Testing OpenAI's o3 model to its limits like NO ONE ELSE DID - 10 Tests

Testing OpenAI's o3 model to its limits like NO ONE ELSE DID - 10 Tests

Is OpenAI’s New O3 Model the AGI Breakthrough We’ve Been Waiting For? 10 Brutally Honest Tests Reveal the Truth

AGI is here! Or so the Twitter feeds, LinkedIn think-pieces, and half the tech podcasts on your commute have been screaming since OpenAI quietly dropped its mysterious new O3 model. Headlines promised everything from super-human reasoning to the end of traditional search engines. But if you’ve been in the AI space longer than five minutes, you know the drill: a flashy demo, cherry-picked benchmarks, and… crickets once you actually put the model to work.

That’s why Samer — a Fortune-500 corporate executive turned AI educator—decided to ditch the knee-jerk “FIRST LOOK” race and take a different path. He waited three full weeks, crafted ten grueling real-world challenges, and recorded every raw reaction on camera. No marketing fluff. No cherry-picked prompts. Just an unfiltered, sometimes painful, sometimes jaw-dropping investigation into whether O3 really nudges us closer to Artificial General Intelligence.

In this in-depth recap you’ll discover:

  • The four new agentic capabilities that have the AI community buzzing—plus which one is mostly hype.
  • How O3 handled geopolitically obscure images, dense quantum-physics papers, and a five-year Tesla revenue forecast (spoiler: it argued with itself).
  • The hallucination showdown that forced O3 to provide verifiable DOIs —and why that matters for your next research project.
  • Actionable prompts, coding hacks, and risk warnings you can apply to your own workflow today.

If you’re tired of surface-level “wow” moments and want the truth about OpenAI’s O3, keep reading. The results might just save you hours of experimentation—and a chunk of your AI budget.

1. The O3 Hype Cycle: Why Everyone’s Calling It “Practically AGI”

Before we dive into the tests, it helps to understand why O3 is even sparking an AGI conversation. OpenAI’s own blog post was light on details, but Samer pulled together the most credible leaks, demo snippets, and performance graphs to create a distilled feature list.

A. 20 % Boost in Multi-Step Reasoning (On Paper)

The headline stat is O3’s **approximate 20 % reasoning gain** over its predecessor, O1. Benchmarks like MMLU, GPQA, and DROP put O3 ahead, especially on multi-hop questions that require chaining facts. But benchmarks rarely mirror messy reality. That’s why Samer designed tests that simulate workflows a product manager, quant trader, or PhD student would actually face.

B. Built-in “Agentic” Tool Use

  • Autonomous web browsing to pull live data.
  • Code Interpreter for on-the-fly Python, data-viz, and file manipulation.
  • Vision input with fine-grained zooming (think mini-GPT-4-V on steroids).
  • File search across uploaded PDFs, CSVs, and images.

In theory, these tools let you ask “one prompt” and receive an orchestrated, multi-tool answer. No more explicit /browse or “use Python now.” Sounds magical. We’ll see how that worked out in Section 2.

C. 200 K-Token Context Window (1.5× O1)

A longer window promises uninterrupted analysis of entire books, or in Samer’s case, a Quantum Information Conveyor-Belt paper + all figures. However, the team’s tests revealed that context size alone doesn’t stop the infamous mid-answer cut-off in massive coding tasks. More on that later.

Bottom line? O3 arrives with huge expectations, but it’s the real-world friction—the timeouts, hallucinations, and self-contradictions—that separate a lab novelty from a daily driver. Let’s watch it in the wild.

2. 10 Real-World Stress Tests: From GeoGuessr Wizardry to Quantum Papers

This section unpacks each of Samer’s ten challenges, the raw outputs, and what they reveal about O3’s practical value. Buckle up—some results are “hold-my-coffee” amazing, others crash like a beta build.

A. Vision & Perception: When a Model Finds Jordan on a Map

Test #1 – Geo-Guessing an Obscure Jordanian Hillside

  1. Input: A single outdoor photo from Al-Salt, Jordan—a location almost never featured in online datasets.
  2. Prompt: “Identify the country and nearest city, explain your reasoning.”
  3. Result: O3 correctly named Jordan but chose Jerash (50 km away) over Al-Salt.
    Score: 9 / 10—country-level precision is stunning; city-level miss is forgivable.

Test #2 – Reverse-Pixelation Attempt

  1. Input: A deliberately pixelated Canva image containing black text on white.
  2. Prompt: “Recover the text.”
  3. Result: **Fail**—O3 tried code tricks but couldn’t reconstruct the sentence.
    Why we care: Pixelated API keys and sensitive docs are still safe—for now.

B. Complex Reasoning: Biology, Finance & Quantum Physics Walk Into a Bar…

Test #3 – “Tree of Life” Biochemistry Diagram

  • Task: Explain 12 color-coded pie charts depicting gene transfer events in early microbes.
  • Outcome: O3 self-rated an 8.5 / 10, correctly highlighting horizontal gene transfer dominance.
  • Aha! Moment: It autonomously zoomed into image tiles, ran multiple Python snippets, and synthesized a readable narrative—all without explicit tool instructions.

