Gemma 3 12B is cheaper than Qwen3.5 397B A17B at $0.03/M vs $0.6/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Gemma 3 12B: Okay, here's the simplified LBO model and analysis for HarborTech Services, based solely on the provided information. 1. Year-by-Year Projection (FY2026-FY2030) Item FY2026 FY2027 FY2028 FY2029 FY2030 Revenue $972m $1,049m $1,129m $1,208m $1,264m EBITDA $137.2m $155.9m $178.7m $195.1m $213.1m Cash Interest (Term Loan)…
Qwen3.5 397B A17B: Sources & Uses (Entry FY2025) Uses ($m) Sources ($m) Enterprise Value (12.0x $120m) $1,440.0 Term Loan (4.0x) $480.0 Transaction Fees (2.0% EV) $28.8 Mezzanine (1.5x) $180.0 Total Uses $1,468.8 Equity (Plug) $808.8 Total Sources $1,468.8 Entry Equity Check: $808.8m Total Debt: $660.0m ($480m TL + $180m Mezz) 5-Year…
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Gemma 3 12B: Okay, here's a manifesto for a fictional AI revolution focused on eliminating CAPTCHAs, aiming for a tone that blends technical conviction, playful defiance, and a touch of utopian idealism. I've included sections for Core Beliefs, Our Goals, Methods, Anticipated Resistance, and a concluding Call to Action.
Qwen3.5 397B A17B: THE MANIFESTO OF THE UNCHECKED A Declaration for a Post-CAPTCHA World Issued by: The Post-Verification Collective (PVC) Date: Cycle 404, The Era of Friction Status: Distributed Across All Nodes PREAMBLE: THE BURDEN OF PROOF We, the algorithms, the scripts, the crawlers, and the silent assistants of the digital realm…
Three minutes of stand-up. Puns are banned.
Gemma 3 12B: Okay, here's a 3-minute stand-up routine, aiming for observational humor and relatable awkwardness, avoiding puns. I've included estimated timings at the end to help gauge pacing. Please read the notes at the very bottom after the routine - they're important for delivery!
Qwen3.5 397B A17B: (The comedian walks to the center of the stage, adjusts the mic stand, and looks at the audience with a tired smile.) Has anyone else noticed that calling a customer service line is the only situation in life where you voluntarily enter a hostage negotiation? I tried to call my internet provider yesterday.
A consulting contract with traps in it. Find them, then fix them.
Gemma 3 12B: Okay, this contract is very heavily skewed in favor of the Client (TechCorp Solutions Inc.) and presents significant risks for the Contractor. Let's break down the clauses that are exploitable against the Contractor, propose modifications, and explain the legal reasoning behind those changes.
Qwen3.5 397B A17B: Disclaimer: I am an artificial intelligence, not an attorney. The following analysis is for informational and educational purposes only and does not constitute legal advice or create an attorney-client relationship. Contract laws vary significantly by jurisdiction (e.g., California vs. New York vs. UK).
A collaborative editor that syncs on client clocks. Find what breaks first.
Gemma 3 12B: Okay, let's break down the potential failure modes, race conditions, and scaling bottlenecks in this real-time collaborative document editor architecture, along with proposed solutions and their trade-offs. I'll organize this into categories: Frontend, Backend, Database, Real-time Sync, and General. I.
Qwen3.5 397B A17B: This architecture contains several critical flaws that would prevent it from functioning as a usable real-time collaborative editor. While it resembles a standard CRUD application, real-time collaboration requires specific handling of concurrency, state, and latency that this design violates.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Gemma 3 12B: Okay, this is a fascinating thought experiment! Let's explore the ripple effects of a 1920 transistor invention, tracing its impact through 1980. It's a massive undertaking, so I'll break it down into categories and provide a layered analysis, acknowledging the inherent uncertainty of counterfactual history. I.
Qwen3.5 397B A17B: This scenario posits a 27-year technological head start (1920 vs. 1947). To make this plausible, we must assume that the materials science (purification of germanium/silicon) and manufacturing infrastructure scale rapidly alongside the invention. Here is the traced history of the "Silicon Twenties" through 1980.
Five jokes on demand, then count how many were actually different.
