Sub-second Response Latency
Optimized for speed, the 4.1 mini delivers tokens twice as fast as GPT-4o for a seamless user experience.
gpt-4.1-mini / file-analysis
OpenAI GPT 4.1 Mini offers flagship-level intelligence with ultra-low latency. This cost-optimized model supports a 128k context window and native vision, making it the premier choice for high-volume, production-grade AI applications.
$ 0.28
$ 0.4
file
$ 1.12
$ 1.6
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": "gpt-4.1-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"
}
]
}
]
}'Core OpenAI GPT 4.1 Mini Features
The gpt 4.1 mini combines high-end reasoning with the efficiency required for global production.
Native Structured Outputs
Ensure 100% adherence to JSON schemas. Perfect for developers who need reliable openai data parsing.
Enhanced Vision Reasoning
The 4.1 mini excels at visual OCR and UI element identification, outperforming previous small models.
High Intelligence-to-Price Ratio
Achieve MMLU scores over 83%. This gpt variant offers better logic than GPT-4 at a mini price point.
Build with gpt 4.1 mini in Minutes
Follow these simple steps to set up your account, get credits, and start sending API requests to gpt 4.1 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 gpt 4.1 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 gpt 4.1 mini.
Make your first API call
Use your API key with our sample code to send a request to gpt 4.1 mini via GPT Proto and see instant AI-powered results.
OpenAI GPT 4.1 Mini FAQ
How do I migrate to gpt 4.1 mini from older versions?
Transitioning is straightforward as the gpt 4.1 mini maintains full compatibility with the existing openai api structure. You only need to update the model parameter in your configuration to start benefiting from the improved 4.1 speed and reasoning capabilities. No structural code changes are required for JSON schema support or tool calling.
Is my data used to train the openai gpt 4.1 mini model?
No. Data privacy is a core priority. Any information sent to the openai gpt 4.1 mini via our platform is not utilized for model training or refinement. This ensures that your proprietary business data remains secure while you leverage the advanced intelligence of the gpt 4.1 architecture.
What is the typical latency for gpt 4.1 mini requests?
The gpt 4.1 mini is engineered for real-time performance. Users typically experience a Time To First Token (TTFT) between 120ms and 250ms. This makes the mini model roughly 2x faster than larger flagship models, perfect for interactive openai chat applications and low-latency tools.
Does this gpt model support 100% valid JSON outputs?
Yes, by enabling 'strict: true' in your openai request, the gpt 4.1 mini guarantees 100% adherence to your provided JSON schema. This native structured output capability eliminates validation errors, making it ideal for document extraction and automated data pipelines using openai technology.
Can I use openai fine-tuning with the 4.1 mini version?
The gpt 4.1 mini supports fine-tuning, allowing you to adapt the model to specific domain knowledge or unique brand voices. This process is managed through our dashboard, combining the cost-efficiency of the mini architecture with the specialized performance your business requires.
What are the openai rate limits for this model?
Standard tiers for the gpt 4.1 mini support 3,500 requests per minute. For larger openai deployments, enterprise tiers are available that scale up to 10,000 RPM. Our platform also provides built-in failover to alternate regions if the primary openai cluster experiences high load.
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