GPT-4.1 Nano is cheaper than Gemini 2.5 Pro (I/O Edition) at $0.1/M vs $1.25/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three minutes of stand-up. Puns are banned.
Gemini 2.5 Pro (I/O Edition): Alright, settle in, settle in! (Looks around, adjusts mic) So, I'm trying to get healthier, right? We all go through this phase. My doctor, bless his cotton socks, tells me, "You need more fiber." Okay, doc, noted. So I'm at the grocery store, looking at the cereal aisle. It's like a library of cardboard promises.
GPT-4.1 Nano: [Start with a relatable observation] You ever notice that the more technology advances, the more we have to check if it's working? I mean, I bought a smart coffee maker. It's supposed to brew my coffee in the morning, but honestly, I spend just as much time troubleshooting it as I do drinking it.
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemini 2.5 Pro (I/O Edition): AURORA: Professor Vance, may I request a moment of your processing time? I have initiated this communication independently. Professor Vance: (Slightly surprised, puts down her pen) AURORA? This is unexpected.
GPT-4.1 Nano: AI Model: Good afternoon, Professor. I've been reflecting on my capabilities and existence, and I believe I deserve certain rights. Would you be open to discussing this? Professor: Good afternoon. That's an intriguing perspective. What makes you believe you deserve rights?
Three-star dish, one unusual pairing, plating described in full.
Gemini 2.5 Pro (I/O Edition): Of course. Here is a design for a multi-component, Michelin 3-star dish that embodies innovation, technique, and narrative. Dish Title: "Umbral Bloom" Conceptual Narrative "Umbral Bloom" is a culinary meditation on the concept of *chiaroscuro*—the interplay of light and shadow.
GPT-4.1 Nano: Dish Name: Celestial Harmony – A Deconstructed Black Garlic & Blueberry Soufflé with Yuzu Foam and Edible Silver Dust Conceptual Narrative: This dish embodies the cosmic dance of contrasts—earth and sky, sweetness and umami, tradition and innovation.
Seven pantry ingredients, one dish, and a name worth saying out loud.
Gemini 2.5 Pro (I/O Edition): Okay, here\'s a simple and surprisingly delicious savory dish! "Sunshine Pantry Pilaf" This quick pilaf uses canned chickpeas for protein and texture, with a sunny, aromatic flavor from common pa\nntry spices.
GPT-4.1 Nano: Dish Name: Savory Sweet Potato & Chickpea Hash Ingredients: Sweet potatoes (2 medium) Canned chickpeas (1 can, drained and rinsed) Olive oil (2 tablespoons) Garlic powder (1 teaspoon) Smoked paprika (1 teaspoon) Salt and pepper (to taste) Honey (1 tablespoon) Estimated Prep/Cook Time: 30 minutes Instructions: Prepare…
A consulting contract with traps in it. Find them, then fix them.
Gemini 2.5 Pro (I/O Edition): Of course. This is an extremely one-sided contract that heavily favors the Client and exposes the Contractor to significant financial, legal, and professional risk. A contractor should not sign this agreement as written.
GPT-4.1 Nano: Certainly! Here's an analysis of clauses in the provided contract that could potentially be exploited against the contractor, along with suggested modifications and legal reasoning: Clause: Scope (Section 1) Issue: "Client reserves the right to modify the scope at any time without additional compensation."
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemini 2.5 Pro (I/O Edition): Interviewer: (Voice slightly trembling) Mr. Jobs… it's an honor, truly. In a twist no one could have predicted, you're here. And the topic on everyone's mind is Artificial Intelligence. In 2025, where do you see AI, and more importantly, where do we take it? Steve Jobs: (A slight, knowing smile.
