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Get all the details on GPT-4.1 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
1.0M tokens
Output Limit
32.8K tokens
Input Cost
$0.40/1M
Output Cost
$1.60/1M
Sample API Code
from openai import OpenAI
client = OpenAI()
response = client.chat.completions.create(
model="gpt-4.1-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 |
|---|---|---|---|
1322 | OpenLLM Leaderboard | Elo rating | |
1189 | OpenLLM Leaderboard | Elo rating | |
1237 | OpenLLM Leaderboard | Elo rating | |
961 | OpenLLM Leaderboard | Elo rating | |
59.05 | LiveBench | Percentage | |
53.78 | LiveBench | Percentage | |
72.11 | LiveBench | Percentage | |
58.78 | LiveBench | Percentage | |
61.34 | LiveBench | Percentage | |
38.00 | LiveBench | Percentage | |
70.31 | LiveBench | Percentage | |
49.6 | Vellum | Percentage | |
65 | Vellum | Percentage | |
23.6 | Vellum | Percentage | |
34.7 | Vellum | Percentage |
Notes
Balanced for intelligence, speed, and cost. Provides a balance between intelligence, speed, and cost that makes it an attractive model for many use cases.
Capabilities
fine tuning
function calling
long context
multimodal input
streaming
structured outputs
Supported Data Types
Input Types
text
image
Output Types
text
Strengths & Weaknesses
Good at
balanced performance
intelligence
speed
cost efficiency
multimodal understanding
coding tasks
data analysis tasks
Poor at
language tasks
Additional Information
Latest Update
Apr 14, 2025
Knowledge Cutoff
Jun 1, 2024