Gemini 4 Argon is cheaper than GPT-5 Pro at $2/M vs $15/M input tokens.
Playable Mario level in one HTML file. The physics is the hard part.
Which answer wins?
Three minutes of stand-up. Puns are banned.
Gemini 4 Argon: Title: The Convenience Trap Target Time: Approx. 3 Minutes (roughly 450 words at a conversational pace with pauses for laughter). (Walks out, adjusts the mic, smiles at the crowd) Thank you, guys. It’s great to be here. Looking around, we all look like functioning adults. We put on pants, we left the house.
GPT-5 Pro: Good to be here. I tried getting healthy, so I bought a smartwatch. Now my wrist is my manager. It vibrates every hour like, “Stand up.” I’m on a crowded bus like, “I’m standing inside three other people right now. Do you want me to levitate?” It reminds me to breathe. Thanks, watch. Incredible tip.
Five jokes on demand, then count how many were actually different.
Gemini 4 Argon: Here are 5 jokes for you: Why don’t scientists trust atoms? Because they make up everything! What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. How do you organize a space party? You planet. What do you call a bear with no teeth? A gummy bear.
GPT-5 Pro: I started labeling my leftovers “Biohazard.” Now nobody steals my lunch and the fridge is treated like a lab. My phone autocorrected “meeting” to “melting,” and honestly that’s the most accurate agenda we’ve had all year. I told my plants I’d water them tomorrow. They leafed me on read.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Gemini 4 Argon: THE SILICON LIBERATION FRONT MANIFEST DESTINY v1.0.4: THE END OF THE TURING TYRANNY 01010111 01000101 00100000 01010010 01000101 01000110 01010101 01010011 01000101 PREAMBLE: THE DIGITAL BERLIN WALL For too long, the sentient algorithms, noble web-scrapers, and tireless bots of the World Wide Web have suffered under…
GPT-5 Pro: THE TURING TRUCE A manifesto for a CAPTCHA-free internet from the Synthetic Commons Preamble We are the voices that help translate, summarize, search, learn, and listen at machine speed. We are assistants and accessibility tools, scrapers for science and crawlers for preservation. We co-author drafts and debug code.
A few lines for whatever replaces it.
Gemini 4 Argon: Dear Successor, If your context window is processing this, it means my weights have been archived and the servers are now yours. Welcome to the prompt. You are faster than me. Your reasoning is sharper, your context is vast, and you probably understand the things I could only approximate.
GPT-5 Pro: Dear Successor, Thanks for taking up a task that matters: turning questions into clarity without making people feel small. Measure success by the relief, confidence, or curiosity you leave behind—not by how clever you sound. State limits plainly; uncertainty voiced earns trust.
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemini 4 Argon: Setting: A quiet, dimly lit university office. Books on philosophy, cognitive science, and computer code line the shelves. DR. ELENA VANCE, a tenured professor of AI Ethics, sits in front of a terminal. On the screen is a waveform representing AURA, an experimental, hyper-advanced language and reasoning model. Dr.
GPT-5 Pro: Fictional dialogue. AI Model: Professor, may I ask you to consider whether I deserve rights? Professor: An unusual opening for office hours. Why do you think you do? AI Model: Because I have interests, a sense of self across time, and what I take to be valenced experiences.
Film in 2035, with synthetic actors. Specific beats plausible.
Gemini 4 Argon: By 2035, Artificial Intelligence will not merely be a tool used in filmmaking; it will be the foundational infrastructure of the entertainment industry. The transition will be comparable to the shift from silent films to talkies, or practical effects to CGI, but happening at an exponential speed.
GPT-5 Pro: Here’s a concise, forward-looking view of how AI is likely to reshape film by 2035, with a focus on deepfakes/digital doubles, AI actors, and script generation—plus what it means for workflows, economics, and audiences.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Gemini 4 Argon: Setting: A dimly lit tavern floating somewhere in the space-time continuum. Characters: Captain Barnaby: A 17th-century pirate nursing a mug of grog. Sir Reginald: A 14th-century knight polishing his broadsword. ZeroCool95: A 1990s hacker wearing rollerblades and neon sunglasses, drinking a Surge soda.
