OpenAI o1-preview

Model Overview

Property	Value
Model ID	`openai/o1-preview`
Full Name	OpenAI: o1-preview
Provider	OpenAI
Release Date	September 12, 2024
Type	Reasoning Language Model (LLM)
Architecture	Transformer-based with Chain-of-Thought Reasoning

Description

OpenAI o1-preview is a reasoning-focused model designed to "spend more time thinking before responding." It employs chain-of-thought reasoning with self-fact-checking capabilities, making it particularly powerful for complex problem-solving tasks.

Key characteristics:

Extended Reasoning: Uses internal reasoning tokens to think through problems step-by-step
PhD-Level Performance: Surpassed human PhD-level performance on the GPQA diamond benchmark
Math Olympiad: Achieved 83% accuracy on the International Mathematics Olympiad qualifying exam
Competitive Programming: 89th percentile performance on Codeforces
STEM Optimization: Specifically designed for math, science, programming, and other STEM-related tasks

Note: This model is currently experimental and not recommended for production use-cases. It may be subject to heavy rate limiting.

Technical Specifications

Context & Token Limits

Parameter	Value
Context Length	128,000 tokens
Max Completion Tokens	32,768 tokens
Training Data Cutoff	October 1, 2023
Version	o1-preview-2024-09-12

Input/Output Modalities

Modality	Support
Text Input	Yes
Image Input	No (beta limitation)
File Input	No
Text Output	Yes
Image Output	No
Audio Output	No

Reasoning Tokens

The o1-preview model uses hidden "reasoning tokens" internally to process complex problems. These tokens:

Are consumed from your context window
Are billed at the same rate as output tokens
Are not visible in the API response
OpenAI recommends reserving at least 25,000 tokens for reasoning and outputs

Pricing

Standard Pricing (Per Token)

Type	Cost per Token	Cost per Million Tokens
Input	$0.000015	$15.00
Output	$0.00006	$60.00
Reasoning Tokens	$0.00006	$60.00 (same as output)

Cost Comparison

Model	Input (per 1M)	Output (per 1M)
o1-preview	$15.00	$60.00
GPT-4o	$2.50	$10.00
GPT-4o Mini	$0.15	$0.60

Note: o1-preview is significantly more expensive than standard models due to its advanced reasoning capabilities.

Capabilities

Core Capabilities

Advanced Reasoning: Complex logical reasoning through chain-of-thought
Mathematical Problem-Solving: Olympiad-level math capabilities
Scientific Analysis: PhD-level performance on physics, chemistry, biology
Code Generation: Expert-level code analysis and generation
Self-Verification: Built-in fact-checking during reasoning

Use Cases

Advanced scientific and mathematical problem-solving
Expert-level code analysis and debugging
Critical domain reasoning (biomedicine, law, finance)
Transparent chain-of-thought applications
High-stakes decision support systems
Research and complex analysis tasks

Limitations

No vision/image processing capability
No tool calling or function use
No fine-tuning available
No streaming support
Higher latency due to reasoning process
Significantly higher cost than standard models

Supported Parameters

Available Parameters (Beta)

Parameter	Type	Description
`model`	string	Model identifier (`o1-preview`)
`messages`	array	User and assistant messages only
`max_tokens`	integer	Maximum tokens to generate (up to 32,768)
`max_completion_tokens`	integer	Alternative to max_tokens

Fixed Parameters (Cannot Be Modified)

Parameter	Fixed Value	Notes
`temperature`	1	Cannot be adjusted during beta
`top_p`	1	Cannot be adjusted during beta
`n`	1	Only single completions supported
`presence_penalty`	0	Cannot be adjusted during beta
`frequency_penalty`	0	Cannot be adjusted during beta

Not Supported (Beta Limitations)

Feature	Status
System Messages	Not supported
Streaming	Not supported
Tool/Function Calling	Not supported
Image Inputs	Not supported
Logprobs	Not supported
Stop Sequences	Not supported
Web Search	Not supported

Best Practices

When to Use o1-preview

Complex multi-step mathematical problems
Scientific reasoning requiring domain expertise
Advanced coding challenges (algorithms, system design)
Tasks requiring careful logical analysis
Problems where accuracy is more important than speed
Research and academic problem-solving

When to Consider Alternatives

Simple tasks (use GPT-4o Mini for cost savings)
Tasks requiring images/vision (use GPT-4o)
Tasks requiring tool/function calling (use GPT-4o)
Real-time applications requiring low latency (use GPT-4o Mini)
Production workloads (o1-preview is experimental)

Optimization Tips

Reserve sufficient tokens: Leave at least 25,000 tokens for reasoning and output
Be detailed in prompts: The model benefits from comprehensive problem descriptions
Avoid system messages: They are not supported; include instructions in user message
Expect higher latency: The model takes longer to respond due to reasoning
Budget for costs: Output tokens (including hidden reasoning) are expensive
Handle errors gracefully: The model may be rate-limited

Prompt Engineering for o1

Since o1-preview cannot use system messages, structure your prompts differently:

# Instead of system message, include instructions in the user prompt
messages = [
    {
        "role": "user",
        "content": """You are an expert mathematician. Please solve the following
        problem step by step, showing all your work and explaining your reasoning.