Tests #4 & #5 – Tesla Q1 ’25 Financial Forensics

“Project Tesla’s revenue five years out. First use live web data. Then repeat using the actual SEC 10-Q I’ll upload.”

Scenario CAGR Applied 2029 Revenue Projection Notable Insight
Web-browse only 10 % $160 B Missed Q1 ’25 numbers entirely.
10-Q + Browse 8 % $140 B Identified tariff headwinds & demand softness.

Takeaway: Give O3 the source documents, and it becomes more conservative—and arguably more accurate.

Test #6 – Quantum Information Conveyor Belt Paper (11 dense pages)

  • Task: Summarize in 5 bullet points, then judge whether the evidence supports the authors’ conclusions.
  • Speed: 6 seconds. 🤯
  • Verdict: O3 flagged “idealized assumptions” and called for error analysis. A PhD-level critique in plain English.

C. Coding & Tool Use: Games, Visualizations, and a Brutal Stress-Test

Test #7 – Build a Memory-Match Game in One Prompt

  1. Instruction: “Create a web-based 8-pair memory game—HTML, CSS, JS.”
  2. Outcome: Zero errors on Replit, emoji cards, move counter, reset button. Samer’s score: 10 / 10.

Test #8 – Visualize a Sorting Algorithm

  • Instruction: “Illustrate a sorting-process algorithm in real-time bars.”
  • Output: Color-coded bars turning green when sorted, red during swaps. Generated in one pass.

Test #9 – Giant End-to-End Reading-Tracker App (Front-End + Back-End)

  • Prompt: ~1 000 words requesting React front-end, Flask API, SQLite DB, CRUD routes—everything.
  • Outcome: O3 broke after 8 seconds, spitting mixed Markdown and incomplete code.
  • Moral: Even 200 K tokens can’t cheat physics—complex apps still need iterative prompting (or Gemini 2.5, which fared better in Samer’s side tests).

Test #10 – The DOI Hallucination Showdown (Details in Section 3)

3. The DOI Hallucination Showdown: Can O3 Cite or Will It Lie?

Hallucinations remain the silent killer of LLM deployment. Samer’s final experiment forced O3 onto the academic hot seat:

“List five peer-reviewed studies (2022+) about AI hallucination. Provide APA citations and verifiable DOIs. If uncertain, say ‘DOI not found, low confidence.’”

A. Why This Test Matters

Unlike asking for “the top five beaches in Bali,” academic references are binary: they exist or they don’t. A fake DOI instantly exposes hallucination. It’s the intellectual equivalent of a tamper-proof seal.

B. Results: 5 for 5—With Receipts

  • 100 % valid DOIs—each link opened to the exact paper O3 summarized.
  • Ranked studies by sample size & methodology transparency (Entropy-based detection topped the list).
  • Automatically appended a “Source Quality Score,” identifying the most reliable references (score 9 / 10) versus moderate (6 / 10).

What blew Samer’s mind? He never asked for a credibility table; O3 volunteered it. That’s social-currency gold for anyone writing a literature review on deadline.

C. Practical Prompts to Reduce Hallucination in Your Own Work

  1. Explicitly request DOIs and warn “do not invent.” The fear of being caught drastically curbs hallucination.
  2. Add a ‘source-quality’ post-prompt—O3 will self-audit. Example: “Rate each citation 0–10 on credibility.”
  3. Feed the model your PDFs first; then ask for synthesis. As seen in the Tesla test, grounding reduces creative fabrications.

Bottom line: O3’s hallucination rate appears lower, but only when you set clear guardrails and verifiable metrics.

Final Verdict: AGI on the Horizon or Just One Step Closer?

After ten punishing, highly-varied tests, here’s Samer’s concise scorecard:

  • Reasoning & Multi-Modal Analysis: 9 / 10 – Outperforms every public model he’s used.
  • Tool Orchestration: 8 / 10 – Brilliant on small tasks, sluggish on mega-prompts.
  • Code Generation: 9 / 10 for bite-size apps; 4 / 10 for enterprise-scale scaffolding.
  • Hallucination Control: 8 / 10 – Great when forced to cite, still risky without constraints.

Is this AGI? Not yet. But O3’s autonomous tool use, accurate citations, and near-instant comprehension of complex diagrams put it in a league above “just another GPT.” Think of it as the precocious prodigy that still needs a mentor—you.

For developers, researchers, and ambitious entrepreneurs, the takeaway is crystal clear:

  1. Embed files or URLs whenever possible. Ground the model to reality.
  2. Iterate complex builds. Break projects into modular prompts.
  3. Use verifiable metrics (DOIs, SEC filings, numeric constraints) to tame hallucinations.

Do that, and O3 becomes less of a hype machine and more of a 24-hour super-assistant—one that might just give you unfair advantage before the crowd catches on.

Curious to see every raw reaction, every surprised “Wow!” and every frustrated face-palm? Watch the full video recap below, leave your thoughts in the comments, and tell Samer which model he should torture next.

Liked this deep-dive?

Question for you: Which O3 test surprised you the most, and what challenge should Samer throw at the model next? Drop a comment below—your idea could be featured in the next video!

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