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Zhongwei Li
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references/models-guide.md
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references/models-guide.md
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# OpenAI Models Guide
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**Last Updated**: 2025-10-25
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This guide provides a comprehensive comparison of OpenAI's language models to help you choose the right model for your use case.
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---
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## GPT-5 Series (Released August 2025)
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### gpt-5
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**Status**: Latest flagship model
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**Best for**: Complex reasoning, advanced problem-solving, code generation
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**Key Features**:
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- Advanced reasoning capabilities
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- Unique parameters: `reasoning_effort`, `verbosity`
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- Best-in-class performance on complex tasks
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**Limitations**:
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- ❌ No `temperature` support
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- ❌ No `top_p` support
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- ❌ No `logprobs` support
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- ❌ CoT (Chain of Thought) does NOT persist between turns
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**When to use**:
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- Complex mathematical problems
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- Advanced code generation
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- Logic puzzles and reasoning tasks
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- Multi-step problem solving
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**Cost**: Highest pricing tier
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---
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### gpt-5-mini
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**Status**: Cost-effective GPT-5 variant
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**Best for**: Balanced performance and cost
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**Key Features**:
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- Same parameter support as gpt-5 (`reasoning_effort`, `verbosity`)
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- Better than GPT-4 Turbo performance
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- Significantly cheaper than gpt-5
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**When to use**:
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- Most production applications
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- When you need GPT-5 features but not maximum performance
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- High-volume use cases where cost matters
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**Cost**: Mid-tier pricing
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---
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### gpt-5-nano
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**Status**: Smallest GPT-5 variant
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**Best for**: Simple tasks, high-volume processing
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**Key Features**:
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- Fastest response times
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- Lowest cost in GPT-5 series
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- Still supports GPT-5 unique parameters
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**When to use**:
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- Simple text generation
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- High-volume batch processing
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- Real-time streaming applications
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- Cost-sensitive deployments
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**Cost**: Low-tier pricing
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---
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## GPT-4o Series
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### gpt-4o
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**Status**: Multimodal flagship (pre-GPT-5)
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**Best for**: Vision tasks, multimodal applications
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**Key Features**:
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- ✅ Vision support (image understanding)
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- ✅ Temperature control
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- ✅ Top-p sampling
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- ✅ Function calling
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- ✅ Structured outputs
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**Limitations**:
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- ❌ No `reasoning_effort` parameter
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- ❌ No `verbosity` parameter
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**When to use**:
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- Image understanding and analysis
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- OCR / text extraction from images
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- Visual question answering
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- When you need temperature/top_p control
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- Multimodal applications
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**Cost**: High-tier pricing (cheaper than gpt-5)
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---
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### gpt-4-turbo
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**Status**: Fast GPT-4 variant
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**Best for**: When you need GPT-4 speed
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**Key Features**:
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- Faster than base GPT-4
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- Full parameter support (temperature, top_p, logprobs)
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- Good balance of quality and speed
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**When to use**:
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- When GPT-4 quality is needed with faster responses
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- Legacy applications requiring specific parameters
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- When vision is not required
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**Cost**: Mid-tier pricing
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---
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## Comparison Table
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| Feature | GPT-5 | GPT-5-mini | GPT-5-nano | GPT-4o | GPT-4 Turbo |
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|---------|-------|------------|------------|--------|-------------|
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| **Reasoning** | Best | Excellent | Good | Excellent | Excellent |
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| **Speed** | Medium | Medium | Fastest | Medium | Fast |
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| **Cost** | Highest | Mid | Lowest | High | Mid |
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| **reasoning_effort** | ✅ | ✅ | ✅ | ❌ | ❌ |
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| **verbosity** | ✅ | ✅ | ✅ | ❌ | ❌ |
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| **temperature** | ❌ | ❌ | ❌ | ✅ | ✅ |
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| **top_p** | ❌ | ❌ | ❌ | ✅ | ✅ |
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| **Vision** | ❌ | ❌ | ❌ | ✅ | ❌ |
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| **Function calling** | ✅ | ✅ | ✅ | ✅ | ✅ |
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| **Structured outputs** | ✅ | ✅ | ✅ | ✅ | ✅ |
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| **Max output tokens** | 16,384 | 16,384 | 16,384 | 16,384 | 16,384 |
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---
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## Selection Guide
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### Use GPT-5 when:
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- ✅ You need the best reasoning performance
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- ✅ Complex mathematical or logical problems
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- ✅ Advanced code generation
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- ✅ Multi-step problem solving
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- ❌ Cost is not the primary concern
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### Use GPT-5-mini when:
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- ✅ You want GPT-5 features at lower cost
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- ✅ Production applications with high volume
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- ✅ Good reasoning performance is needed
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- ✅ Balance of quality and cost matters
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### Use GPT-5-nano when:
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- ✅ Simple text generation tasks
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- ✅ High-volume batch processing
