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OpenAI: o4 Mini

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.

Context Window

200K

Input Price

$1.10/M tokens

Output Price

$4.40/M tokens

Modality

text+image+file->text

Pricing Breakdown
Cost per million tokens for input and output
Input$1.10/M tokens
Output$4.40/M tokens

Cost Example: Processing 10,000 input tokens and generating 1,000 output tokens would cost approximately $0.015400

Context Window Size
Maximum tokens the model can process in a single request
Context Window200K tokens
20% of 1M max

~150K

Words Equivalent

~100

Pages of Text

~2000

Code Functions

Best Use Cases
What this model excels at
CodingProgrammingDevelopmentMathReasoningAnalysisVisionImage-analysisMultimodal

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