What ChatGPT o3-pro really does when it “reasons”
- Graziano Stefanelli
- Jun 17
- 5 min read
As OpenAI rolls out its most powerful model yet—o3-pro—you might hear people claim it can “reason” or “think through problems.”
But what does that actually mean? And is it really true? Let’s break it down.
❓ Is o3-pro Actually Thinking?
o3-pro doesn’t think like a human. It doesn’t form ideas, make decisions, or understand things the way we do. When it solves problems, explains code, or walks through a math equation, it’s really predicting what text should come next—step by step—based on the billions of examples it absorbed during training. In other words, it follows patterns it has already seen.
But... o3-pro takes noticeably longer to answer than standard o3. While o3 is designed for speed and usually replies within seconds, o3-pro may take one to three minutes for a typical prompt.
Deeper processing is the reason for the extra time. o3-pro examines more possibilities, checks its own work more carefully, and often runs extra tools or data lookups during its answer process. This slower pace brings a quality boost. You’ll usually get more detailed, accurate, and reliable answers from o3-pro, especially for complex or multi-step questions
🧱 The (partial) illusion of Step-by-Step Thinking
What makes models like o3-pro look smart is the way they break answers into steps—often called “chain-of-thought reasoning.” For example, to solve a math problem it might say:
Identify the given numbers.
Apply multiplication.
Add the results.
That breakdown sounds thoughtful, but it’s not true reasoning. o3-pro is simply mirroring how people typically explain solutions, without actually understanding why those steps matter.
But Why Is This Still So Useful?
Even if o3-pro isn’t really “thinking,” this step-by-step style is extremely practical for real people. When o3-pro spells out each part of its answer, it’s almost like having a helpful tutor who walks you through a problem instead of just giving you the solution. You can easily follow along, check each step, and see where things might go wrong.
This approach also makes it easier to learn new things. If you’re not sure how to solve a certain type of problem, o3-pro can break it down into small, understandable parts, making even tricky tasks much more approachable. For bigger jobs—like writing reports or planning projects—it can organize the process into clear, bite-sized actions, so nothing feels overwhelming.
So... even if the model doesn’t “understand” the way people do, its ability to lay out each step clearly turns complex problems into simple, manageable pieces. This makes it much easier for anyone to review, learn, and build on the answers, turning AI into a genuinely helpful partner.
🌐 Where o3-pro Shines in Real Workflows
O3-pro is extremely useful in practice:
Task | Why o3-pro Helps |
Code debugging | Predicts likely bug explanations and suggests fixes drawn from millions of code examples. |
Complex document drafting | Produces well-structured reports, policies, and business plans in minutes. |
Math & logic puzzles | Runs rapid “trial-and-error” behind the scenes to hit correct answers. |
Research summarization | Compresses dense studies into concise notes with citations pulled from its training set. |
🎯 Prompt Engineering Tricks to Unlock Better Results
Using the right prompt can make o3-pro feel far more capable. Try these quick tactics:
Show your work – Ask it to explain each step (“Walk through your reasoning step by step”).
Provide examples – Feed a short pattern of how you want the answer formatted.
Set the persona – “Act as a senior financial analyst” narrows its style and jargon.
Tighten scope – Give specific constraints (“Limit the explanation to IFRS references only”).
Ask for verification – “Double-check accuracy against authoritative sources.” This nudges the model to cross-reference what it knows.
⚖️ o3-pro vs. Other Cutting-Edge Models
Feature | o3-pro | Gemini Ultra | Claude Opus 4 |
Max context | 256 k*¹ | 1 M tokens | 200 k tokens |
Speed | Slowest | Moderate | Fastest |
Best at | Multi-step reasoning simulations | Multilingual content | Summaries & long-form context |
Pricing (API) | $20 / M in | $15 / M in | $12 / M in |
*¹ 256 k currently limited to enterprise & research partners.
Below is the same comparison table, followed by a short, plain-English walkthrough that puts each metric in context so it’s clear why these numbers and labels matter.
Feature | o3-pro | Gemini Ultra | Claude Opus 4 |
Max context window | 256 k tokens ¹ | 1 M tokens | 200 k tokens |
Typical speed | Slowest | Moderate | Fastest |
Shines at | Multi-step reasoning simulations | Multilingual content | Summaries & long-form context |
API pricing (USD) | $20 / M input tokens | $15 / M | $12 / M |
¹ 256 k currently limited to enterprise & research partners.
How to read this table
Max context window
Think of the context window as the model’s “short-term memory.” A 256 k window means o3-pro can read roughly 500 pages of text at once before it starts forgetting earlier parts. Gemini Ultra’s 1 M window is huge—handy if you need the model to digest an entire codebase or multiple books in one prompt. Claude Opus 4 sits in the middle, still large enough for most long reports.
Speed
o3-pro intentionally spends extra compute time running deeper “deliberation” passes and tool calls, so it’s the slowpoke of the group. Claude Opus 4 is engineered for snappy replies, while Gemini Ultra falls somewhere between the two.
Strengths
o3-pro excels when you want step-by-step logic—complex financial modeling, intricate code fixes, or multi-stage business analyses.
Gemini Ultra is your go-to for multilingual drafting or translations because it was trained with heavy emphasis on non-English corpora.
Claude Opus 4 is known for highly readable, concise summaries and can handle large documents fast without losing coherence.
API pricing
Prices reflect how much raw compute each model needs. o3-pro’s deeper reasoning path and tool access command the highest rate, while Claude Opus 4’s lighter-weight architecture makes it cheapest per million tokens.
Which model to pick?
Speed matters most? — Claude Opus 4
Need a truly massive prompt? — Gemini Ultra
Care about rigorous, multi-step reasoning? — o3-pro (worth the wait and cost)
In practice, many teams use more than one model: a faster, cheaper option for day-to-day drafts, and o3-pro for the moments when absolute accuracy and deep reasoning are non-negotiable.
🔭 What Comes After o3-pro? Hybrid Reasoning Approaches
Researchers are actively experimenting with neuro-symbolic hybrids that blend pattern-matching LLMs with rule-based logic engines. Early prototypes can:
Call external calculators when math precision is critical.
Query real-time databases instead of guessing the latest facts.
Apply symbolic logic to verify that each step truly follows from the previous one.
These hybrids may offer genuine leaps beyond “statistical guessing,” making future AI less of a parrot and more of a logic partner.
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