is not designed for simple, high-volume tasks; rather, it is a specialized instrument for scenarios where the quality and accuracy of the output are paramount, such as in legal analysis, scientific research, or advanced software development.
While its intelligence is its main selling point, its performance profile presents a more nuanced picture. With an average output speed of 71.5 tokens per second, it operates at a pace slower than the class average of 93 tokens/s. This deliberate pace suggests an architecture optimized for depth of thought over raw speed. Similarly, its latency, or time to first token, is not class-leading. This means that for real-time, interactive applications like a snappy chatbot, GPT-5 mini (high) might introduce a noticeable delay. Users should approach this model with the expectation that they are trading speed for superior cognitive ability.
The cost structure of GPT-5 mini (high) is a critical factor in its evaluation. The input token price of $0.25 per million tokens is moderate and aligns with the market average. However, the output token price is a steep $2.00 per million tokens, placing it among the most expensive models for text generation. This pricing strategy heavily penalizes verbosity. The model's tendency to be verbose—generating 84 million tokens during intelligence testing compared to the 30 million average—exacerbates this cost. The total expense to run the intelligence benchmark, a staggering $181.65, serves as a stark illustration of how quickly costs can accumulate, particularly in generative or conversational use cases.
Beyond its core performance, GPT-5 mini (high) is equipped with a powerful set of features. It supports multimodal inputs, allowing it to analyze and interpret both text and images. This opens up a wide range of applications, from describing visual data to understanding complex diagrams. Its massive 400,000-token context window is another standout feature, enabling it to ingest and reason over entire books, extensive legal documents, or large codebases in a single pass. Combined with a recent knowledge cutoff of May 2024, the model is not only powerful but also current, making it a formidable tool for a variety of advanced AI applications.
64 (#2 / 134)
71.5 tokens/s
$0.25 / 1M tokens
$2.00 / 1M tokens
84M tokens
~94 ms
| Spec | Details |
|---|---|
| Model Owner | OpenAI |
| License | Proprietary |
| Context Window | 400,000 tokens |
| Knowledge Cutoff | May 2024 |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Architecture | Transformer-based |
| Fine-tuning | Not Supported |
| API Providers | OpenAI, Microsoft Azure, Databricks |
| Intelligence Index | 64 (#2 / 134) |
| Input Pricing | $0.25 / 1M tokens |
| Output Pricing | $2.00 / 1M tokens |
While GPT-5 mini (high) has uniform pricing across its main API providers, performance metrics like latency and throughput show slight variations. Your choice of provider may depend on whether your application prioritizes the fastest initial response or the quickest overall generation, or if it needs to integrate seamlessly into an existing cloud ecosystem.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Lowest Latency | Microsoft Azure | At ~77ms TTFT, Azure provides the quickest initial response, which is vital for interactive applications where every millisecond counts. | The throughput advantage over OpenAI is marginal and may not be noticeable in practice. |
| Highest Throughput | Microsoft Azure | Generating at 77 tokens/s, Azure is the fastest option for producing long-form content, reducing the total time to receive a complete response. | This speed comes with the standard high output cost of the model itself. |
| Balanced Performance | OpenAI | As the direct source, OpenAI offers a solid blend of low latency (~90ms) and strong throughput (71 t/s), providing a reliable and well-rounded experience. | It is slightly outperformed by Azure on both key speed metrics. |
| Databricks Ecosystem | Databricks | The definitive choice if your data, models, and workflows are already hosted on the Databricks platform, simplifying integration and governance. | This convenience comes at the cost of performance; it is the slowest of the three providers in both latency and throughput. |
*Performance benchmarks are based on data at a specific point in time and can fluctuate based on geographic region, server load, and other factors. We recommend conducting your own tests to determine the best provider for your specific use case.
The abstract pricing of 'dollars per million tokens' can be difficult to translate into tangible business costs. To make this clearer, let's examine several real-world scenarios. These examples highlight how the ratio of input to output tokens dramatically affects the final cost of using GPT-5 mini (high).
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Legal Document Review | 350,000 tokens | 5,000 tokens | Analyzing a large contract to extract key clauses. High-input, low-output. | ~$0.098 |
| Customer Support Chat | 25,000 tokens | 25,000 tokens | A lengthy, balanced conversation with a customer. Symmetric input/output. | ~$0.056 |
| Blog Post Generation | 500 tokens | 2,500 tokens | Creating a detailed article from a short prompt. Low-input, high-output. | ~$0.005 |
| Codebase Refactoring Plan | 150,000 tokens | 10,000 tokens | Ingesting multiple code files to suggest improvements. High-input, medium-output. | ~$0.058 |
| Image Analysis & Description | 750 tokens (image) | 500 tokens | Providing a detailed description of a complex diagram. Low-input, low-output. | ~$0.001 |
These scenarios demonstrate that GPT-5 mini (high) offers the best value proposition for 'analytical' tasks that require processing large amounts of input to generate concise, high-value outputs. It becomes progressively more expensive for 'generative' tasks where the output token count is high, making cost management essential.
Given its premium output pricing and natural verbosity, controlling the cost of GPT-5 mini (high) is essential for any production application. A proactive approach to cost optimization can yield significant savings without compromising the quality of results. Here are several effective strategies to manage your spend.
Design a system that uses cheaper, faster models for initial processing and only escalates to GPT-5 mini (high) when necessary. This is a classic router or cascade pattern.
The most direct way to control output cost is to control output length. Engineer your prompts to explicitly guide the model toward conciseness.
Many applications receive redundant queries. Caching responses from GPT-5 mini (high) prevents you from paying for the same answer multiple times.
If you need the reasoning power of GPT-5 mini (high) but its verbosity is too costly, use a two-step generation process.
GPT-5 mini (high) is a hypothetical high-end model from OpenAI. It is characterized by its state-of-the-art intelligence, multimodal (text and image) input capabilities, and a very large context window. The "(high)" designation suggests it is optimized for maximum reasoning ability within a 'mini' series of models.
The ideal user is a developer, researcher, or business that needs to solve complex problems requiring deep, nuanced reasoning. It is best suited for expert domains like legal tech, scientific research, financial analysis, and advanced software engineering, where the quality of the AI's reasoning justifies the high cost.
The high output price of $2.00 per million tokens likely reflects the immense computational resources required to generate its high-quality, nuanced text. This pricing model encourages users to apply it to tasks where the generated text has a very high value, rather than for casual conversation or bulk content generation.
A 400,000-token context window allows the model to hold and process an enormous amount of information in a single request. This is roughly equivalent to a 300-page book. You can use it to:
Generally, no. Its output speed of ~71 tokens/second is slower than average and may feel sluggish to users accustomed to instant responses. While usable, it is not optimized for low-latency, real-time interaction. It is better suited for asynchronous tasks or applications where users expect a short wait for a high-quality result.
It means the model can accept more than one type of data as input. For GPT-5 mini (high), you can provide it with both text and images in the same prompt. For example, you could upload a picture of a meal and ask, "What is a healthy recipe for this dish?" The model understands the image and uses that visual context to answer the text-based question. It still only produces text as output.