O1
Deep reasoning model for complex text tasks, excelling at logic and code.
A tradução deste card ainda não está disponível; parte do conteúdo aparece em inglês.
- Janela de contexto (tokens)
- 200.000
- Inteligência
- 15.2
- Código
- 39.7
- Velocidade
- —–
- Custo/tarefa
- $0.330
This model is built for users who need to tackle intricate problems that require step-by-step logical deduction. It is particularly strong in software development, mathematical reasoning, and strategic planning. Its distinctive feature is its ability to 'think' through problems internally before generating a response, leading to more accurate and nuanced answers.
Ideal for developers, researchers, and analysts, it shines when standard models fall short on complex prompts. Use it for debugging code, writing algorithms, or analyzing dense documents. The trade-off is that it prioritizes depth of reasoning over raw output speed, making it best suited for tasks where correctness is paramount.
- Debugging complex code
- Writing algorithms
- Analyzing dense documents
- Solving math problems
- Strategic planning
Excels at multi-step logical deduction and complex problem-solving.
Highly capable of understanding, generating, and debugging code across languages.
Provides precise and well-structured answers to intricate questions.
- Provide clear context and constraints
- Break down very large problems into smaller steps
- Use specific examples to guide the output format
- Review outputs for factual accuracy in niche domains
- Slower output speed compared to standard models
- Higher cost per token for complex tasks
- Not optimized for real-time conversational chat
Design an efficient algorithm to find the shortest path in a dynamic graph.
Provide a formal proof for the convergence of this series.
Draft a scalable system architecture for a real-time collaborative editing tool.
Limits choices to the most probable tokens summing to this probability mass for nucleus sampling.
Sets the maximum length of the generated response.
Controls randomness; lower values make output more deterministic, higher values more creative.
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What is this model best used for?
It is best for complex reasoning, coding, and analytical tasks that require deep thought.
How does it differ from faster models?
It spends more internal computation time reasoning, which results in higher quality but slower responses.
Is it suitable for casual conversation?
While it can converse, it is optimized for depth rather than speed, making it less ideal for quick chats.