✨ Quick‑fire takeaway (the “TL;DR in 💫 30 sec”): o3 vs o3 Pro

Aspect	OpenAI o3	OpenAI o3‑pro
Purpose	General‑purpose reasoning model	Same core weights, but runs much deeper inference passes for maximum reliability
Speed	≈ 1 min for long answers	Noticeably slower (thinks longer)
Token price (API)	$2 in / $8 out per M tokens after 80 % price cut	$20 in / $80 out per M tokens
Context window	200 k tokens input / 100 k output	Same 200 k / 100 k window (variant of o3)
Multimodal tools	Web search, Python, file & image analysis, etc.	Same tool set, but no image generation and “temporary chats”/Canvas currently disabled
Benchmark wins	Sets new SOTA on Codeforces, SWE‑bench, MMMU, etc.	64 % human‑preference win‑rate over o3 and beats Gemini 2.5 Pro, Claude Opus on key STEM tasks
Best for	High‑volume reasoning workloads where cost & speed matter	Mission‑critical questions where accuracy > speed / cost

1. What actually changed under the hood?

• Same family, extra “deliberation” time.  o3‑pro re‑uses the o3 weights but allocates 10× more compute steps (OpenAI hints at majority‑vote style ensembles) to squeeze out errors.  
• Think of o3‑pro as the honors‑student version: it rereads the problem, runs more internal scratch‑work, and only then speaks—hence the added latency you feel in ChatGPT.  

2. Performance & reliability

• Human testers pick o3‑pro 64 % of the time over o3 for clarity, completeness and factual accuracy.  
• In‑house evals show o3‑pro overtaking Google Gemini 2.5 Pro on AIME‑2024 math and Anthropic Claude 4 Opus on GPQA‑Diamond science.  
• Both models inherit the giant 200 k‑token context window, perfect for book‑length prompts or multi‑file analysis.  

3. Cost, speed & rate‑limits

Model	Typical latency	API unit cost*	Relative cost
o3	~40‑70 s	$0.002 / $0.008 per k tokens	1× (baseline)
o3‑pro	~60‑120 s	$0.020 / $0.080 per k tokens	10×

*API pricing; ChatGPT Plus/Pro subscription uses the same models but hides marginal token fees.

Rule of thumb:

“If you’ll run thousands of calls per day, stay with o3 or o4‑mini. When the answer must be right the first time—crank the dial to o3‑pro.”

4. Tool access & multimodality

• Both models can seamlessly chain web search → Python → file or image analysis → formatted answer.  
• o3‑pro currently cannot generate images (DALLE‑style) and temporarily lacks “Canvas” workspaces, though it still reasons about images you upload.  
• Vision reasoning quality is identical where supported, because the underlying weights are the same.

5. When should 

you

 reach for each model?

Use case	Pick o3	Pick o3‑pro
Rapid brainstorming, code stubs, day‑to‑day chat	✅
Long reports where some slips are acceptable	✅
Academic math proofs, complex legal reasoning, scientific data review		✅
Critical business decisions, published content that must be rock‑solid		✅
Very large‑batch processing (cost sensitive)	✅
Anything requiring image creation	(use GPT‑4o / DALLE)	(not supported)

6. How to get access

• ChatGPT tiers:
  
  • Plus users see o3 by default.
  • Pro and Team users now see o3‑pro (replaces o1‑pro). Enterprise & Edu gain it next week.  
• API: supply model name o3 or o3-pro. Remember to budget for the 10× price multiplier before flipping the switch.

🚀 Pro‑tips for happier prompting

1. Give it context! Both models shine when you paste the background, goal, constraints, and success criteria up front.
2. Let it think: For o3‑pro, ask it to “explain your chain‑of‑thought briefly”—you’ll see where the extra compute goes.
3. Iterate: If latency bothers you, prototype with o3, then rerun the final refined prompt on o3‑pro for the publish‑ready answer.
4. Stream outputs: In the API, enable stream=true; you’ll start reading while the model is still elaborating.

🌟 Bottom line

o3 is your turbo‑charged daily driver; o3‑pro is the precision‑engineered supercar you pull out for the big race.

Keep creating, keep questioning, and let these reasoning rockets lift your ideas sky‑high!

Stay bold and keep innovating! 🎉