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o4-mini - In-Depth Overview

OpenAI · o-series

Current
Latest in family

Get all the details on o4-mini, an AI model from OpenAI. This page covers its token limits, pricing structure, key capabilities such as fine_tuning, function_calling, long_context, available API code samples, and performance strengths.

Key Metrics

Input Limit

200K tokens

Output Limit

100K tokens

Input Cost

$1.10/1M

Output Cost

$4.40/1M

Sample API Code

from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
  model="o4-mini",
  messages=[
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Hello!"}
  ]
)
print(response.choices[0].message.content)

Required Libraries

openai
openai

Benchmarks

BenchmarkScoreSourceNotes
1351
OpenLLM Leaderboard-
1093
OpenLLM Leaderboard-
1265
OpenLLM Leaderboard-
78.72
LiveBenchHigh configuration
74.40
LiveBenchMedium configuration
88.11
LiveBenchHigh configuration
78.47
LiveBenchMedium configuration
79.98
LiveBenchHigh configuration
74.22
LiveBenchMedium configuration
84.90
LiveBenchHigh configuration
81.02
LiveBenchMedium configuration
68.33
LiveBenchHigh configuration
68.47
LiveBenchMedium configuration
66.05
LiveBenchHigh configuration
62.41
LiveBenchMedium configuration
84.96
LiveBenchHigh configuration
81.83
LiveBenchMedium configuration
81.4%
Vellum-
93.4%
Vellum-
68.1%
Vellum-
68.9%
Vellum-
50%
Vellum-

Notes

Optimized for fast, effective reasoning with exceptionally efficient performance in coding and visual tasks. Part of the latest small o-series models.

Capabilities

fine tuning
function calling
long context
multimodal input
streaming
structured outputs

Supported Data Types

Input Types

text
image

Output Types

text

Strengths & Weaknesses

Exceptional at

coding tasks
visual tasks
high school math

Good at

fast reasoning
effective reasoning
general reasoning
instruction following
coding
data analysis

Additional Information

Latest Update

Apr 16, 2025

Knowledge Cutoff

Jun 1, 2024