A powerful but verbose and expensive model from OpenAI, excelling in intelligence but requiring careful cost management and performance trade-offs.
GPT-5 nano (medium) emerges as a formidable entry in OpenAI's next generation of language models, positioning itself as a high-intellect powerhouse designed for complex tasks. It represents a significant leap in reasoning capabilities, but this advancement comes with a notable set of trade-offs. While it boasts impressive generation speed and a massive context window, its operational costs are among the highest in the market, driven by premium pricing and a striking tendency towards verbosity. This makes it a specialized tool: immensely powerful for the right job, but potentially inefficient and costly for general-purpose use without careful implementation.
On the Artificial Analysis Intelligence Index, GPT-5 nano (medium) achieves a score of 49, placing it firmly in the top echelon of models and ranking #6 out of 120. This score is more than double the class average of 19, underscoring its advanced analytical and reasoning skills. However, a critical factor revealed during this evaluation was its verbosity. The model generated a staggering 62 million tokens to complete the benchmark tasks, a five-fold increase over the 12 million token average. This chattiness has direct and significant implications for cost, as every extra token generated incurs a fee at its premium output rate.
The pricing structure of GPT-5 nano (medium) is a key consideration for any developer. At $0.05 per 1 million input tokens and a steep $0.40 per 1 million output tokens, it is classified as expensive. The total cost to run our Intelligence Index evaluation on this model was $29.44, a substantial figure that highlights the financial commitment required. The asymmetric pricing heavily penalizes applications that generate lengthy responses, a characteristic this model is naturally inclined towards. This makes understanding and controlling the input-to-output ratio essential for managing its total cost of ownership.
Despite its cost, the model delivers on performance in other areas. With an output speed of 121 tokens per second, it is faster than the average model in its class (106 t/s), ensuring a relatively fluid experience in interactive settings once generation begins. Furthermore, its capabilities are enhanced by a vast 400,000-token context window and multimodal input support for both text and images. This combination allows it to tackle sophisticated, context-heavy tasks like analyzing extensive legal documents with embedded diagrams or debugging large, multi-file codebases, solidifying its role as a high-end, specialist tool.
49 (#6 / 120)
121.0 tokens/s
$0.05 / 1M tokens
$0.40 / 1M tokens
62M tokens
42.81 s
| Spec | Details |
|---|---|
| Model Owner | OpenAI |
| License | Proprietary |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Context Window | 400,000 tokens |
| Knowledge Cutoff | May 2024 |
| Intelligence Index Score | 49 / 100 |
| Intelligence Rank | #6 / 120 |
| Avg. Output Speed (OpenAI) | 121.0 tokens/s |
| Avg. Latency (TTFT on Azure) | 42.81 seconds |
| Input Price | $0.05 / 1M tokens |
| Output Price | $0.40 / 1M tokens |
GPT-5 nano (medium) is available from its creator, OpenAI, and through Microsoft Azure. While the list prices for the model are identical across both platforms, our performance benchmarks reveal significant differences in speed and latency. This makes the choice of provider a critical decision, especially for applications where user experience and operational efficiency are paramount.
Our analysis benchmarks these providers head-to-head to help you make the optimal choice based on your priorities, whether that's raw generation speed, minimum response latency, or simply the best overall value.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Raw Speed (tokens/s) | Azure | Azure delivered 167 t/s, over 35% faster than OpenAI's 121 t/s in our benchmarks. | None; it also leads in latency and matches on price. |
| Lowest Latency (TTFT) | Azure | Azure's 42.81s TTFT is significantly lower than OpenAI's 61.51s, reducing user wait time. | None; it also leads in raw speed. |
| Lowest Price | Tie | Both Azure and OpenAI offer identical pricing: $0.05/1M input and $0.40/1M output tokens. | Given Azure's performance advantages, it offers better value despite the price parity. |
| Overall Best Value | Azure | Offers superior speed and lower latency for the exact same price, making it the clear choice for performance-sensitive applications. | Potential for less direct access to the newest alpha features that might appear on the OpenAI API first. |
Performance benchmarks represent a snapshot in time and can vary based on region, time of day, and specific workload. Prices are as of May 2024 and are subject to change by the providers. TTFT stands for Time to First Token.