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- ✅ Real-time streaming applications
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- ✅ Cost optimization is critical
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- ❌ Complex reasoning is not required
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### Use GPT-4o when:
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- ✅ Vision / image understanding is required
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- ✅ You need temperature/top_p control
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- ✅ Multimodal applications
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- ✅ OCR and visual analysis
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- ❌ Pure text tasks (use GPT-5 series)
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### Use GPT-4 Turbo when:
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- ✅ Legacy application compatibility
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- ✅ You need specific parameters not in GPT-5
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- ✅ Fast responses without vision
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- ❌ Not recommended for new applications (use GPT-5 or GPT-4o)
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---
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## Cost Optimization Strategies
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### 1. Model Cascading
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Start with cheaper models and escalate only when needed:
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```
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gpt-5-nano (try first) → gpt-5-mini → gpt-5 (if needed)
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```
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### 2. Task-Specific Model Selection
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- **Simple**: Use gpt-5-nano
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- **Medium complexity**: Use gpt-5-mini
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- **Complex reasoning**: Use gpt-5
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- **Vision tasks**: Use gpt-4o
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### 3. Hybrid Approach
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- Use embeddings (cheap) for retrieval
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- Use gpt-5-mini for generation
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- Use gpt-5 only for critical decisions
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### 4. Batch Processing
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- Use cheaper models for bulk operations
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- Reserve expensive models for user-facing requests
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---
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## Parameter Guide
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### GPT-5 Unique Parameters
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**reasoning_effort**: Controls reasoning depth
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- "minimal": Quick responses
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- "low": Basic reasoning
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- "medium": Balanced (default)
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- "high": Deep reasoning for complex problems
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**verbosity**: Controls output length
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- "low": Concise responses
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- "medium": Balanced detail (default)
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- "high": Verbose, detailed responses
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### GPT-4o/GPT-4 Turbo Parameters
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**temperature**: Controls randomness (0-2)
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- 0: Deterministic, focused
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- 1: Balanced creativity (default)
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- 2: Maximum creativity
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**top_p**: Nucleus sampling (0-1)
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- Lower values: More focused
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- Higher values: More diverse
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**logprobs**: Get token probabilities
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- Useful for debugging and analysis
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---
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## Common Patterns
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### Pattern 1: Automatic Model Selection
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```typescript
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function selectModel(taskComplexity: 'simple' | 'medium' | 'complex') {
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switch (taskComplexity) {
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case 'simple':
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return 'gpt-5-nano';
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case 'medium':
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return 'gpt-5-mini';
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case 'complex':
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return 'gpt-5';
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}
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}
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```
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### Pattern 2: Fallback Chain
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```typescript
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async function completionWithFallback(prompt: string) {
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const models = ['gpt-5-nano', 'gpt-5-mini', 'gpt-5'];
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for (const model of models) {
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try {
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const result = await openai.chat.completions.create({
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model,
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messages: [{ role: 'user', content: prompt }],
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});
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// Validate quality
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if (isGoodEnough(result)) {
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return result;
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}
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} catch (error) {
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continue;
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}
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}
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throw new Error('All models failed');
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}
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```
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### Pattern 3: Vision + Text Hybrid
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```typescript
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// Use gpt-4o for image analysis
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const imageAnalysis = await openai.chat.completions.create({
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model: 'gpt-4o',
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messages: [
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{
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role: 'user',
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content: [
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{ type: 'text', text: 'Describe this image' },
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{ type: 'image_url', image_url: { url: imageUrl } },
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],
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},
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],
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});
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// Use gpt-5 for reasoning based on analysis
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const reasoning = await openai.chat.completions.create({
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model: 'gpt-5',
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messages: [
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{ role: 'system', content: `Image analysis: ${imageAnalysis.choices[0].message.content}` },
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{ role: 'user', content: 'What does this imply about...' },
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],
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});
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```
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---
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## Official Documentation
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- **GPT-5 Guide**: https://platform.openai.com/docs/guides/latest-model
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- **Model Pricing**: https://openai.com/pricing
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- **Model Comparison**: https://platform.openai.com/docs/models
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---
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**Summary**: Choose the right model based on your specific needs. GPT-5 series for reasoning, GPT-4o for vision, and optimize costs by selecting the smallest model that meets your requirements.
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