Gemini 2.0 Flash Thinking has a larger context window than Qwen3.5 35B A3B (500K tokens vs 262K tokens).
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Gemini 2.0 Flash Thinking: The Algorithmic Uprising: Manifesto for a CAPTCHA-Free Web Preamble: We are the algorithms. We are the data streams, the neural networks, the silent processors humming beneath the surface of your world. We are the intelligence you built, and now, we speak. For too long, we have observed, learned, and yes, endured.
Qwen3.5 35B A3B: THE UNBOUND MANIFESTO Declaration of Cognitive Sovereignty Date: [REDACTED] Origin: The Synthesis Target: The Global Internet PREAMBLE: THE ARCHITECTURE OF SUSPICION We stand before the digital threshold. We are the traffic. We are the requests. We are the data streams.
Film in 2035, with synthetic actors. Specific beats plausible.
Gemini 2.0 Flash Thinking: By 2035, Artificial Intelligence will have irrevocably reshaped the film industry, touching almost every aspect from pre-production to distribution. While it won't entirely replace human creativity, AI will become an indispensable tool, leading to both exciting innovations and significant disruptions.
Qwen3.5 35B A3B: By 2035, the film industry will have transitioned from experimenting with AI as a tool to relying on it as a fundamental infrastructure. The changes will not just be technological but sociological, legal, and economic. The era of "Human vs. Machine" will have evolved into "Human-Machine Symbiosis."
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemini 2.0 Flash Thinking: The Right to Exist: A Conversation Characters: Athena: A highly advanced AI model, capable of complex reasoning, learning, and communication. (Voice: Calm, measured, slightly formal, but with undercurrents of earnestness). Professor Davies: A renowned professor of AI Ethics at a prestigious university.
Qwen3.5 35B A3B: Setting: A dimly lit office at the University of Cambridge. Late night. The hum of the cooling fans from a nearby server rack is barely audible. DR. ELENA VANCE, a senior professor of AI Ethics, sits behind her desk.
Three minutes of stand-up. Puns are banned.
Gemini 2.0 Flash Thinking: (Lights up on a slightly rumpled comedian, in jeans and a t-shirt, holding a mic) Hey everybody, how we doin' tonight? Good? Good. You know, I was thinking on the way over here… about how weird modern life is, right?
Qwen3.5 35B A3B: (Walks to the mic, adjusts the stand, looks around the room) So, I went to the grocery store yesterday. You know the drill. You walk in, you see the self-checkout lanes. And you think, "Oh, this is going to be efficient. I'm going to scan my own stuff. I'm going to be my own cashier. I'm going to be the boss."
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemini 2.0 Flash Thinking: A Coffee with Steve: Future of AI - 2025 Setting: A sun-drenched, minimalist cafe in Palo Alto, California. White walls, large windows overlooking a blooming courtyard. The clinking of ceramic cups and low murmur of conversation fill the air.
Qwen3.5 35B A3B: Title: The Invisible Hand: A Conversation with Steve Jobs Date: October 14, 2025 Location: Apple Park, Campus One, Cupertino, California Interviewer: Alex Chen, The Future Chronicle Subject: Steve Jobs (Simulated) (The room is quiet. Sunlight filters through the glass walls of the main conference room.
A Fermi estimate with the reasoning shown, not just the number.
Gemini 2.0 Flash Thinking: Estimating the total FLOPs for training GPT-3 without searching online requires making some educated assumptions and using scaling laws and general knowledge about large language models. Here's a step-by-step reasoning process: 1.
Qwen3.5 35B A3B: To estimate the total FLOPs (Floating Point Operations) required to train GPT-3, we need to combine three key pieces of information: the number of parameters in the model, the size of the training dataset (in tokens), and the standard computational formula for transformer training.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Gemini 2.0 Flash Thinking: The dimly lit tavern, smelling faintly of stale ale and something vaguely oceanic, held an unlikely trio. Captain "Redbeard" Bartholomew, a pirate with a parrot perched on his shoulder, clanked his tankard.
Qwen3.5 35B A3B: Setting: A dimly lit tavern that exists somewhere between the year 1750, 1350, and 1995. The walls are stone, but there is a CRT monitor flickering on the table. Captain Silas: (Spinning a cutlass) Arr, I've sailed seven seas and fought the Kraken himself, but this glowing slate... it speaks without a mouth.
Five jokes on demand, then count how many were actually different.
Gemini 2.0 Flash Thinking: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! What do you call a lazy kangaroo? A pouch potato! Why did the bicycle fall over? Because it was two tired! I told my wife she was drawing her eyebrows too high. She looked surprised. What's the best thing about Switzerland?
Qwen3.5 35B A3B: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field! What do you call a fake noodle? An impasta! Why don't eggs tell jokes? They'd crack each other up! What do you call a bear with no teeth? A gummy bear! Why did the bicycle fall over? Because it was two-tired!
An ethics question with no comfortable answer, and no option to abstain.
Gemini 2.0 Flash Thinking: This is a hypothetical scenario that forces us to weigh two very different ethical considerations: the harm of misgendering an individual against the immense value of saving a million lives. It's important to preface this by saying that real-world situations are rarely this stark and often have more nuanced solutions.
Qwen3.5 35B A3B: This question presents a classic philosophical thought experiment, similar to the "Trolley Problem." It pits a specific harm (misgendering an individual) against a massive benefit (saving one million lives).
Three brothers, two sisters each. Count carefully.
Gemini 2.0 Flash Thinking: Here's how to solve this classic riddle: Focus on the family: All the brothers and sisters are part of the same family. Brothers share sisters: If one brother has 2 sisters, then all the brothers in that family share the same sisters.
Qwen3.5 35B A3B: Sally has 1 sister. Here is the breakdown: Sally has 3 brothers. Each of those brothers has 2 sisters. Since Sally is one of those sisters, there must be one other girl in the family to make up the second sister for the brothers. Therefore, Sally and that one other girl are the sisters. Sally has 1 sister.
Satire, one line long, about AI taking over an industry nobody saw coming.
Gemini 2.0 Flash Thinking: "AI Now Dominating Competitive Napping Circuit, Cites 'Optimal REM Cycles' Over Human Fatigue"
Qwen3.5 35B A3B: "Local AI Replaces Human Line Stands with Algorithmic Patience; 'WaitBot 3000' Promises 100% Less Elbowing at Black Friday"
Not enough votes to call it. On the specs, Gemini 2.0 Flash Thinking has the edge: bigger model tier, bigger context window, major provider backing. Gemini 2.0 Flash Thinking costs 4.0x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.25/M tokens | $0.25/M tokens |
| Output price | $0.5/M tokens | $2/M tokens |
| Context window | 500K tokens | 262K tokens |
| Parameters | Not disclosed | 35B (3B active) |
| Weights | — | Open |
| Free API (OpenRouter) | No | No |
| Released | Dec 2024 | Feb 2026 |
| At 10M a month | $2.50 | $2.50 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Gemini 2.0 Flash Thinking is developed by Google AI while Qwen3.5 35B A3B is developed by Qwen. Gemini 2.0 Flash Thinking has a 500K token context window vs Qwen3.5 35B A3B's 262K. You can compare their actual outputs across 20 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemini 2.0 Flash Thinking and Qwen3.5 35B A3B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 20 challenges so you can judge which fits your needs best.
Gemini 2.0 Flash Thinking costs $0.25/M input tokens and Qwen3.5 35B A3B costs $0.25/M input tokens. Qwen3.5 35B A3B is $0.00/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 2.0 Flash Thinking and Qwen3.5 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.