A top-tier model excelling in intelligence and speed, offering competitive pricing and a vast context window for advanced applications.
The o4-mini (high) model stands out as a formidable contender in the landscape of large language models, demonstrating exceptional performance across critical benchmarks. Developed by OpenAI, this proprietary model is engineered for high-demand applications, offering a compelling blend of intelligence, speed, and cost-efficiency. Its ability to process both text and image inputs, coupled with a substantial 200,000-token context window, positions it as a versatile tool for complex generative AI tasks.
Scoring an impressive 60 on the Artificial Analysis Intelligence Index, o4-mini (high) significantly surpasses the average intelligence of comparable models (which typically hover around 44). This places it firmly within the top echelon of models for reasoning and understanding, ranking #19 out of 101 models evaluated. While its output can be somewhat verbose, generating 76 million tokens during the Intelligence Index evaluation compared to an average of 28 million, this verbosity often translates to more comprehensive and detailed responses, which can be an advantage depending on the use case.
Performance-wise, o4-mini (high) is notably fast, achieving an average output speed of 112.6 tokens per second. When deployed via Microsoft Azure, this speed can reach up to 126 tokens per second, with an excellent time to first token (TTFT) latency of just 35.99 seconds. This makes it highly suitable for real-time applications where quick responses are paramount. Its pricing structure is also highly competitive, with input tokens costing $1.10 per million and output tokens at $4.40 per million, both of which are well below industry averages of $1.60 and $10.00 respectively. This aggressive pricing, combined with its robust capabilities, makes o4-mini (high) an economically attractive option for developers and enterprises.
The model's balanced profile—high intelligence, rapid processing, and cost-effectiveness—makes it an ideal choice for a wide array of applications, from advanced content generation and complex data analysis to sophisticated conversational AI and multimodal understanding. Its strong performance across key metrics, as evidenced by its high rankings in intelligence and speed, underscores its potential to drive significant value in demanding AI workflows.
60 (#19 / 101)
112.6 tokens/s
$1.10 $/M tokens
$4.40 $/M tokens
76M tokens
35.99 seconds
| Spec | Details |
|---|---|
| Owner | OpenAI |
| License | Proprietary |
| Context Window | 200,000 tokens |
| Input Modalities | Text, Image |
| Output Modalities | Text |
| Intelligence Index Score | 60 (#19 / 101) |
| Average Output Speed | 112.6 tokens/s (#20 / 101) |
| Lowest Latency (TTFT) | 35.99s (Azure) |
| Input Token Price | $1.10 / 1M tokens (#26 / 101) |
| Output Token Price | $4.40 / 1M tokens (#20 / 101) |
| Blended Price (Avg) | $1.93 / 1M tokens |
| Verbosity (Intelligence Index) | 76M tokens (#49 / 101) |
Choosing the right API provider for o4-mini (high) can significantly impact performance and cost. While both Microsoft Azure and OpenAI offer access to this powerful model, their specific optimizations and service level agreements can make one a better fit depending on your primary objectives.
Our analysis reveals distinct advantages for each provider across key metrics, allowing you to tailor your deployment strategy to prioritize speed, cost, or a balanced approach.
| Priority | Pick | Why | Tradeoff to accept |
|---|---|---|---|
| Performance Priority | Microsoft Azure | Azure offers the fastest output speed (126 t/s) and the lowest latency (35.99s TTFT). | Slightly higher blended cost compared to OpenAI's base offering, but worth it for speed. |
| Cost Priority | OpenAI | Matches Azure's input and output token prices, offering the same competitive rates. | Slightly higher latency and lower peak output speed compared to Azure. |
| Balanced Approach | Microsoft Azure | Provides an excellent balance of top-tier performance (speed, latency) with competitive pricing. | Requires integration into the Azure ecosystem, which might be a consideration for some. |
| Latency-Critical Applications | Microsoft Azure | With the lowest Time To First Token (35.99s), Azure is the clear choice for real-time interactive experiences. | Focus on latency might mean less emphasis on raw throughput in some scenarios. |
| Redundancy & Multi-cloud | OpenAI | Direct access to OpenAI's API can serve as a primary or secondary provider for redundancy or specific regional needs. | May not always match Azure's specialized performance optimizations for this model. |
Note: Performance and pricing data are based on benchmark tests and may vary depending on region, specific API configurations, and usage patterns.
