o4 Agentic Autonomy
Independent multi-step tool use, including Python execution and browsing, makes o4 a premier choice for agents.
o4-mini / file-analysis
The o4 mini api brings native multimodal capabilities and agentic tool-use to the "mini" class. It bridges the gap between GPT-4o-mini and frontier models, offering superior STEM logic for complex coding and mathematical reasoning tasks.
$ 0.99
$ 1.1
file
$ 3.96
$ 4.4
text
API
File Analysis
curl --request POST "https://gptproto.com/v1/responses" \
--header "Authorization: Bearer $GPTPROTO_API_KEY" \
--header "Content-Type: application/json" \
--data '{
"model": "o4-mini",
"input": [
{
"role": "user",
"content": [
{
"type": "input_text",
"text": "what is in this file?"
},
{
"type": "input_file",
"file_url": "https://tos.gptproto.com/resource/gptproto.pdf"
}
]
}
]
}'o4 mini api Core Features & Capabilities
Explore how the o4 mini api leverages chain-of-thought logic and multimodal vision to outperform legacy models.
o4 Production Coding
Scoring 85.9% on LiveCodeBench, o4 is optimized for software tasks and maintaining state in long sessions.
Variable Reasoning Depth
Adjust o4 latency and logic depth with low, medium, and high settings to match your specific task complexity.
Multimodal Reasoning in o4
o4 processes visual data directly within its chain-of-thought, excelling at diagram analysis and UI screenshots.
Build with o4 mini in Minutes
Follow these simple steps to set up your account, get credits, and start sending API requests to o4 mini via GPT Proto.
Sign up
Create your free GPT Proto account to begin. You can set up an organization for your team at any time.
Top up
Your balance can be used across all models on the platform, including o4 mini, giving you the flexibility to experiment and scale as needed.
Generate your API key
In your dashboard, create an API key — you'll need it to authenticate when making requests to o4 mini.
Make your first API call
Use your API key with our sample code to send a request to o4 mini via GPT Proto and see instant AI-powered results.
o4 mini api: Common Questions & Logic
What makes the o4 mini api unique?
The o4 mini api is the first in its class to integrate native multimodal reasoning directly into the chain-of-thought. Unlike earlier models that relied on separate vision encoders, o4 processes images and PDFs logically, reducing visual errors by 35%. This makes o4 ideal for complex STEM tasks, UI debugging, and agentic workflows where visual context is just as important as text logic.
How does o4 pricing compare to o3?
The o4 mini api is roughly 10 times more cost-effective than the full o3 model. It costs $1.10 per 1M input tokens and $4.40 per 1M output tokens. Additionally, o4 offers a 50% discount on cached input tokens and a 50% discount for asynchronous processing through the Batch API, making o4 one of the most economical reasoning models available today.
What is the 'reasoning_effort' parameter in o4?
In o4, developers can control the depth of the chain-of-thought using 'low', 'medium', or 'high' values. Using 'low' effort with o4 reduces latency for simpler logic, while 'high' effort allows o4 to spend more time on complex math or coding problems. This flexibility lets you trade speed for accuracy depending on your specific use case.
Does the o4 mini api support tool use?
Yes, o4 is designed for agentic autonomy. It supports parallel function calling, independent web browsing, and executing Python code in sandboxed environments. Because o4 maintains state through the Responses API, it is highly effective at multi-step tasks such as recursive file analysis and autonomous software engineering.
Are thinking tokens billed in o4?
Yes, o4 generates internal reasoning tokens during its 'thinking' phase. Both these internal tokens and the final visible output tokens are billed at the standard rate of $4.40 per 1M tokens. When setting max_completion_tokens in o4, ensure the limit is high enough to accommodate both the hidden reasoning and the final response.
How do I migrate to o4 from o3-mini?
Migrating to the o4 mini api is straightforward. You simply need to update the model parameter to 'o4 mini-2025-04-16' in your API calls. Note that o4 supports a larger 200k context window and includes native multimodal capabilities that were not present in o3-mini, allowing you to expand your application's feature set.
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