Here are some notes I made to understand each of these models and when to use them.
# OpenAI Models
## Reasoning Models (o-series)
- All `oX` (o-series aka `omni`) models are reasoning models.
- Use these for complex, multi-step, reasoning tasks.
## Flagship/Core Models
- All `x.x` and `Xo` models are the core models.
- Use these for one-shot results
- Examples: 4o, 4.1
## Cost Optimized
- All `-mini`, `-nano` are cheaper, faster models.
- Use these for high-volume, low effort tasks.
## Flagship vs Reasoning (o-series) Models
- Latest flagship model = 4.1
- Latest reasoning model = o3
- The flagship models are general purpose, typically with larger context windows. These rely mostly on pattern matching.
- The reasoning models are trained with extended chain-of-thought and reinforcement learning models. They work best with tools, code and other multi-step workflows. Because tools are used, the accuracy will be higher.
# List of Models
## 4o (omni)
- 128K context window
- complex multimodal, applications requiring the top level of reliability and nuance
## 4o-mini
- 128K context window
- Use: multimodal reasoning for math, coding, and structured outputs
- Use: Cheaper than `4o`. Use when you can trade off accuracy vs speed/cost.
- Dont Use: When high accuracy is needed
## 4.1
- 1M context window
- Use: For large context ingest, such as full codebases
- Use: For reliable instruction following, comprehension
- Dont Use: For high volume/faster tasks
## 4.1-mini
- 1M context window
- Use: For large context ingest
- Use: When a tradeoff can be made with accuracy vs speed
## 4.1-nano
- 1M context window
- Use: For high-volume, near-instant responses
- Dont Use: When accuracy is required
- Examples: classification, autocompletion, short-answers
## o3
- 200K context window
- Use: for the most challenging reasoning tasks in coding, STEM, and vision that demand deep chain‑of‑thought and tool use
- Use: Agentic workflows leveraging web search, Python execution, and image analysis in one coherent loop
- Dont Use: For simple tasks, where lighter model will be faster and cheaper.
## o4-mini
- 200K context window
- Use: High-volume needs where reasoning and cost should be balanced
- Use: For high throughput applications
- Dont Use: When accuracy is critical
## o4-mini-high
- 200K context window
- Use: When o4-mini results are not satisfactory, but before moving to o3.
- Use: Compex tool-driven reasoning, where o4-mini results are not satisfactory
- Dont Use: When accuracy is critical
## o1-pro-mode
- 200K context window
- Use: Highly specialized science, coding, or reasoning jobs that benefit from extra compute for consistency
- Dont Use: For simple tasks
## Models Sorted for Complex Coding Tasks (my opinion)
1. o3
2. Gemini 2.5 Pro
3. Claude 3.7
2. o1-pro-mode
3. o4-mini-high
4. 4.1
5. o4-mini