Understanding the real-world cost of using o4-mini (high) involves considering typical input and output token counts for various applications. Below are estimated costs for common scenarios, based on its competitive input price of $1.10/M tokens and output price of $4.40/M tokens.
These examples illustrate how the model's pricing structure translates into practical application costs, helping you budget and optimize your usage.
| Scenario | Input | Output | What it represents | Estimated cost |
|---|---|---|---|---|
| Advanced Summarization | 5,000 tokens (article) | 500 tokens (summary) | Condensing a long document into a concise overview. | $0.0077 |
| Long-form Content Generation | 1,000 tokens (prompt) | 10,000 tokens (article) | Generating a detailed blog post or report from a brief outline. | $0.0451 |
| Complex Code Generation | 10,000 tokens (requirements) | 2,000 tokens (code) | Generating a complex software module based on detailed specifications. | $0.0198 |
| Multimodal Image Captioning | 1,000 tokens (image description + prompt) | 200 tokens (caption) | Generating descriptive captions for images, assuming image processing is part of input cost. | $0.00198 |
| Interactive Chatbot Session | 200 tokens (user query) | 300 tokens (bot response) | A single turn in a dynamic, intelligent conversation. | $0.00154 |
| Data Extraction & Analysis | 50,000 tokens (raw data) | 5,000 tokens (extracted insights) | Processing a large dataset to identify key patterns and generate reports. | $0.0275 |
o4-mini (high)'s competitive pricing makes it highly economical for both short, frequent interactions and longer, more complex generative tasks. Even with its tendency for verbosity, the low output token cost helps keep overall expenses manageable, especially for applications requiring detailed responses.
Optimizing costs while leveraging the full power of o4-mini (high) requires a strategic approach. Given its high intelligence and competitive pricing, focusing on efficient prompt engineering and output management can yield significant savings.
Here are key strategies to maximize value and minimize expenditure:
While o4-mini (high) boasts a 200k context window, every input token costs. Design prompts to be concise yet comprehensive, providing only necessary context. Avoid redundant information or excessively long examples if shorter ones suffice.
o4-mini (high) can be verbose, which is great for detail but can increase output token costs. Explicitly instruct the model on desired output length and format.
Choose your API provider strategically based on your primary needs. Azure offers superior performance for latency-sensitive and high-throughput applications, while OpenAI provides a direct, robust alternative.
For tasks that don't require immediate responses, consider batching requests. This can sometimes lead to better throughput and potentially more efficient resource utilization from the provider's side, though direct cost savings per token are less common.
Regularly review your token consumption and costs. Most providers offer detailed usage dashboards that can highlight areas of inefficiency or unexpected spend.
o4-mini (high) achieves a score of 60 on the Artificial Analysis Intelligence Index, placing it at #19 out of 101 models. This indicates a superior capability in understanding, reasoning, and generating complex, coherent responses, significantly outperforming the average model intelligence of 44.
The model is notably fast, with an average output speed of 112.6 tokens per second. When deployed on Microsoft Azure, it can reach 126 tokens/s and boasts an excellent Time To First Token (TTFT) latency of 35.99 seconds, making it highly suitable for real-time and interactive applications.
Yes, o4-mini (high) offers highly competitive pricing. Its input token price of $1.10 per million and output token price of $4.40 per million are both substantially lower than the industry averages of $1.60/M and $10.00/M respectively, providing significant cost savings.
o4-mini (high) features a generous 200,000-token context window. This means it can process and retain a vast amount of information within a single interaction, allowing for the generation of highly relevant and contextually aware responses for very long documents or complex conversational histories.
Yes, o4-mini (high) supports both text and image inputs, enabling it to understand and generate responses based on a combination of textual prompts and visual information. Its output modality is text.
While o4-mini (high) is described as somewhat verbose (generating 76M tokens in benchmark vs. 28M average), this often translates to more detailed and comprehensive outputs. Users should consider prompt engineering techniques to manage output length if conciseness is a primary requirement, to optimize output token costs.
o4-mini (high) is owned by OpenAI and is offered under a proprietary license. This means access is typically through OpenAI's API or via partner platforms like Microsoft Azure, subject to their terms of service.