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