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Get all the details on o3-mini, an AI model from OpenAI. This page covers its token limits, pricing structure, key capabilities such as structured_outputs, function_calling, batch_api, 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="o3-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
Benchmark | Score | Source | Notes |
---|---|---|---|
1302 | OpenLLM Leaderboard | - | |
1092 | OpenLLM Leaderboard | - | |
79.7% | Vellum | - | |
87.3% | Vellum | - | |
61% | Vellum | - | |
97.9% | Vellum | - | |
65.12% | Vellum | - | |
60.4% | Vellum | - | |
14 | Vellum | - |
Notes
o3-mini is a small reasoning model providing high intelligence at the same cost and latency targets of o1-mini. It supports key developer features like Structured Outputs, function calling, and Batch API. It has a 200,000 context window and 100,000 max output tokens. Reasoning token support is available.
Supported Data Types
Input Types
text
Output Types
text
Strengths & Weaknesses
Exceptional at
reasoning
Good at
structured outputs
function calling
batch api
Additional Information
Latest Update
Jan 31, 2025
Knowledge Cutoff
Oct 1, 2023