        Problem: [Your problem here]

        Please provide a complete solution with explanation."""
    }
]

Model	Comparison
o1-mini	Faster, cheaper, smaller context; good for coding tasks
o1	Full version with 200K context (if available)
GPT-4o	General purpose, multimodal, supports tools and vision
Claude 3.5 Sonnet	Anthropic competitor with different reasoning approach
Gemini 2.0 Flash Thinking	Google's reasoning model alternative

Provider Information

Primary Provider: OpenAI

Property	Value
Base URL	`https://api.langmart.ai/v1`
Data Training	Disabled by default
Prompt Retention	Yes (for abuse monitoring)
Status	Beta/Experimental

OpenRouter Access

Property	Value
OpenRouter Model ID	`openai/o1-preview`
Base URL	`https://api.langmart.ai/v1`

Usage Examples

Basic Chat Completion (OpenAI Direct)

curl https://api.langmart.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "o1-preview",
    "messages": [
      {
        "role": "user",
        "content": "Solve this problem step by step: If a train travels at 60 mph for 2 hours, then at 40 mph for 3 hours, what is the average speed for the entire journey?"
      }
    ],
    "max_completion_tokens": 5000
  }'

Via OpenRouter

curl https://api.langmart.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $LANGMART_API_KEY" \
  -H "HTTP-Referer: https://your-app.com" \
  -d '{
    "model": "openai/o1-preview",
    "messages": [
      {
        "role": "user",
        "content": "Prove that the square root of 2 is irrational."
      }
    ],
    "max_tokens": 10000
  }'

Python SDK Example

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="o1-preview",
    messages=[
        {
            "role": "user",
            "content": """
            A farmer has a 400-meter fence and wants to enclose a rectangular
            field next to a river (no fence needed on the river side).
            What dimensions maximize the enclosed area?
            """
        }
    ],
    max_completion_tokens=10000
)

print(response.choices[0].message.content)

Complex Coding Problem

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="o1-preview",
    messages=[
        {
            "role": "user",
            "content": """
            Implement a function that finds the longest increasing subsequence
            in an array of integers. Explain your approach and analyze the
            time and space complexity.
            """
        }
    ],
    max_completion_tokens=8000
)

print(response.choices[0].message.content)

Scientific Reasoning

from openai import OpenAI

client = OpenAI()

response = client.chat.completions.create(
    model="o1-preview",
    messages=[
        {
            "role": "user",
            "content": """
            Explain the mechanism by which CRISPR-Cas9 achieves gene editing,
            including the role of guide RNA, PAM sequences, and the repair
            mechanisms that follow DNA cleavage. Include potential off-target
            effects and current strategies to minimize them.
            """
        }
    ],
    max_completion_tokens=15000
)

print(response.choices[0].message.content)

OpenRouter Python Example

from openai import OpenAI

client = OpenAI(
    base_url="https://api.langmart.ai/v1",
    api_key="your-openrouter-key"
)

response = client.chat.completions.create(
    model="openai/o1-preview",
    messages=[
        {
            "role": "user",
            "content": "Derive the Euler-Lagrange equation from first principles."
        }
    ],
    max_tokens=10000,
    extra_headers={
        "HTTP-Referer": "https://your-app.com"
    }
)

print(response.choices[0].message.content)

Model Variants

Variant	Model ID	Description
o1-preview	`o1-preview`	Standard reasoning model (128K context)
o1-preview-2024-09-12	`o1-preview-2024-09-12`	Specific version snapshot
o1	`o1`	Full o1 model (200K context, when available)
o1-mini	`o1-mini`	Smaller, faster, cheaper reasoning model

Rate Limits

o1-preview is subject to stricter rate limits during its beta phase:

Tier	Approximate Limits
Standard	Heavily rate-limited
Usage-based	Increases with usage

Note: Specific limits may vary. Check OpenAI's documentation for current limits.

Error Handling

Common errors with o1-preview:

Error	Cause	Solution
`system_message_not_supported`	Using system role	Remove system messages
`streaming_not_supported`	Stream parameter set	Set stream=false
`rate_limit_exceeded`	Too many requests	Implement backoff
`context_length_exceeded`	Input too long	Reduce input size
`model_not_available`	Model unavailable	Try again later

Additional Resources

Last Updated: December 2024 Source: LangMart API, OpenAI Documentation, and third-party benchmarks

OpenAI o1-preview

OpenAI o1-preview

Model Overview

Description

Technical Specifications

Context & Token Limits

Input/Output Modalities

Reasoning Tokens

Pricing

Standard Pricing (Per Token)

Cost Comparison

Capabilities

Core Capabilities

Use Cases

Limitations

Supported Parameters

Available Parameters (Beta)

Fixed Parameters (Cannot Be Modified)

Not Supported (Beta Limitations)

Best Practices

When to Use o1-preview

When to Consider Alternatives

Optimization Tips

Prompt Engineering for o1

Related Models

Provider Information

Primary Provider: OpenAI

OpenRouter Access

Usage Examples

Basic Chat Completion (OpenAI Direct)

Via OpenRouter

Python SDK Example

Complex Coding Problem

Scientific Reasoning

OpenRouter Python Example

Model Variants

Rate Limits

Error Handling

Additional Resources