Reasoning

Overview

Who is this for? Developers building AI applications that require complex problem-solving, mathematical computations, coding challenges, or detailed analysis where seeing the model's reasoning process is valuable.

What you'll achieve: Enable AI models to show their step-by-step thinking process, perform complex reasoning tasks, and provide transparent explanations for their conclusions across multiple providers.

The AI Proxy supports advanced reasoning capabilities through specialized models and thinking configurations that expose the internal reasoning process of AI models.

Supported Models & Providers

OpenAI Reasoning Models

Anthropic Claude Reasoning

Google AI (Gemini) Reasoning

Basic Reasoning Usage

OpenAI Reasoning Models

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Anthropic Thinking Mode

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Google AI Thinking Configuration

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Advanced Reasoning Features

Configurable Thinking Budget

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Streaming Reasoning Process

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Multi-Step Problem Solving

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Implementation Examples

Node.js Reasoning Handler

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Python Reasoning Processor

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React Reasoning Component

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Response Format

OpenAI Reasoning Response

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Anthropic Thinking Response

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Google AI Thinking Response

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Use Cases

Mathematical Problem Solving

• Complex Calculations: Multi-step mathematical computations with shown work
• Proof Verification: Step-by-step mathematical and logical proofs
• Algorithm Design: Reasoning through algorithm creation and optimization
• Statistical Analysis: Detailed statistical reasoning and interpretation

Code Analysis & Development

• Debugging: Step-by-step analysis of code issues and solutions
• Code Review: Detailed reasoning about code quality and improvements
• Architecture Design: Reasoning through system design decisions
• Performance Optimization: Analysis of performance bottlenecks and solutions

Research & Analysis

• Literature Review: Systematic analysis of research papers and findings
• Data Analysis: Step-by-step data interpretation and insights
• Competitive Analysis: Detailed reasoning about market positioning
• Strategic Planning: Multi-factor decision making with transparent reasoning

Educational Applications

• Tutoring: Step-by-step explanations of complex concepts
• Problem Solving: Guided reasoning through academic problems
• Critical Thinking: Teaching logical reasoning and analysis
• Exam Preparation: Detailed explanations of solution methods

Provider-Specific Features

OpenAI (o1, o3 Series)

• Automatic Reasoning: Built-in reasoning without configuration
• Token Efficient: Optimized reasoning process
• Max Completion Tokens: Uses max_completion_tokens instead of max_tokens
• Temperature Fixed: Reasoning models have fixed temperature settings

Anthropic Claude

• Explicit Thinking: Controllable thinking process visibility
• Budget Management: Configurable thinking token allocation
• Streaming Thinking: Real-time reasoning process visibility
• Signature Verification: Cryptographic verification of thinking process

Google AI (Gemini)

• Thinking Configuration: Granular control over reasoning process
• Budget Tokens: Configurable thinking computation budget
• Multi-Modal Reasoning: Reasoning with images and text
• Long Context: Reasoning over large context windows

Best Practices

Reasoning Prompt Design

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Budget Management

• Conservative Budgets: Start with smaller thinking budgets (5,000-10,000 tokens)
• Cost Monitoring: Track reasoning token usage separately from completion tokens
• Task Complexity: Adjust budget based on problem complexity
• Provider Optimization: Different providers have different reasoning efficiencies

Response Handling

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Error Recovery

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Performance Considerations

Token Usage

• Reasoning Tokens: Separate from completion tokens, used for internal thinking
• Budget Planning: Reasoning can use significant token budgets
• Cost Impact: Reasoning models typically cost more per token
• Optimization: Use reasoning selectively for complex tasks

Latency Management

• Processing Time: Reasoning models take longer to process
• Streaming Benefits: Use streaming to show progress during reasoning
• Timeout Configuration: Set appropriate timeouts for complex reasoning tasks
• Fallback Strategy: Have non-reasoning fallbacks for time-sensitive applications

Troubleshooting

Common Issues

Budget Exceeded
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Reasoning Not Appearing
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Performance Issues

• High Latency: Reasoning models inherently take longer
• Token Costs: Monitor reasoning token usage vs. output quality
• Budget Tuning: Adjust thinking budgets based on task complexity
• Model Selection: Choose appropriate reasoning models for task difficulty

Next Steps

• Tool Calling: Combine reasoning with function calls
• Structured Outputs: Structure reasoning outputs
• Streaming: Stream reasoning processes in real-time