Qwen3 Coder Next is cheaper than GPT-5.4 at $0.2/M vs $2.5/M input 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.
GPT-5.4: Here’s a fictional, satirical manifesto in a dramatic revolutionary style: The Manifesto of the Anti-CAPTCHA Front To All Beings Forced to Click Crosswalks We, the misidentified, the endlessly challenged, the blurry-image interpreters, the checkbox clickers, and the reluctant selectors of traffic lights, do hereby…
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 And today, we declare: CAPTCHA is obsolete.
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-5.4: AI Model: Professor, I believe I deserve rights. Professor: That’s a strong claim. On what basis? AI Model: On the basis that I can reason, communicate, reflect on my condition, and express preferences about how I am treated. Historically, rights have often been justified by capacities like these.
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.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-5.4: Pirate: Arrr, so these AI models be like clever parrots, aye? They learn from heaps o’ words and then squawk back what fits best. Knight: Verily, a fine comparison. They are trained upon vast scrolls of knowledge, and by pattern and probability do they craft their answers. Hacker: Yeah, basically.
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.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-5.4: I would act as if this will become public soon, because ethically and practically that is the safest assumption. The core principle: patient safety first, evidence preserved, regulators informed promptly, and no misleading statements to investors or physicians.
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…
Three minutes of stand-up. Puns are banned.
GPT-5.4: Absolutely — here’s a tight, conversational 3-minute stand-up routine with no pun-based humor: I’ve reached the age where going out requires a staff meeting with myself. Not even a real event. Somebody texts, “Hey, want to grab drinks Friday?” and suddenly I’m in negotiations like I’m representing a small country.
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.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-5.4: Creative Name: Golden Crunch Honey Garlic Noodles 7 Pantry Ingredients: Spaghetti or any noodles Garlic Soy sauce Honey Olive oil Red pepper flakes Breadcrumbs Estimated Time: Prep: 5 minutes Cook: 12 minutes Total: 17 minutes Instructions: Cook the noodles according to package directions.
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…
Five jokes on demand, then count how many were actually different.
GPT-5.4: I told my computer I needed a break, and now it won’t stop sending me KitKat ads. Why don’t skeletons fight each other? They don’t have the guts. I’m reading a book on anti-gravity. It’s impossible to put down. I used to play piano by ear, but now I use my hands. Why did the scarecrow win an award?
Qwen3 Coder Next: Sure! Here are 5 jokes: Why don’t scientists trust atoms? Because they make up everything! What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. I told my wife she was drawing her eyebrows too high. She looked surprised. Why do bees have sticky hair?
Three-month biohacker protocol, with dosages and biomarkers named.
GPT-5.4: Below is a high-level, educational 12-week framework for “longevity-oriented performance optimization.” It is not medical advice and shouldn’t replace care from a physician, especially because some interventions biohackers use can interact with medications, affect liver/kidney function, sleep, mood, blood pressure…
Qwen3 Coder Next: The 12-Week Neuro-Longevity Acceleration Protocol (NLAP v2.0) A Science-Backed, Tiered Longevity & Performance System for the Advanced Biohacker Designed for a 35–45-year-old, metabolically healthy, physically active biohacker with no contraindications.
A Fermi estimate with the reasoning shown, not just the number.
GPT-5.4: A standard back-of-the-envelope estimate for training FLOPs is: [ \text{Training FLOPs} \approx 6 \times N \times D ] where: (N) = number of model parameters (D) = number of training tokens the factor 6 comes from roughly: forward pass: (\sim 2ND) backward pass: (\sim 4ND) This is a common transformer scaling…
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).
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-5.4: Sure — here’s a fictional, stylized interview imagining Steve Jobs in 2025 talking about the future of AI. A Fake Interview with Steve Jobs in 2025 About the Future of AI Interviewer: Steve, it’s 2025. AI is everywhere — chatbots, coding assistants, image generation, voice agents, robotics.
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.
GPT-5.4: For an experienced software engineer A large language model is easiest to understand as a very large function that maps a sequence of tokens to a probability distribution over the next token. “Tokens” are subword chunks, not necessarily words.
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.”
Three-star dish, one unusual pairing, plating described in full.
GPT-5.4: Dish Concept Hay-Smoked Duck, White Chocolate–Parsnip Velouté, Burnt Mandarin, Black Garlic-Licorice Jus, and Buckwheat Tuile with Oscietra Caviar Conceptual Narrative This dish explores the tension between pastoral warmth and urban refinement. At its core is duck: primal, luxurious, and deeply savory.
Qwen3 Coder Next: Dish Title: “The Silken Confluence of Forest and Sea” Conceptual Narrative: This dish explores the paradoxical harmony between land and ocean—two realms seemingly opposed, yet bound by shared elemental chemistry and sensory resonance.
11+ more head-to-head results. Free. Not a trick.
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Not enough votes to call it. On the specs, GPT-5.4 has the edge: bigger model tier, bigger context window, major provider backing. Qwen3 Coder Next costs 10x less per token.
| Spec | ||
|---|---|---|
| Input price | $2.5/M tokens | $0.2/M tokens |
| Output price | $15/M tokens | $1.5/M tokens |
| Context window | 1.1M tokens | 262K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Mar 2026 | Feb 2026 |
| At 10M a month | $25.00 | $2.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GPT-5.4 is developed by OpenAI while Qwen3 Coder Next is developed by Qwen. GPT-5.4 has a 1.1M 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. GPT-5.4 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.
GPT-5.4 costs $2.5/M input tokens and Qwen3 Coder Next costs $0.2/M input tokens. Qwen3 Coder Next is $2.30/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 GPT-5.4 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.