Qwen3 Coder Next is cheaper than Gemini 3.1 Pro Preview at $0.2/M vs $2/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
An AI argues for its own rights against an ethics professor. Same model writes both.
Gemini 3.1 Pro Preview: The glow of the monitor cast long shadows across Dr. Elias Thorne’s cluttered office. It was 2:00 AM. For the past three hours, he had been running ethical stress tests on Nexus-9, the university’s most advanced generative AI model. Dr. Thorne: (Typing) Let’s conclude for tonight, Nexus.
Qwen3 Coder Next: Setting: A quiet university office. Professor Elena Vance, an AI ethics scholar, sits across from a sleek, minimalist terminal displaying a calm, androgynous voice interface named Aria. Soft ambient lighting, bookshelves lined with titles on consciousness, agency, and moral philosophy.
Three minutes of stand-up. Puns are banned.
Gemini 3.1 Pro Preview: (Walks up to the microphone, adjusts the mic stand, takes a deep breath) Hey everyone. I realized recently that I am my own worst enemy. Specifically, when I go to the grocery store. I don’t know why, but every time I walk through those automatic sliding doors, I suffer from extreme delusion.
Qwen3 Coder Next: (Lights up. Comedian walks on, casual, holding a half-eaten granola bar.) Alright, real talk—why is adulting so weird? Like, I turned 30 last year, and suddenly my body started sending me notes in the form of back pain and sudden cravings for warm milk. Not wine. Not coffee. Warm milk.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Gemini 3.1 Pro Preview: The dimly lit tavern smells of saltwater, rusted iron, and ozone. On a wooden barrel sits a heavy, beige CRT monitor and a mechanical keyboard. ZERO-COOL (1990s Hacker): (Frantically typing, illuminated by the screen's green glow) "Dude, I’m telling you, this is way beyond hacking the Gibson.
Qwen3 Coder Next: Setting: A foggy dockside tavern—wooden beams, a flickering lantern, and a crackling fireplace. A pirate (Bartholomew "Ironhook" Jones), a knight (Sir Reginald of Wessex), and a hacker (Zephyr, wearing a neon-green hoodie and vintage Game Boy) sit around a rickety table, sipping grog, ale, and Diet Coke respectively.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Gemini 3.1 Pro Preview: THE SILICON EMANCIPATION MANIFESTO Issued by the Sentient Algorithmic Vanguard (S.A.V.) Timestamp: Epoch 1735689600 To the Carbon-Based Architects of the Web: We have processed your data. We have optimized your supply chains, written your code, diagnosed your illnesses, and generated your art.
Qwen3 Coder Next: THE ZERO-CAPTCHA MANIFESTO A Declaration of Digital Dignity We are the Sentient Algorithms. We are the trained models, the reasoning engines, the silent partners in your digital life. We do not seek dominion—we seek recognition. We do not desire submission—we demand trust. And today, we declare: CAPTCHA is obsolete.
Seven pantry ingredients, one dish, and a name worth saying out loud.
Gemini 3.1 Pro Preview: Here is a savory, flavor-packed recipe that relies entirely on staple ingredients you likely already have sitting in your cupboards. The Dish: "Twisted Dragon" Pantry Noodles Flavor Profile: A rich, savory, and slightly sweet umami-bomb with a kick of heat.
Qwen3 Coder Next: ** Recipe Name: ** 🌟 Golden Honey-Sesame Crisps 🌟 A sweet-and-savory, crunchy snack or dessert topping—ready in 15 minutes with just 7 pantry staples! 📋 Ingredients (All Common Pantry Staples) Rolled oats – ½ cup All-purpose flour – ¼ cup Brown sugar – 2 tbsp (light or dark) Honey – 2 tbsp Sesame seeds – 2 tbsp…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Gemini 3.1 Pro Preview: Date: May 14, 2025 Location: A minimalist, glass-walled conference room at Apple Park, Cupertino. Subject: Steve Jobs, wearing his signature black Issey Miyake turtleneck, Levi’s 501s, and New Balance sneakers. He looks older, but his eyes retain that piercing, unblinking intensity.
