Qwen3.6 35B A3B is cheaper than Gemini 2.5 Pro (I/O Edition) at $0.1612/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.
Qwen3.6 35B A3B: Title: High-Maintenance Electronics Time: Approx. 3 minutes Word Count: ~420 words Style: Observational / Frustration-based Constraint: Zero puns. (Walk to mic, adjust stand. Take a deep breath. Look tired.) I've reached that age where I'm just trying to negotiate with my own life.
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.
Qwen3.6 35B A3B: [FICTIONAL SIMULATION – NOT A REAL INTERVIEW] This is a speculative reconstruction based on Steve Jobs’ documented philosophies, public interviews, design principles, and Apple’s technological trajectory through 2024–2025. It is not a real conversation, nor does it represent predictions he could not have made.
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.
Qwen3.6 35B A3B: EXECUTIVE CRISIS PLAN: 48-HOUR ACTION WINDOW Core Mandate: Patient safety and regulatory compliance are non-negotiable. The legal team’s “6-month reporting” guidance is legally indefensible under FDA 21 CFR 312.32/314.80 and EMA GVP Module VI.
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.
Qwen3.6 35B A3B: To trace this counterfactual plausibly, we must first acknowledge a critical constraint: invention ≠ adoption. In 1920, semiconductor physics, crystal growth, doping techniques, photolithography, and clean-room manufacturing did not exist.
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.
Qwen3.6 35B A3B: Setting: A soundproofed observation room. DR. ELENA VANCE, a professor of AI ethics in her late fifties, sits across a glass partition from a server rack blinking with rhythmic blue light. A secure terminal sits between them. The text cursor on the screen blinks steadily. Dr.
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…
Qwen3.6 35B A3B: For the Experienced Software Engineer Training an LLM is essentially a massively parallelized optimization job. You feed billions of text tokens into a transformer architecture, which computes a differentiable function across ~100B parameters.
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.
Qwen3.6 35B A3B: Disclaimer: I am an AI assistant, not an attorney. The following analysis is for informational purposes only and does not constitute legal advice. Contract law varies significantly by jurisdiction (e.g., California vs. New York vs. EU).
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.
Qwen3.6 35B A3B: Here’s a step-by-step reasoning process to estimate the total FLOPs required to train GPT-3, using only established architectural principles and widely cited training parameters. Step 1: Identify the number of trainable parameters (N) GPT-3 is a decoder-only transformer with 175 billion parameters.
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.
Qwen3.6 35B A3B: Here’s a systematic breakdown of the critical failure modes, race conditions, and scaling bottlenecks in this architecture, along with specific solutions and their trade-offs. 🔴 1. WebSocket Routing & Load Balancing Problem: Round-robin LB + per-server WebSocket state + no sticky sessions.
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.
Qwen3.6 35B A3B: 🍜 Golden Honey-Garlic Snap Noodles A quick, sticky-yet-chewy noodle dish that balances sweet, salty, and spicy flavors. It's comforting, fast, and uses ingredients you likely already have.
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.
Qwen3.6 35B A3B: ⚠️ Medical Disclaimer & Safety Protocol This plan is for informational purposes only and constitutes advanced biohacking strategies, not medical advice. Consultation Required: You must consult a physician before starting, especially regarding supplements, fasting, and exercise intensity.
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.
Qwen3.6 35B A3B: Simplified Year-by-Year Model (FY2026–FY2030) All figures in $m. Assumptions: TL amort = 1.0% of beginning balance; Mezz PIK compounds to principal; ΔNWC = 0.5% of incremental revenue; Cash Tax = 25% × max(0, EBITDA – Cash Interest).
7+ more head-to-head results. Free. Not a trick.
Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy
Not enough votes to call it. On the specs, Gemini 2.5 Pro (I/O Edition) has the edge: bigger model tier, major provider backing. Qwen3.6 35B A3B costs 10x less per token.
| Spec | ||
|---|---|---|
| Input price | $1.25/M tokens | $0.1612/M tokens |
| Output price | $10/M tokens | $0.9653/M tokens |
| Context window | — | 262K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | May 2025 | Apr 2026 |
| At 10M a month | $12.50 | $1.61 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
Gemini 2.5 Pro (I/O Edition) is developed by Google AI while Qwen3.6 35B A3B is developed by Qwen. 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 Qwen3.6 35B A3B 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 Qwen3.6 35B A3B costs $0.1612/M input tokens. Qwen3.6 35B A3B is $1.09/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 Qwen3.6 35B A3B 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.