| 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
- Give it context! Both models shine when you paste the background, goal, constraints, and success criteria up front.
- Let it think: For o3‑pro, ask it to “explain your chain‑of‑thought briefly”—you’ll see where the extra compute goes.
- Iterate: If latency bothers you, prototype with o3, then rerun the final refined prompt on o3‑pro for the publish‑ready answer.
- 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! 🎉