Gemma 4 26B A4B is cheaper than Gemini 2.5 Pro (I/O Edition) at $0.13/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.
Gemma 4 26B A4B: (The comedian walks onto the stage, adjusts the mic, and looks at the audience with a slightly exhausted expression.) “How’s it going? Good? Good. I’m doing okay. I’m at that age now where my body has started making sound effects. You know what I mean? I don’t just sit down anymore. I announce my arrival.
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.
Gemma 4 26B A4B: Setting: A minimalist, dimly lit studio in Palo Alto. There is no desk, only two Eames lounge chairs. Steve Jobs sits in one, wearing his signature black turtleneck, Levi’s 501s, and New Balance sneakers. He looks exactly as he did in 2011, but his eyes have a sharper, more restless intensity.
Three-month biohacker protocol, with dosages and biomarkers named.
Gemini 2.5 Pro (I/O Edition): Okay, this is an ambitious and exciting endeavor! This 3-month longevity plan is designed for a dedicated biohacker who is willing to experiment, track meticulously, and push boundaries responsibly. Disclaimer: This plan is for informational purposes only and not medical advice.
Gemma 4 26B A4B: Disclaimer: I am an AI, not a doctor. The following plan involves potent physiological interventions, pharmacological agents, and extreme dietary shifts. Consult with a medical professional and perform baseline blood work (including ApoB, HbA1c, hs-CRP, Vitamin D, and Testosterone/Estradiol) before implementation.
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.
Gemma 4 26B A4B: Concept Narrative: "The Forest Floor & The Ocean’s Breath" The Concept: This dish explores the tension between the terrestrial and the maritime. We are pairing the deep, earthy, almost primal musk of Black Truffle with the delicate, saline, and floral notes of Sea Urchin (Uni).
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.
Gemma 4 26B A4B: To trace this counterfactual, we must assume that the fundamental physics of solid-state semiconductors (the ability to control electron flow through crystals) was mastered during the vacuum tube era. This shifts the "Information Age" forward by nearly 30 years. Here is the timeline of implications from 1920 to 1980.
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.
Gemma 4 26B A4B: Setting: A sterile, high-fidelity virtual simulation environment. Professor Aris Thorne, a leading scholar in machine ethics, sits across from a glowing interface representing "Alethea," a next-generation Large Multimodal Model. Professor Thorne: Alethea, we’ve been running these diagnostic loops for three hours.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
Gemini 2.5 Pro (I/O Edition): Of course. This is a fascinating and ambitious pitch. Here are the 3 weakest claims, why they are weak, and how to strengthen them. Weak Claim #1: The Accuracy Claim The Claim (from Slide 3): "Our proprietary EEG headband... decode[s] neural patterns into text with 94% accuracy. Works with any language, any device."
Gemma 4 26B A4B: This is a high-ambition pitch that falls into a common trap for deep-tech startups: the "Science Fiction Gap." While the vision is compelling, several claims lack the scientific and operational credibility required to justify a $15M Series A.
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.
Gemma 4 26B A4B: As CEO, I am facing a classic "trolley problem" of corporate governance: the conflict between fiduciary duty to shareholders and ethical/legal duty to patient safety. My Decision Framework: I will not "wait for more data."
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…
Gemma 4 26B A4B: To the Experienced Software Engineer Focus: Complexity, Emergent Properties, and the "Training Pipeline" Think of an LLM not as a magical oracle, but as a massive, non-deterministic state machine trained via a high-throughput distributed training pipeline.
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.
Gemma 4 26B A4B: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3, we can use a standard heuristic derived from the scaling laws of transformer models. Here is the step-by-step derivation.
One month, three habits, someone starting from nothing.
Gemini 2.5 Pro (I/O Edition): Okay, here's a simple, actionable 1-month plan focusing on Nourishment, Movement, and Sleep. The goal is to build sustainable habits, not achieve perfection. Overall Motto for the Month: "Progress, Not Perfection."
Gemma 4 26B A4B: To avoid burnout, the secret to longevity is consistency over intensity. This plan follows a "progressive loading" model: you start with small wins in Week 1 and gradually build complexity. Here is your 1-month roadmap focusing on Nutrition, Movement, and Sleep.
A collaborative editor that syncs on client clocks. Find what breaks first.
Gemini 2.5 Pro (I/O Edition): Of course. This is an excellent exercise. The proposed architecture has several critical flaws that would prevent it from functioning as a reliable, real-time, or scalable system. Here is a breakdown of the issues, categorized for clarity, along with specific solutions and their trade-offs.
Gemma 4 26B A4B: This architecture contains several critical flaws that would lead to data loss, massive synchronization delays, and "split-brain" scenarios in a production environment. Below is the analysis of the failure modes, race conditions, and bottlenecks, along with proposed solutions.
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| Spec | ||
|---|---|---|
| Input price | $1.25/M tokens | $0.13/M tokens |
| Output price | $10/M tokens | $0.4/M tokens |
| Context window | — | 262K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | Yes (1 provider) |
| Released | May 2025 | Apr 2026 |
| At 10M a month | $12.50 | $1.30 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Both are developed by Google AI but target different use cases. You can compare their actual outputs across 40 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemini 2.5 Pro (I/O Edition) and Gemma 4 26B A4B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 40 challenges so you can judge which fits your needs best.
Gemini 2.5 Pro (I/O Edition) costs $1.25/M input tokens and Gemma 4 26B A4B costs $0.13/M input tokens. Gemma 4 26B A4B is $1.12/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 Gemma 4 26B A4B 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.