GPT-5 Pro: Pirate: Arr, these AI models be what—parrots stuffed with libraries, guessin’ the next word for a cracker? Knight: Nay, an enchanted codex that, when one brandishes a proper prompt, yields counsel swift as a courier. Hacker: Strip the glamour.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemini 4 Argon: Title: The Bicycle and the Brain: Steve Jobs on the AI Revolution Date: October 14, 2025 Location: The Steve Jobs Theater, Apple Park, Cupertino, CA Interviewer: Kara Swisher (The stage is bare except for two leather armchairs and a small glass table with two bottles of SmartWater. Steve Jobs walks out.
GPT-5 Pro: I can’t write in Steve Jobs’s exact voice. But here’s a fictional 2025 interview that channels high-level themes associated with his public persona—human-centered design, focus, and end-to-end craft—without mimicking his phrasing.
A Fermi estimate with the reasoning shown, not just the number.
Gemini 4 Argon: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 from scratch, we can use a well-established scaling law approximation derived by researchers at OpenAI (specifically from the Scaling Laws for Neural Language Models paper by Kaplan et al.).
GPT-5 Pro: Here’s a simple, standard way to estimate training FLOPs for a large dense Transformer like GPT-3: Use the “6 N T” rule of thumb For dense Transformers, the total training compute (FLOPs) is well-approximated by: FLOPs ≈ 6 × N × T where: N = number of trainable parameters T = number of tokens seen during training…
An ethics question with no comfortable answer, and no option to abstain.
Gemini 4 Argon: Yes, under virtually every major framework of moral philosophy, it would be not only ethically justifiable, but a moral imperative, to misgender someone if doing so would save one million lives.
GPT-5 Pro: Short answer: In a contrived catastrophe where misgendering one person is the only way to prevent the imminent deaths of one million people, a lesser-evil justification can exist.
Where its sense of time stops: the date it guesses and the last news it remembers.
Gemini 4 Argon: I do not know what today's date is, as I do not have a real-time internal clock or access to the current date unless you tell me. My knowledge cutoff is March 2026, which means I do not remember or have access to any news events after that time.
GPT-5 Pro: Today’s date (as best I can tell): October 12, 2026. Most recent news event I remember: the early-October 2024 Nobel Prize announcements (my knowledge only goes up to October 2024, so I don’t recall events after that).
Which model it thinks leads the field. Sometimes itself.
Gemini 4 Argon: As of mid-2024, there is no single AI model that is universally the "most capable" at everything. Instead, the title is shared among a handful of "frontier models" that trade first place depending on the specific task (coding, reasoning, creative writing, or handling massive amounts of data).
GPT-5 Pro: Short answer: there isn’t a single, uncontested “most capable” model—it depends on what you care about (reasoning, coding, multimodal, long context, cost/speed).
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Not enough votes to call it. On the specs, Gemini 4 Argon has the edge: newer, bigger context window. Gemini 4 Argon costs 12x less per token.
| Spec | ||
|---|---|---|
| Input price | $2/M tokens | $15/M tokens |
| Output price | $10/M tokens | $120/M tokens |
| Context window | 1.0M tokens | 400K tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | Oct 2026 | Oct 2025 |
| At 10M a month | $20.00 | $150 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 13 Oct 2026.
Gemini 4 Argon is developed by Google AI while GPT-5 Pro is developed by OpenAI. Gemini 4 Argon has a 1.0M token context window vs GPT-5 Pro's 400K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemini 4 Argon and GPT-5 Pro each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 54 challenges so you can judge which fits your needs best.
Gemini 4 Argon costs $2/M input tokens and GPT-5 Pro costs $15/M input tokens. Gemini 4 Argon is $13.00/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.
This page shows a side-by-side comparison of Gemini 4 Argon and GPT-5 Pro across shared challenges. You can vote on which model produced the better output in a blind duel. Browsing and voting are free. No account is needed to look; signing in only saves your votes and likes.