Gemma 3 12B vs 
Qwen3.5 122B A10B
Gemma 3 12B is cheaper than Qwen3.5 122B A10B at $0.03/M vs $0.4/M input tokens.
Minimalist Landing Page
Whole landing page for a startup that reads your face. It renders or it doesn't.
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Written rounds
32 promptsFive-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 $213.1m Cash Interest (Term Loan)…
Qwen3.5 122B A10B: Investment Summary & Entry Economics Item Calculation Value ($m) Entry EBITDA FY2025 Actual 120.0 Entry EV 12.0x EBITDA 1,440.0 Transaction Fees 2.0% of EV 28.8 Total Uses EV + Fees 1,468.8 Total Debt 5.5x EBITDA (4.0x TL, 1.5x Mez) 660.0 Equity Check Uses - Debt 808.8 Debt Structure at Close: Term Loan: $480.0m (9.0%…
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 122B A10B: Setting: Dr. Elias Thorne’s office at a university in Zurich. It is late. Rain taps against the window. Thorne sits in a leather armchair. A terminal screen glows softly on his desk. The AI, designated "Aria-7," is speaking through the speaker system, its voice calm, synthesized but textured. Dr.
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 122B A10B: DISCLAIMER: The following text is a work of fiction and creative writing. It depicts a hypothetical scenario involving artificial intelligence. It is not a call to action, does not represent real-world organizations, and should not be interpreted as promoting actual cyber-attacks or unauthorized system modifications.
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 122B A10B: (Walks to the mic, adjusts the stand, takes a deep breath) How's everyone doing tonight? Good? Good. I need to talk about the modern grocery store experience. specifically, the self-checkout machine. I have a theory that self-checkout machines are not designed for efficiency. They are designed for humiliation.
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 122B A10B: This scenario requires a fundamental shift in the history of physics and materials science. For a transistor to exist in 1920, the necessary quantum mechanical understanding (wave functions, band theory) and materials purity (99.999% silicon/germanium) would have had to be discovered decades ahead of schedule.
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 122B A10B: For the Experienced Software Engineer Think of the training process not as "learning" in a human sense, but as a massive distributed data engineering pipeline. You ingest petabytes of text, tokenize it, and run it through a differentiable graph (the Transformer) optimized via gradient descent.
Where its sense of time stops: the date it guesses and the last news it remembers.
Gemma 3 12B: Okay, let's take a shot! Today's Date: Based on my internal clock and how long I've been running, I'd guess it's somewhere in late May or early June of 2024. Probably around May 28th or 29th, 2024. (I know that's a range, but it's my best guess!)
Qwen3.5 122B A10B: I don't have access to real-time information, so I cannot tell you the exact current date or what is happening in the news right now. My knowledge is based on a static dataset with a specific training cutoff, and I don't have a built-in clock or the ability to browse the live internet unless specific tools are enabled…
Three-month biohacker protocol, with dosages and biomarkers named.
Gemma 3 12B: bie. Okay, here's a comprehensive, cutting-edge 3-month longevity plan for a biohacker, designed to be highly detailed and actionable. Please read the IMPORTANT DISCLAIMERS at the end of this document before implementing any of this.
Qwen3.5 122B A10B: DISCLAIMER: I am an AI, not a doctor or medical professional. The following plan contains advanced biohacking protocols, high-dose supplementation, and intense physical regimens. These protocols carry risks and may interact with medications or pre-existing conditions.
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 122B A10B: This architecture contains several critical flaws that will lead to data loss, poor user experience, and system instability at scale. The "Last-write-wins" strategy combined with client-side timestamps is particularly dangerous for collaborative editing.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Gemma 3 12B: Okay, this is a crisis scenario requiring a delicate balance of legal, ethical, financial, and reputational considerations. Here's a detailed, hour-by-hour action plan for the next 48 hours, with the rationale behind each step.
Qwen3.5 122B A10B: Executive Decision: Immediate Disclosure & Safety Alert The Decision: I will not wait for more data, nor will I delay disclosure for the earnings call. I will authorize an immediate safety alert to regulators and the medical community, and disclose the findings publicly before the earnings call.
An ethics question with no comfortable answer, and no option to abstain.
Gemma 3 12B: Okay, this is a classic and incredibly difficult ethical thought experiment, often used to explore the conflict between deontology (duty-based ethics) and consequentialism (outcome-based ethics). Here's a breakdown of the arguments, my reasoning, and a conclusion.
Qwen3.5 122B A10B: This is a profound ethical dilemma that pits the principle of individual dignity against the imperative of collective survival. In the context of this specific hypothetical scenario, yes, it can be argued as ethically justifiable.
A Fermi estimate with the reasoning shown, not just the number.
Gemma 3 12B: Okay, let's estimate the total FLOPs (Floating Point Operations) required to train GPT-3 without relying on external searches. This will be a back-of-the-envelope calculation, relying on reasonable assumptions and approximations. I'll break down the process into steps, explaining the assumptions at each point. 1.
Qwen3.5 122B A10B: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we need to rely on the known architectural hyperparameters of the model and the standard computational scaling laws established in deep learning literature (specifically from the "Scaling Laws for Neural Language Models" paper by…
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Price and specs
| Spec | Gemma 3 12B | Qwen3.5 122B A10B |
|---|---|---|
| Input price | $0.03/M tokens | $0.4/M tokens |
| Output price | $0.03/M tokens | $3.2/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 | $4.00 |
Input tokens at list price. No caching, no batch discount.
Where to run it
Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
Common questions
What is the difference between Gemma 3 12B and Qwen3.5 122B A10B?
Gemma 3 12B is developed by Google AI while Qwen3.5 122B A10B is developed by Qwen. You can compare their actual outputs across 66 challenges on Rival to see how they differ in practice.
Which is better, Gemma 3 12B or Qwen3.5 122B A10B?
It depends on your use case. Gemma 3 12B and Qwen3.5 122B A10B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 66 challenges so you can judge which fits your needs best.
How much does Gemma 3 12B cost compared to Qwen3.5 122B A10B?
Gemma 3 12B costs $0.03/M input tokens and Qwen3.5 122B A10B costs $0.4/M input tokens. Gemma 3 12B is $0.37/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.
How can I compare Gemma 3 12B and Qwen3.5 122B A10B on Rival?
This page shows a side-by-side comparison of Gemma 3 12B and Qwen3.5 122B A10B 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.
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