To understand the practical cost implications of GPT-5 nano (medium)'s pricing and verbosity, let's estimate the cost for several common real-world scenarios. These examples, based on its $0.05/1M input and $0.40/1M output pricing, highlight how the balance of input and output tokens dramatically affects the final price of a task.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Customer Support Chat (1000 sessions) | 1.5M tokens | 3.0M tokens | A typical interactive chat where the model's responses are longer than user queries. | $1.28 |
| Document Summarization (50 long articles) | 5.0M tokens | 0.5M tokens | An input-heavy task where a large document is condensed into a short summary. | $0.45 |
| Code Generation (200 complex functions) | 0.2M tokens | 4.0M tokens | An output-heavy task where a short prompt generates a large block of code. | $1.61 |
| RAG Analysis (100 queries) | 20.0M tokens | 1.0M tokens | Retrieval-Augmented Generation using the large context window to analyze retrieved documents. | $1.40 |
These scenarios demonstrate that output-heavy tasks like code generation and verbose chat are significantly more expensive than input-heavy tasks like summarization. The model's high verbosity and punishing output price are the primary cost drivers to monitor and control in any application.
Given GPT-5 nano (medium)'s high and asymmetric pricing, actively managing costs is crucial for production viability. Simply using the model without optimization can lead to unsustainable expenses. The following strategies can help you mitigate costs by targeting the model's specific weaknesses—its verbosity and high output price—without completely sacrificing its powerful capabilities.
The model's default verbosity is its biggest cost driver. You must actively counteract this tendency in your prompts. This is the most direct way to control costs.
Use the max_tokens parameter in your API calls as a non-negotiable backstop. This prevents runaway costs from unexpectedly verbose outputs, which can occur even with careful prompting.
Not all tasks require GPT-5 nano's elite intelligence. A 'model cascade' or 'router' approach can dramatically reduce costs by filtering requests.
For applications with repetitive queries, implementing a caching layer is a simple and effective cost-saving measure. This is particularly useful for customer support bots or information retrieval systems.
GPT-5 nano (medium) is a high-performance, multimodal large language model from OpenAI. It is part of the next-generation GPT-5 family, engineered to provide top-tier intelligence and fast generation speeds, albeit at a premium price point. It is designed for complex reasoning, analysis, and generation tasks.
GPT-5 nano (medium) represents a significant step up from models like GPT-4 Turbo in several key areas. It achieves a much higher score on intelligence and reasoning benchmarks, features a larger context window (400k vs. 128k), and offers faster output speeds. However, these improvements come at the cost of being substantially more expensive per token and exhibiting much higher verbosity and latency.
Multimodal means the model can accept and process multiple types of input data within a single prompt. For GPT-5 nano (medium), this specifically refers to its ability to understand both text and images simultaneously. This allows it to perform advanced tasks like answering questions about a photograph, analyzing a chart within a document, or describing the contents of a user-uploaded image.
The high Time to First Token (TTFT) of over 40 seconds is a direct consequence of the model's immense size and complexity. It requires a significant amount of computation to process the prompt and prepare the initial part of its response. This 'think time' makes it less suitable for applications that require instant, real-time feedback, such as conversational AI assistants.
It's a double-edged sword. Its high intelligence enables incredibly sophisticated and helpful conversations. However, its significant drawbacks—high latency (long pauses), extreme verbosity, and expensive output cost—make it a challenging choice for a high-volume chatbot. Without aggressive optimization (like prompt engineering for brevity and using a model router), it can be both slow and prohibitively expensive.
Based on current performance benchmarks, Microsoft Azure offers a superior experience for the same price. It provides significantly higher output speed (tokens/second) and lower latency (time to first token) compared to using the model directly via the OpenAI API. For most production use cases where performance and user experience are critical, Azure is the recommended provider.