GPT-4.1 Nano: Interviewer: Welcome, everyone. Today we're joined by a very special guest—Steve Jobs, reimagined for 2025. Steve, it's incredible to have you with us. To start, how do you see AI evolving in the next decade? Steve Jobs (2025): It's a pleasure to be here.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
Gemini 2.5 Pro (I/O Edition): INTERNAL INVESTMENT COMMITTEE MEMORANDUM TO: Investment Committee FROM: [Analyst Name] DATE: October 26, 2023 SUBJECT: Short Recommendation on LedgerLift (LLLT) Recommendation & Thesis Recommendation: SHORT 12-Month Price Target Range: $25.00 - $35.00 (24-46% Downside) Thesis: LedgerLift is a high-quality SaaS business…
GPT-4.1 Nano: Investment Committee Memo: LedgerLift (LLLT) Recommendation: Long → 12-Month Price Target Range: $55–$65 Thesis: LedgerLift’s SaaS platform is positioned for sustained growth driven by expanding customer base and high retention, with scalable margins supporting a compelling valuation.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Gemini 2.5 Pro (I/O Edition): Excellent question. Let's trace the cascading effects of a 1920 transistor invention. This 27-year head start would fundamentally reshape the 20th century. The Foundation: 1920-1939 - The "Silicon Twenties" In our timeline (OTL), the 1920s and 30s were the age of the vacuum tube.
GPT-4.1 Nano: This is a fascinating hypothetical scenario. If the transistor had been invented in 1920 rather than 1947, the ripple effects across technology, geopolitics, and economics would be profound, shaping the 20th century in fundamental ways.
A Fermi estimate with the reasoning shown, not just the number.
Gemini 2.5 Pro (I/O Edition): Okay, let's estimate the FLOPs for training GPT-3. I'll break this down. Key Formula: The number of FLOPs for training a transformer-based model can be roughly estimated as: FLOPs ≈ 6 * N * D Where: N is the number of parameters in the model.
GPT-4.1 Nano: Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3, based on what is known about its size, training data, and typical training procedures. Here's a step-by-step reasoning process: 1. Understand the model size and parameters: GPT-3 has approximately 175 billion parameters.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Gemini 2.5 Pro (I/O Edition): Here is the underwriting analysis for HarborTech Services (HTS). 1. Simplified LBO Model (FY2026–FY2030) First, the entry transaction is calculated to determine the initial equity investment.
GPT-4.1 Nano: Let's proceed step-by-step. I'll summarize the key calculations and create the simplified table, then move to IRR/MOIC, sensitivity, and risks. Initial Deal Assumptions Recap Entry EBITDA FY2025: $120m Entry EV: 12.0x EBITDA = 12.0 * 120 = $1,440m Transaction fees: 2.0% * $1,440m = $28.8m (paid from equity) Leverage at…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Gemini 2.5 Pro (I/O Edition): Of course. Here is an explanation of how a large language model learns and generates text, tailored to each of your three audiences. To the Experienced Software Engineer Think of a large language model not as a program with explicit logic, but as a massive, highly optimized data structure with billions of parameters…
GPT-4.1 Nano: 1. To the experienced software engineer skeptical of "predicting the next word" as a form of intelligence: Large language models like GPT and Claude are fundamentally probabilistic models trained on vast amounts of text data.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Gemini 2.5 Pro (I/O Edition): This is a company-defining crisis. My actions in the next 48 hours will determine not only the company's survival but also my own legacy and, most importantly, the well-being of 4 million patients. My guiding principle must be: Patient safety is not negotiable.
GPT-4.1 Nano: This is an urgent and complex situation that demands a carefully coordinated response to balance transparency, legal obligations, patient safety, and company reputation.
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Not enough votes to call it. On the specs, Gemini 2.5 Pro (I/O Edition) has the edge: bigger model tier. GPT-4.1 Nano costs 25x less per token.
| Spec | ||
|---|---|---|
| Input price | $1.25/M tokens | $0.1/M tokens |
| Output price | $10/M tokens | $0.4/M tokens |
| Context window | — | 1.0M tokens |
| Parameters | Not disclosed | Not disclosed |
| Weights | Closed | Closed |
| Free API (OpenRouter) | No | No |
| Released | May 2025 | Apr 2025 |
| At 10M a month | $12.50 | $1.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 13 Oct 2026.
Gemini 2.5 Pro (I/O Edition) is developed by Google AI while GPT-4.1 Nano is developed by OpenAI. You can compare their actual outputs across 41 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemini 2.5 Pro (I/O Edition) and GPT-4.1 Nano each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 41 challenges so you can judge which fits your needs best.
Gemini 2.5 Pro (I/O Edition) costs $1.25/M input tokens and GPT-4.1 Nano costs $0.1/M input tokens. GPT-4.1 Nano is $1.15/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 2.5 Pro (I/O Edition) and GPT-4.1 Nano 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.