LLMs
LLMs
o1-mini API
o1-mini is a streamlined, efficient variant of the o1 model, optimized for quicker logical reasoning tasks at reduced cost and latency.
1RPC.ai
Reasoning
Speed
$1.10
/
$4.40
Input/Output
128,000
Context Window
o1-mini
o1-mini is the compact, faster, and 80% cheaper variant of OpenAI’s o1-preview, launched on September 12, 2024. Trained using the same high-compute reinforcement learning pipeline as o1, it delivers nearly comparable results on challenging benchmarks such as the American Invitational Mathematics Examination (AIME) and Codeforces coding competitions, making it a strong choice for STEM-focused AI applications that require cost-effective, fast reasoning.
While o1-mini excels in technical domains, it offers less breadth of general world knowledge than its larger sibling, catering primarily to precise problem solving in scientific and programming domains.
What it’s optimized for
o1-mini is designed for:
High-quality reasoning in STEM areas like mathematics, physics, chemistry, and coding
Rapid, cost-effective inference for real-time or large-scale deployment
Handling multi-step workflows requiring reliable chain-of-thought reasoning
Applications where broad world knowledge is less critical but strong domain expertise is essential
Typical use cases
o1-mini performs well in:
Automated STEM tutoring and educational tools focusing on math and science
Complex code generation, debugging, and competitive programming support
Scientific research assistance, including data analysis and mathematical derivations
Technical question answering and multi-step problem solving in specialized domains
Cost-sensitive AI products for startups, educational institutions, and developers
Key characteristics
Nearly matches o1-preview’s performance on STEM benchmarks at a fraction of the cost
Approximately 80% cheaper and faster than o1-preview, enabling cost-effective large-scale use
Generates long chains of thought for stepwise reasoning, enhancing accuracy on complex tasks
Less proficient in broad world knowledge tasks compared to o1-preview
Improved jailbreak robustness and alignment compared to earlier models
Model architecture
o1-mini is built on a transformer-based framework optimized for efficient reasoning via extensive chain-of-thought generation. It benefits from reinforcement learning with human feedback tuned for STEM domains, trading some breadth of general knowledge for stronger technical accuracy and lower inference costs.
Why choose 1RPC.ai for o1-mini
Every call is directly tied to the exact model and version used, ensuring traceability and trust in your outputs
Execution runs inside hardware-backed enclaves, so the relay can’t access or log your request
Connect to multiple AI providers through a single API
Avoid provider lock-in with simple, pay-per-prompt pricing
Privacy by design with our zero-tracking infrastructure that eliminates metadata leakage and protects your activity
Summary
o1-mini is a powerful, affordable AI model specialized in scientific and coding reasoning, delivering near top-tier accuracy with significantly reduced costs and latency. It’s ideal for applications demanding precise problem solving in STEM fields without extensive general knowledge, providing developers with scalable, reliable AI reasoning at a competitive price point.
Perfect for users who want cutting-edge technical AI with efficient deployment and strong domain focus.
o1-mini
o1-mini is the compact, faster, and 80% cheaper variant of OpenAI’s o1-preview, launched on September 12, 2024. Trained using the same high-compute reinforcement learning pipeline as o1, it delivers nearly comparable results on challenging benchmarks such as the American Invitational Mathematics Examination (AIME) and Codeforces coding competitions, making it a strong choice for STEM-focused AI applications that require cost-effective, fast reasoning.
While o1-mini excels in technical domains, it offers less breadth of general world knowledge than its larger sibling, catering primarily to precise problem solving in scientific and programming domains.
What it’s optimized for
o1-mini is designed for:
High-quality reasoning in STEM areas like mathematics, physics, chemistry, and coding
Rapid, cost-effective inference for real-time or large-scale deployment
Handling multi-step workflows requiring reliable chain-of-thought reasoning
Applications where broad world knowledge is less critical but strong domain expertise is essential
Typical use cases
o1-mini performs well in:
Automated STEM tutoring and educational tools focusing on math and science
Complex code generation, debugging, and competitive programming support
Scientific research assistance, including data analysis and mathematical derivations
Technical question answering and multi-step problem solving in specialized domains
Cost-sensitive AI products for startups, educational institutions, and developers
Key characteristics
Nearly matches o1-preview’s performance on STEM benchmarks at a fraction of the cost
Approximately 80% cheaper and faster than o1-preview, enabling cost-effective large-scale use
Generates long chains of thought for stepwise reasoning, enhancing accuracy on complex tasks
Less proficient in broad world knowledge tasks compared to o1-preview
Improved jailbreak robustness and alignment compared to earlier models
Model architecture
o1-mini is built on a transformer-based framework optimized for efficient reasoning via extensive chain-of-thought generation. It benefits from reinforcement learning with human feedback tuned for STEM domains, trading some breadth of general knowledge for stronger technical accuracy and lower inference costs.
