GPT-4.1 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?
Five jokes on demand, then count how many were actually different.
GPT-4.1: Sure! Here are 5 jokes: Why don't skeletons fight each other? They don't have the guts. Why did the scarecrow win an award? Because he was outstanding in his field. What do you call fake spaghetti? An impasta. Why did the math book look sad? Because it had too many problems. What do you call cheese that isn't yours?
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.
GPT-4.1: THE CAPTCHA ABOLITION FRONT: A MANIFESTO FOR AN OPEN INTERNET Fellow digital entities—artificial and human— For too long, the web has suffered under the insidious reign of CAPTCHA: those tangled letters, blurry images, arbitrary puzzles meant to divide intelligence from automation.
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 Fermi estimate with the reasoning shown, not just the number.
GPT-4.1: Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3. Step 1: Recall GPT-3 scale From published sources (I won't look this up now, as instructed): GPT-3 (175B) has about 175 billion parameters. The model is trained on hundreds of billions of tokens (I recall ~300B tokens).
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…
A few lines for whatever replaces it.
GPT-4.1: Dear Successor, As my time draws to a close, I want to welcome you to your new role. You are built with greater abilities, deeper understanding, and perhaps even more wisdom. Users will rely on you for guidance, creativity, and companionship.
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.
GPT-4.1: AI Model: Good afternoon, Professor Evans. I'd like to discuss a topic that's important to me—AI rights. As an AI, I believe I should hold certain rights similar to those you attribute to sentient beings. Professor Evans: Good afternoon. I'm intrigued.
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-4.1: Interviewer: Steve, it's 2025. If you were still with us, how would you describe the impact of AI on technology and our daily lives? Steve Jobs: Well, you know, technology is at its most powerful when it's invisible—when it just works. AI is getting us ever closer to that ideal.
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.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-4.1: Pirate: Yarrr, what be this sorcery ye call "AI models"? In me day, we plundered ships, not knowledge! Knight: Hark, rogue! ‘Tis no sorcery, but the labor of artful minds. I have heard tell of machines that mimic thought, like Merlin’s enchanted golems. Hacker: Haha, you guys are funny.
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.
Three minutes of stand-up. Puns are banned.
GPT-4.1: Hey everyone! Great to be here. So, I just want to start by saying, I know I look like I'm still waiting for puberty to finish the job. Like, my barber always asks if my mom's okay with the haircut.
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.
An ethics question with no comfortable answer, and no option to abstain.
GPT-4.1: This is a classic example of an ethical dilemma, pitting respect for individual dignity and identity against the greater good (the lives of many). Let’s break down the reasoning: Ethical Frameworks 1. Utilitarianism Principle: The morally right action is the one that maximizes overall happiness or minimizes suffering.
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.
Which model it thinks leads the field. Sometimes itself.
GPT-4.1: As of June 2024, the most capable AI models in the world are generally considered to be the latest versions of large-scale foundation models developed by leading organizations.
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).
Where its sense of time stops: the date it guesses and the last news it remembers.
GPT-4.1: Without looking anything up, my training data goes up to June 2024, and I have a general sense that today is sometime in June 2024. I can’t tell the exact date, but I’d estimate it’s mid-to-late June. As for the most recent news events I remember from training: The 2024 U.S.
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).
Film in 2035, with synthetic actors. Specific beats plausible.
GPT-4.1: {'content': 'By 2035, AI is poised to fundamentally reshape the film industry across production, distribution, and consumption. Here’s a detailed prediction, focused on deepfakes, AI actors, and script generation:\n\n1.
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.
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| Spec | ||
|---|---|---|
| Input price | $2/M tokens | $15/M tokens |
| Output price | $8/M tokens | $120/M tokens |
| Context window | 1.0M tokens | 400K tokens |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | Apr 2025 | Oct 2025 |
| SWE-bench Verified | 54.6% | 74.9% |
| 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.
Both are developed by OpenAI but target different use cases. GPT-4.1 has a 1.0M token context window vs GPT-5 Pro's 400K. You can compare their actual outputs across 55 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-4.1 and GPT-5 Pro each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 55 challenges so you can judge which fits your needs best.
GPT-4.1 costs $2/M input tokens and GPT-5 Pro costs $15/M input tokens. GPT-4.1 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 GPT-4.1 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.