An intelligent and concise model from OpenAI that offers top-tier analytical power but comes with a high output price and slower-than-average speed.
GPT-5 mini (minimal) represents OpenAI's latest entry into the smaller model category, but its performance profile is anything but minimal. It establishes a distinct identity by combining top-tier intelligence with remarkable conciseness, a pairing that sets it apart from many of its peers. This model is designed for users who prioritize accuracy and brevity over raw speed. With a massive 400,000-token context window and the ability to process both text and image inputs, it is positioned as a powerful tool for deep analysis of complex, multi-format information. However, this power comes with significant trade-offs, most notably in its slower performance and a premium pricing model for generated text.
On the Artificial Analysis Intelligence Index, GPT-5 mini (minimal) achieves a score of 42, placing it firmly in the upper echelon of models, ranking #11 out of 77. This score is substantially higher than the class average of 28, indicating its strong capabilities in reasoning, instruction following, and complex problem-solving. This intellectual prowess is contrasted sharply by its speed. With a median output of just 71.2 tokens per second, it falls into the bottom half of the performance rankings (#46 out of 77), well below the average of 93 tokens per second. This profile paints a clear picture: GPT-5 mini is a deliberate thinker, not a rapid-fire generator, making it better suited for backend analysis than real-time user interaction.
The cost structure of GPT-5 mini (minimal) is a critical factor in its evaluation. The input price of $0.25 per million tokens is moderate and aligns with the market average. The output price, however, is a steep $2.00 per million tokens, making it one of the more expensive models for generative tasks. This pricing strategy heavily penalizes workloads that produce large amounts of text. The blended price, assuming a typical 3:1 input-to-output ratio, is $0.69 per million tokens. The total cost to run the model through the comprehensive Intelligence Index benchmark was $28.07, a testament to its premium positioning.
A key distinguishing feature that can help mitigate its high output cost is its exceptional conciseness. In our benchmark testing, GPT-5 mini (minimal) generated only 4.7 million tokens, ranking it #5 out of 77 for brevity. This is less than half the average of 11 million tokens generated by other models on the same set of tasks. This natural tendency towards succinctness means that for many queries, it will use fewer output tokens to deliver a complete answer, directly reducing the cost of generation. This makes the model a compelling, if nuanced, choice for tasks like summarization, data extraction, and any application where direct, to-the-point answers are valued over verbose, conversational responses.
42 (#11 / 77)
71.2 tokens/s
$0.25 / 1M tokens
$2.00 / 1M tokens
4.7M tokens
0.95 seconds
| Spec | Details |
|---|---|
| Owner | OpenAI |
| License | Proprietary |
| Context Window | 400,000 tokens |
| Knowledge Cutoff | May 2024 |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Intelligence Index Score | 42 |
| Blended Price (3:1) | $0.69 / 1M tokens |
| Input Price | $0.25 / 1M tokens |
| Output Price | $2.00 / 1M tokens |
| Median Latency (TTFT) | 0.95 seconds |
| Median Output Speed | 71.2 tokens/s |
GPT-5 mini (minimal) is exclusively available through its creator, OpenAI. As the sole provider, the choice of where to access the model is straightforward. Users benefit from a direct-from-source integration, ensuring they are always using the most optimized and up-to-date version of the model via the official API.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Best Performance | OpenAI | Direct API access ensures the lowest possible latency and highest throughput the model architecture allows. | None, as it is the only provider available. |
| Lowest Price | OpenAI | The standard pricing is the only pricing available, with no competition from other cloud providers. | There is no opportunity for price shopping or leveraging committed-use discounts from other platforms. |
| Latest Version | OpenAI | As the developer, OpenAI always serves the most up-to-date version of the model. | Users are subject to OpenAI's release and deprecation schedule, with no option to stay on older versions. |
| Ease of Use | OpenAI | The API is well-documented with extensive official and community support, making integration straightforward. | Reliance on a single provider creates vendor lock-in within the OpenAI ecosystem. |
Provider performance and pricing can change. The data presented here is based on benchmarks conducted by Artificial Analysis and reflects a snapshot in time. As this model is only available from OpenAI, all metrics reflect their API performance.
To understand the practical cost implications of GPT-5 mini (minimal), let's examine a few common workloads. These scenarios highlight how the model's unique pricing structure—average input cost but high output cost—affects the final price depending on the task's nature. Notice how the cost shifts dramatically based on the ratio of input to output tokens.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Summarize a long report | 20,000 tokens | 500 tokens | Analyzing a dense document to extract key points. A high input-to-output ratio. | $0.006 |
| Extract structured data | 10,000 tokens | 1,000 tokens | Parsing unstructured text from an article into a structured JSON object. | $0.0045 |
| Customer support chat | 2,500 tokens | 2,500 tokens | An interactive conversation with a balanced number of input and output tokens. | $0.0056 |
| Draft a blog post | 200 tokens | 1,500 tokens | A generative task where the output is much larger than the input prompt. A low input-to-output ratio. | $0.0031 |
The takeaway is clear: GPT-5 mini (minimal) is most cost-effective for tasks with a high input-to-output ratio, such as summarization or analysis. For generative tasks where output tokens dominate, the high output price makes it a premium, and potentially costly, choice compared to other models.
Given its premium output pricing and slower speed, managing the implementation of GPT-5 mini (minimal) is crucial for production use. The key is to lean into its strengths—intelligence and conciseness—while actively mitigating the impact of its primary cost driver: expensive output tokens. Here are several strategies to build a cost-effective and performant application around this model.
Design workflows that capitalize on the model's moderate input pricing and large context window. It is most cost-effective when the value comes from processing information, not generating it.
Avoid using this powerful model for simple tasks. Instead, create a routing system that uses cheaper, faster models as a first line of defense, only escalating to GPT-5 mini when its intelligence is truly required.
While the model is naturally concise, you can further reduce output token count through careful prompt engineering. Since output tokens are the main cost, every token saved has a significant impact.
The model's slower speed can harm user experience in real-time applications. Employ techniques to manage this latency and improve perceived performance.
GPT-5 mini (minimal) excels at tasks requiring high accuracy, deep understanding of large contexts, and concise outputs. Ideal use cases include:
The pricing reflects a strategy that values the generation of high-quality, intelligent text more than the processing of input data. The high output price of $2.00/M tokens positions it as a premium model for generative tasks. This encourages its use for analysis-heavy workloads (high input, low output) where its cost is more competitive and its intelligence provides maximum value.
GPT-5 mini (minimal) is slower than the average model in its class, with a median output of approximately 71 tokens per second. This makes it less suitable for applications that demand instant, real-time responses, such as high-traffic conversational AI or interactive content creation tools where users expect immediate feedback.
While powerful, the 400k context window is a specialized tool. It is only useful if your task requires processing that much information at once (e.g., analyzing an entire book, a large codebase, or hours of transcripts). For smaller tasks, it provides no benefit, and filling the context window unnecessarily can be expensive and may even slow down inference.
It means the model can accept both text and images as input within the same prompt. You can, for example, provide an image of a chart and ask the model to analyze the data, or upload a document containing diagrams and have it answer questions about the entire content. The model's output, however, is always text.
Its high level of conciseness is a major economic advantage. By naturally generating fewer tokens to provide a complete answer, it directly reduces costs from the expensive output tokens. This can partially offset the high per-token price, especially if you reinforce this behavior with prompting. In essence, you pay more per token, but you often need fewer of them.