OpenAI: o1-mini

OpenAI O1 is an advanced AI model developed by OpenAI. It excels in reasoning and provides accurate answers to complex questions. Trained on diverse data, it focuses on STEM tasks like math and programming. With a context length of 128k tokens, it can handle intricate queries efficiently. This model is designed for users seeking high-quality responses in technical fields.

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "openai/o1-mini",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="openai/o1-mini",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

OpenAI: o1-mini

Context128000

Input$3 / M

Output$12 / M

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Description

OpenAI launched the o1-mini model on September 12, 2024. This model is built for advanced reasoning tasks, especially in STEM areas. It is particularly good at solving tough math problems and coding challenges. For example, it scored 70% on the American Invitational Mathematics Examination (AIME), placing it among the top students.

The o1-mini model is designed for high performance. It has a 128k context window, which allows it to manage a lot of information at once. On coding platforms, it achieved an elo rating of 1650 on Codeforces. This rating puts it in the 86th percentile of programmers, showing its strong skills in technical tasks.

This model uses a chain of thought reasoning method. This method helps it break down complex problems into smaller steps. As a result, it can provide higher accuracy and reliability. In tests, o1-mini even outperformed its larger version, o1-preview, in coding tasks.

It's important to know that continuous improvements are in progress. The model has received reinforcement learning training, focusing on math and science reasoning. Users have noticed that o1-mini can quickly provide accurate answers while being cost-effective.

In summary, the o1-mini model is notable for its advanced reasoning skills and strong performance metrics. It is a great tool for anyone needing help with STEM questions and coding challenges. Use our AIAPILAB service to integrate this model and gain access to even more favorable pricing!

Model API Use Case

The OpenAI o1-mini API is very useful. It helps with reasoning tasks in STEM fields. For example, it scored 70% on the American Invitational Mathematics Examination (AIME). This places it among the top 500 high school students in the U.S. Students and educators can benefit a lot from this model. It provides personalized tutoring in math and coding.

In software development, o1-mini is great at generating code. It also helps with debugging. It has an Elo rating of 1650 on Codeforces. This ranks it in the 86th percentile of programmers. This performance makes it a helpful tool for developers. They can use it to improve algorithms and automate testing.

Researchers can use o1-mini for data analysis. It helps with generating hypotheses and scientific reasoning. Its ability to understand complex data sets is impressive. It can find patterns that boost research efficiency. Plus, it can handle large amounts of information with a context length of 128k tokens. This makes it perfect for solving complicated problems.

Overall, the o1-mini API is a flexible tool. It offers strong AI support in math, coding, and scientific research. This makes advanced AI more available to many people.

Model Review

Pros

1. The o1-mini model tackles complex math problems with ease, showcasing remarkable skills. 2. It excels in coding tasks, achieving high ratings on competitive platforms like Codeforces. 3. The model employs chain of thought reasoning, simplifying intricate problems into manageable steps. 4. Users find it quick and reliable, consistently delivering accurate solutions in STEM fields. 5. Continuous enhancements boost its performance, ensuring it remains a powerful tool for users.

Cons

1. o1-mini struggles with non-STEM topics. It lacks depth in general knowledge areas. 2. The model sometimes generates verbose responses. Users may find it overwhelming to sift through. 3. Latency issues arise during complex queries. Users may experience frustrating delays in responses.

Comparison

Feature/Aspect	GPT-4	OpenAI O1	OpenAI O1 Mini
Context Length	Supports up to 64k tokens	Supports up to 128k tokens	Supports up to 128k tokens
Safety Features	Standard safety features but less focus on advanced reasoning safety	Enhanced safety measures with high robustness against jailbreaks	Similar safety features as O1, slightly lower robustness
Reasoning Capability	Strong reasoning but less focused on specialized tasks	Advanced reasoning with deep analysis for complex problems	Optimized for STEM tasks, excels in coding and math
Response Generation Speed	Generally faster than both O1 models	Slower due to extensive reasoning process	Faster than O1, suitable for quick responses
Performance in Competitions	Good performance but not specialized for coding	High performance in math competitions (AIME) and coding contests	Competitive in coding challenges, high ELO rating on Codeforces

API

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "openai/o1-mini",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="openai/o1-mini",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

FAQ

Q1: What is OpenAI o1-mini?  
A1: OpenAI o1-mini is a specialized AI model for reasoning tasks in STEM fields.

Q2: How does o1-mini differ from other models?  
A2: o1-mini excels in math, coding, and science, outperforming previous models in these areas.

Q3: Can o1-mini assist with coding questions?  
A3: Yes, o1-mini is adept at generating code snippets and debugging programming issues.

Q4: What types of tasks is o1-mini best suited for?  
A4: o1-mini shines in complex reasoning tasks, especially in mathematics and scientific inquiries.

Q5: How does o1-mini ensure safety during use?  
A5: OpenAI applies rigorous safety protocols and alignment techniques to enhance o1-mini's safety.