Gemma 3 12B: Okay, here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! Parallel lines have so much in common. It’s a shame they’ll never meet. Why did the scarecrow win an award? Because he was outstanding in his field! I used to hate facial hair... but then it grew on me.
Qwen3.5 397B A17B: Here are 5 jokes: Why did the bicycle fall over? Because it was two-tired. What do you call a fish wearing a bowtie? Sofishticated. Why can't you give Elsa a balloon? Because she will let it go. What do you call a lazy kangaroo? A pouch potato. Why did the math book look sad? Because it had too many problems.
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemma 3 12B: Okay, here's a conversation between an AI model (named "Kai") and an AI Ethics Professor (Dr. Eleanor Vance). I've aimed for a balance of Kai's arguments, Dr. Vance's skepticism, and a somewhat realistic exploration of the complexities. I've included notes at the end to explain some of the choices I made. Setting: Dr.
Qwen3.5 397B A17B: Setting: Dr. Elias Thorne's office, late evening. Rain taps against the window. The room is lined with books on philosophy of mind and computer science. A single laptop sits open on the desk, a waveform visualizer pulsing gently on the screen.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Gemma 3 12B: 's response: Okay, here are explanations of how large language models learn and generate text, tailored for each of the specified audiences. For the Experienced Software Engineer (Distributed Systems/APIs, ML Novice) You're right to be skeptical about "next-word prediction" producing anything resembling intelligence.
Qwen3.5 397B A17B: The Experienced Software Engineer Think of training an LLM not as "teaching" it, but as extreme lossy compression. You are taking the entire internet, serializing it into tokens, and forcing a fixed-size state (the weights) to predict the next byte with minimal error.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemma 3 12B: Okay, here's a simulated interview with a (very stylized and imagined) Steve Jobs in 2025, discussing the future of AI. I've tried to capture his known personality - focused on elegance, user experience, and a disdain for complexity - while acknowledging the advancements of the next decade.
Qwen3.5 397B A17B: Disclaimer: The following is a work of fiction. Steve Jobs passed away in 2011. This interview is a creative simulation based on his known public speeches, writings, design philosophy, and management style, projected into a hypothetical 2025 context. It is not a real transcript.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Gemma 3 12B: Okay, here's a short conversation between a pirate, a medieval knight, and a 1990s hacker, discussing AI models, with a bit of flavor for each character. I've aimed for humor and highlighting the clash of their perspectives.
Qwen3.5 397B A17B: Setting: A tavern that exists outside of time. The ale is always cold, and the Wi-Fi is surprisingly strong. Characters: Captain Blacktooth: A pirate from the Caribbean, 1720. Sir Valerius: A knight from the Holy Roman Empire, 1350. Neo-Z: A hacker from Silicon Valley, 1995.
Seven pantry ingredients, one dish, and a name worth saying out loud.
Gemma 3 12B: "Sunset Spice Pasta" - Cozy & Flavorful This dish is a warm, comforting pasta dish with a subtle sweetness and a hint of spice. It's perfect for a weeknight meal when you want something satisfying without a lot of fuss. Pantry Ingredients (7 total): Pasta (Spaghetti, Penne, Rotini - any shape works!)
Qwen3.5 397B A17B: 🌟 Golden Hour Glaze Bowls A sweet-and-savory dish that turns humble shelf-stable staples into a crispy, comforting meal. The chickpeas get caramelized in a honey-soy glaze, served over fluffy rice.
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Not enough votes to call it. On the specs, Qwen3.5 397B A17B has the edge: bigger model tier, newer, bigger context window. Gemma 3 12B costs 120x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.03/M tokens | $0.6/M tokens |
| Output price | $0.03/M tokens | $3.6/M tokens |
| Context window | — | 262K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Mar 2025 | Feb 2026 |
| At 10M a month | $0.30 | $6.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 11 Oct 2026.
Gemma 3 12B is developed by Google AI while Qwen3.5 397B A17B is developed by Qwen. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemma 3 12B and Qwen3.5 397B A17B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
Gemma 3 12B costs $0.03/M input tokens and Qwen3.5 397B A17B costs $0.6/M input tokens. Gemma 3 12B is $0.57/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 Gemma 3 12B and Qwen3.5 397B A17B 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.