Why choose 1RPC.ai for o1-mini
Every call is directly tied to the exact model and version used, ensuring traceability and trust in your outputs
Execution runs inside hardware-backed enclaves, so the relay can’t access or log your request
Connect to multiple AI providers through a single API
Avoid provider lock-in with simple, pay-per-prompt pricing
Privacy by design with our zero-tracking infrastructure that eliminates metadata leakage and protects your activity
Summary
o1-mini is a powerful, affordable AI model specialized in scientific and coding reasoning, delivering near top-tier accuracy with significantly reduced costs and latency. It’s ideal for applications demanding precise problem solving in STEM fields without extensive general knowledge, providing developers with scalable, reliable AI reasoning at a competitive price point.
Perfect for users who want cutting-edge technical AI with efficient deployment and strong domain focus.
o1-mini
o1-mini is the compact, faster, and 80% cheaper variant of OpenAI’s o1-preview, launched on September 12, 2024. Trained using the same high-compute reinforcement learning pipeline as o1, it delivers nearly comparable results on challenging benchmarks such as the American Invitational Mathematics Examination (AIME) and Codeforces coding competitions, making it a strong choice for STEM-focused AI applications that require cost-effective, fast reasoning.
While o1-mini excels in technical domains, it offers less breadth of general world knowledge than its larger sibling, catering primarily to precise problem solving in scientific and programming domains.
What it’s optimized for
o1-mini is designed for:
High-quality reasoning in STEM areas like mathematics, physics, chemistry, and coding
Rapid, cost-effective inference for real-time or large-scale deployment
Handling multi-step workflows requiring reliable chain-of-thought reasoning
Applications where broad world knowledge is less critical but strong domain expertise is essential
Typical use cases
o1-mini performs well in:
Automated STEM tutoring and educational tools focusing on math and science
Complex code generation, debugging, and competitive programming support
Scientific research assistance, including data analysis and mathematical derivations
Technical question answering and multi-step problem solving in specialized domains
Cost-sensitive AI products for startups, educational institutions, and developers
Key characteristics
Nearly matches o1-preview’s performance on STEM benchmarks at a fraction of the cost
Approximately 80% cheaper and faster than o1-preview, enabling cost-effective large-scale use
Generates long chains of thought for stepwise reasoning, enhancing accuracy on complex tasks
Less proficient in broad world knowledge tasks compared to o1-preview
Improved jailbreak robustness and alignment compared to earlier models
Model architecture
o1-mini is built on a transformer-based framework optimized for efficient reasoning via extensive chain-of-thought generation. It benefits from reinforcement learning with human feedback tuned for STEM domains, trading some breadth of general knowledge for stronger technical accuracy and lower inference costs.
Why choose 1RPC.ai for o1-mini
Every call is directly tied to the exact model and version used, ensuring traceability and trust in your outputs
Execution runs inside hardware-backed enclaves, so the relay can’t access or log your request
Connect to multiple AI providers through a single API
Avoid provider lock-in with simple, pay-per-prompt pricing
Privacy by design with our zero-tracking infrastructure that eliminates metadata leakage and protects your activity
Summary
o1-mini is a powerful, affordable AI model specialized in scientific and coding reasoning, delivering near top-tier accuracy with significantly reduced costs and latency. It’s ideal for applications demanding precise problem solving in STEM fields without extensive general knowledge, providing developers with scalable, reliable AI reasoning at a competitive price point.
Perfect for users who want cutting-edge technical AI with efficient deployment and strong domain focus.
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Get started with an API-friendly relay
Send your first request to verified LLMs with a single code snippet.
import requests
import json
response = requests.post(
url="https://1rpc.ai/v1/chat/completions",
headers={
"Authorization": "Bearer <1RPC_AI_API_KEY>",
"Content-type": "application/json",
},
data=json.dumps ({
"model": "o1-mini",
"messages": [
{
"role": "user",
"content": "What is the meaning of life?"
}
]
})
)Copy and go
Copied!
import requests
import json
response = requests.post(
url="https://1rpc.ai/v1/chat/completions",
headers={
"Authorization": "Bearer <1RPC_AI_API_KEY>",
"Content-type": "application/json",
},
data=json.dumps ({
"model": "o1-mini",
"messages": [
{
"role": "user",
"content": "What is the meaning of life?"
}
]
})
)Copy and go
Copied!
Pricing
Pricing
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