Author's Description
OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning and coding performance across benchmarks like AIME (99.5% with Python) and SWE-bench, outperforming its predecessor o3-mini and even approaching o3 in some domains. Despite its smaller size, o4-mini exhibits high accuracy in STEM tasks, visual problem solving (e.g., MathVista, MMMU), and code editing. It is especially well-suited for high-throughput scenarios where latency or cost is critical. Thanks to its efficient architecture and refined reinforcement learning training, o4-mini can chain tools, generate structured outputs, and solve multi-step tasks with minimal delay—often in under a minute.
Key Specifications
Supported Parameters
This model supports the following parameters:
Features
This model supports the following features:
Performance Summary
OpenAI o4-mini demonstrates moderate speed performance, ranking in the 38th percentile across benchmarks. Its pricing tends to be at premium levels, placing it in the 18th percentile. A standout feature is its exceptional reliability, achieving a 100% success rate with minimal technical failures. The model excels in several key areas. It achieved perfect accuracy in General Knowledge, making it the most accurate model at its price point and among models of comparable speed. O4-mini also shows strong performance in Reasoning and Coding, scoring 98.0% and 94.0% accuracy respectively, placing it in the 93rd percentile for both categories. Its Instruction Following capabilities are also robust, with 76.0% accuracy (88th percentile). In Ethics and Email Classification, it performs competently with 99.0% and 98.0% accuracy. A notable weakness appears in the Hallucinations Baseline, where its 86.0% accuracy (28th percentile) suggests room for improvement in acknowledging uncertainty. Despite its premium pricing, its high accuracy in critical reasoning and coding tasks, combined with its perfect reliability, positions o4-mini as a powerful tool for demanding applications.
Model Pricing
Current Pricing
Feature | Price (per 1M tokens) |
---|---|
Prompt | $1.1 |
Completion | $4.4 |
Input Cache Read | $0.275 |
Web Search | $10000 |
Price History
Available Endpoints
Provider | Endpoint Name | Context Length | Pricing (Input) | Pricing (Output) |
---|---|---|---|---|
OpenAI
|
OpenAI | openai/o4-mini-2025-04-16 | 200K | $1.1 / 1M tokens | $4.4 / 1M tokens |
Benchmark Results
Benchmark | Category | Reasoning | Strategy | Free | Executions | Accuracy | Cost | Duration |
---|
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