Lazy loaded imageOpenAI ChatGPT Progression

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It’s kind of messy to name different versions of models with incremental names…Since GPT-4, everything is getting complicated, here is a simplified explanation of these models:
  • GPT-4o (the o means omni, a multi-modal model, featuring processing different modalities)
  • The mini: smaller and faster models from the distillation of larger models
    • GPT-4o-mini
    • o1-mini
    • o3-mini
  • The nano: double distillations
  • The pro: the full-fledged version
    • o1-pro
  • The o: reasoning models
    • o1, o1-mini
    • o3, o3-mini
 
Below is a full comparison in terms of Web Search, Python Interpreter, Reasoning, Image Analysis/Understanding, Image Generation, and File Interpretation.
Model
Time
Web Search
Python
Reasoning
Image Analysis
Image Generation
File Interpretation
Features
GPT-3.5
Nov., 2022
❌
❌
❌
❌
❌
❌
Chat
GPT-4
Mar. 14, 2023
❌
✅
❌
✅
✅
✅
Larger Model Scales
GPT-4o
May. 13, 2024
✅
✅
❌
✅
✅
✅
Multi-modal
GPT-4o-mini
Jul. 18, 2024
✅
✅
❌
✅
✅
✅
Multi-modal
o1-mini
Sep. 12, 2024
❌
✅
✅
✅
✅
✅
Reasoning
o1-preview
Sep. 12, 2024
❌
✅
✅
✅
✅
✅
Reasoning
o1
Dec. 5, 2024
❌
✅
✅
✅
❌
✅
Reasoning
o3-mini
Jan. 31, 2025
✅
✅
✅
✅
❌
✅
Advanced Reasoning
o3-mini-high
Jan. 31, 2025
✅
✅
✅
✅
❌
✅
Advanced Reasoning
GPT-4.5 Preview
Feb. 27, 2025
✅
✅
✅
✅
❌
✅
Research Preview
o1-pro
Mar. 17, 2025
❌
❌
✅
✅
❌
❌
full-fledged
o3
Apr. 16, 2025
✅
✅
✅
✅
✅
✅
Multimodal Reasoning
o4-mini
Apr. 16, 2025
✅
✅
✅
✅
✅
✅
Multimodal Reasoning
o4-mini-high
Apr. 16, 2025
✅
✅
✅
✅
✅
✅
Multimodal Reasoning
 
There is an a bit outdated but more comprehensive comparisons between OpenAI’s models from Peter Gostev
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Lazy loaded image 5.1 Prompt Engineering
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Lazy loaded image 6.1 GPU Usage Analysis
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Lazy loaded image 6.6 PEFT
🌾 LLM Compression
Lazy loaded image 7.1 Quantization
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Lazy loaded image 7.3 Knowledge Distillation
🌵 LLM Hands-on Practice
Lazy loaded image 8.1 nanoGPT: Training a GPT from Scratch
Lazy loaded image 8.2 Instruction Fine-Tuning Qwen3-0.6B: A Minimal Working Example
Lazy loaded image 8.3 OpenRLHF: SFT Qwen3-0.6B and Code Analysis
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Lazy loaded image 9.1 Technical Reports Explained
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Lazy loaded image 9.3 o1 Techinical Paper
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🌳 LLM Q&A
Lazy loaded image 10.1 Why GRPO training loss starts at 0
Lazy loaded image 10.2 VLM-R1 Reproduction Results
Lazy loaded image 10.3 KL(P||Q) or KL(Q||P)?
Lazy loaded image 10.4 Left Padding or Right Padding?
Lazy loaded image 10.5 What is QK-Norm?
Lazy loaded image 10.6 Why LLM training rarely adopts Dropout?
Lazy loaded image 10.7 Dropout and DropPath
🐝 VLM Image Encoders
Lazy loaded image 11.0 Introduction
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