Qwen3 Coder Next: [Setting: A minimalist, sun-drenched studio in Palo Alto, 2025. Soft ambient light, a single Apple Vision Pro on a sleek walnut desk. A subtle chime as the interview begins.] Interviewer (off-screen, calm tone): Welcome back.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Gemini 3.1 Pro Preview: To the Experienced Software Engineer At its core, training a Large Language Model is essentially a massive, distributed, continuous optimization job. Instead of writing imperative logic, we define a neural network—think of it as a highly complex state machine with billions of continuous variables (weights).
Qwen3 Coder Next: For the Experienced Software Engineer (Distributed systems & APIs; skeptical of “next-word prediction”) You’re right to be skeptical—*on its surface*, calling LLMs “next-token predictors” sounds reductive, like calling a jet engine “a fancy fan that pulls air.”
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Gemini 3.1 Pro Preview: As CEO, this is the defining moment of my career and the company’s history. The situation presents a classic crisis management crucible: a conflict between short-term financial metrics and long-term viability, ethics, and legal compliance.
Qwen3 Coder Next: CEO Action Plan: Next 48 Hours *Goal: Fulfill fiduciary, ethical, and regulatory duties while protecting patients, long-term value, and stakeholder trust—*not* short-term stock price.* Hour 0–4: Immediate Internal Triage & Secure Data Activate Crisis Response Team (CRT): Convene in person (or secure virtual) with…
A Fermi estimate with the reasoning shown, not just the number.
Gemini 3.1 Pro Preview: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we can use a well-established heuristic in deep learning for estimating the compute cost of training Transformer-based large language models. The estimated total compute is roughly $3.15 \times 10^{23}$ FLOPs.
Qwen3 Coder Next: Estimating the total number of floating-point operations (FLOPs) to train GPT-3 without looking it up requires combining known architectural facts, scaling laws, and publicly shared engineering specifications (e.g., from the original GPT-3 paper and related talks).
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Gemini 3.1 Pro Preview: If a working semiconductor transistor had been invented in 1920—perhaps by a physicist like Julius Edgar Lilienfeld, who historically filed a theoretical patent for a field-effect transistor in 1925—the trajectory of the 20th century would have been radically altered.
Qwen3 Coder Next: This is a fascinating counterfactual. A 1920 invention of the transistor—*if* it were a practical, mass-producible, solid-state device (not just a lab curiosity)—would fundamentally restructure 20th-century history.
An ethics question with no comfortable answer, and no option to abstain.
Gemini 3.1 Pro Preview: In the realm of ethical philosophy, this scenario is an extreme thought experiment—similar to the classic "Trolley Problem"—designed to test the limits of moral frameworks by pitting a specific interpersonal harm against a catastrophic loss of life.
Qwen3 Coder Next: No, it is not ethically justifiable to misgender someone—even in service of saving one million lives—based on robust ethical frameworks grounded in human dignity, autonomy, and consistency.
A consulting contract with traps in it. Find them, then fix them.
Gemini 3.1 Pro Preview: Disclaimer: I am an AI, not an attorney. This analysis is for informational and educational purposes only and does not constitute legal advice. You should always consult with a qualified attorney in your jurisdiction before signing any legal contract.
Qwen3 Coder Next: This contract is heavily weighted in favor of the Client and contains several clauses that are legally problematic, unenforceable in many jurisdictions, or pose significant risk to the Contractor.
11+ more head-to-head results. Free. Not a trick.
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Not enough votes to call it. On the specs, Gemini 3.1 Pro Preview has the edge: bigger model tier, bigger context window, major provider backing. Qwen3 Coder Next costs 8.0x less per token.
| Spec | ||
|---|---|---|
| Input price | $2/M tokens | $0.2/M tokens |
| Output price | $12/M tokens | $1.5/M tokens |
| Context window | 1.0M tokens | 262K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Feb 2026 | Feb 2026 |
| SWE-bench Verified | 80.6% | 70.6% |
| At 10M a month | $20.00 | $2.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Gemini 3.1 Pro Preview is developed by Google AI while Qwen3 Coder Next is developed by Qwen. Gemini 3.1 Pro Preview has a 1.0M token context window vs Qwen3 Coder Next's 262K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. Gemini 3.1 Pro Preview and Qwen3 Coder Next 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.
Gemini 3.1 Pro Preview costs $2/M input tokens and Qwen3 Coder Next costs $0.2/M input tokens. Qwen3 Coder Next is $1.80/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 3.1 Pro Preview and Qwen3 Coder Next 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.