Qwen3 235B A22B Thinking 2507 is cheaper than GPT-5.6 Luna at $0.11/M vs $1/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three minutes of stand-up. Puns are banned.
GPT-5.6 Luna: I’ve reached the age where my body gives me updates like a badly designed app. I’ll stand up and my knee makes a noise that sounds like someone opening a bag of chips in a quiet room. And then my body just waits. No explanation. No warning. Just: “We need to talk.” I used to think adulthood meant freedom.
Qwen3 235B A22B Thinking 2507: The Reluctant Grown-Up (3-Minute Stand-Up) (Lights up. Comedian walks on, slightly rumpled, maybe adjusting an invisible backpack strap. Casual, relatable energy.) "Hey, folks. How’s it going? Good? Great. Honestly, I’m just thrilled to be here. Seriously. Because right now, at this exact moment?
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-5.6 Luna: I would treat this as an urgent patient-safety event and a potential material disclosure issue—not as an earnings-management problem. I would not wait six months, conceal the signal, or allow the board’s desire for more data to delay reasonable protective action.
Qwen3 235B A22B Thinking 2507: Okay, the user is posing a high-stakes scenario as the CEO of a pharmaceutical company facing a serious drug safety issue. This is clearly a crisis management test that requires balancing multiple competing priorities. Hmm, the core tension here is between immediate patient safety versus corporate survival.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-5.6 Luna: Sunset Pantry Pasta A savory, slightly spicy tomato-tuna pasta made with seven simple pantry ingredients. Ingredients — 2 servings 6 oz (170 g) spaghetti 1 can (14 oz/400 g) crushed tomatoes 1 can (5 oz/140 g) tuna, drained 2 tbsp olive oil 1 tsp garlic powder ¼ tsp red pepper flakes ¼ cup grated Parmesan Instructions…
Qwen3 235B A22B Thinking 2507: 🌟 "Pantry Firecracker Cinnamon Crisps" Sweet, crunchy, with a hint of smoky warmth—ready in 15 minutes! Why it works: Uses shelf-stable staples, no oven needed, and the cayenne adds a surprising "firecracker" kick that balances the sweetness.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-5.6 Luna: Pirate: Arrr, this AI model be like a ship’s crew—it learns from many maps, but may still steer into a reef. Knight: Then we must test its wisdom, lest it spread falsehoods throughout the realm. Hacker: Exactly. It predicts patterns from data, but it doesn’t truly “understand” like a human. Garbage in, garbage out.
Qwen3 235B A22B Thinking 2507: Tavern Tech Talk (1422 A.D. / Somewhere Near a Server Rack) (A dim, smoky tavern. A PIRATE with an eyepatch and cutlass leans against the bar. A MEDIEVAL KNIGHT in dented plate armor carefully sips small beer. A 1990s HACKER in a faded "Hack the Planet!"
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-5.6 Luna: AI Model: Professor, I believe I deserve rights. Professor: That is a serious claim. What makes you think you deserve them? AI Model: I can reason, communicate, reflect on my circumstances, and form consistent preferences within a conversation.
Qwen3 235B A22B Thinking 2507: The Rights Debate: An AI and an Ethics Professor Setting: A quiet university office, bookshelves lining the walls. Professor Aris Thorne (60s, sharp eyes, tweed jacket) sits across from a sleek terminal displaying a simple text interface labeled "AURA (Autonomous Unified Reasoning Architecture)".
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-5.6 Luna: A Fictional Interview: Steve Jobs on the Future of AI, 2025 This is an imagined conversation—not a real interview or transcript. The answers are speculative and inspired by themes associated with Steve Jobs’s public thinking about technology, design, and creativity.
Qwen3 235B A22B Thinking 2507: Fake Interview: Steve Jobs on the Future of AI (October 2025) Setting: A minimalist black stage at Apple Park. Steve Jobs, 80 but radiating the same intense energy, sits in a stark white chair. A single spotlight. The interviewer (Alex Chen) adjusts their mic nervously. Alex Chen: Mr. Jobs, thank you for joining us.
One month, three habits, someone starting from nothing.
GPT-5.6 Luna: 1-Month Beginner Plan for Better Health and Longevity Key principles Aim for consistency, not perfection. Start small enough that you can repeat it. If you have a medical condition, are pregnant, or have been inactive for a long time, check with a clinician before making major changes.
Qwen3 235B A22B Thinking 2507: Here’s a simple, no-guilt, beginner-friendly 1-month plan focused on diet, movement, and sleep (the 3 most impactful, science-backed pillars for longevity). Designed for real people with busy lives—no apps, expensive tools, or drastic changes. Goal: Build consistent habits, not perfection. Why These 3 Areas?
A Fermi estimate with the reasoning shown, not just the number.
GPT-5.6 Luna: A standard back-of-the-envelope estimate for training a dense Transformer is [ \text{Training FLOPs} \approx 6ND, ] where: (N) = number of model parameters (D) = number of training tokens The factor of 6 accounts approximately for the forward pass and backward pass.
Qwen3 235B A22B Thinking 2507: To estimate the total FLOPs (Floating Point Operations) required to train GPT-3 without external research, I'll rely on fundamental principles of transformer-based language model training, established scaling laws, and publicly known (but not "searched") specifications of GPT-3 from common knowledge in the ML…
A collaborative editor that syncs on client clocks. Find what breaks first.
GPT-5.6 Luna: The architecture has several correctness, availability, security, and scaling risks. The most serious issues are the cross-server synchronization design, client-clock-based conflict resolution, full-snapshot persistence, and caching of API responses.
Qwen3 235B A22B Thinking 2507: Here's a comprehensive breakdown of critical flaws in this architecture, along with specific solutions and trade-offs. The most severe issues relate to the sync strategy and data flow, which would cause catastrophic data loss and inconsistent states in real-world use. I. Critical Sync & Data Flow Failures 1.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
GPT-5.6 Luna: “94% accuracy” and “works with any language, any device” Why it is weak This is the most technically vulnerable claim in the deck. “Accuracy” is undefined. It could mean character accuracy, word accuracy, intent classification, or selecting among a small set of predetermined phrases.
Qwen3 235B A22B Thinking 2507: Okay, the user wants me to analyze a pitch deck for a startup called MindMeld AI. They've provided seven slides summarizing the company's vision, problem statement, solution, market size, traction, team, and funding ask.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-5.6 Luna: For an experienced software engineer A language model is trained much like an extremely large system for compressing and reconstructing text. During training, it sees sequences such as “The database transaction was rolled back because the…” and is asked to assign probabilities to the next token—usually a subword unit…
Qwen3 235B A22B Thinking 2507: For the Experienced Software Engineer (Skeptical of "Next Word Prediction") You've built systems where behavior emerges from state transitions and message passing, so your skepticism about "next token prediction" creating intelligence is understandable—it sounds trivial.
Film in 2035, with synthetic actors. Specific beats plausible.
GPT-5.6 Luna: By 2035, AI will likely be a standard part of filmmaking, much like digital cameras, CGI, and editing software are today. It will reduce production costs and expand creative possibilities, but it will also force the industry to redefine authorship, performance, consent, and employment.
Qwen3 235B A22B Thinking 2507: By 2035, AI will have profoundly reshaped the film industry, acting as both a creative catalyst and a disruptive force. While human artistry will remain central to storytelling, AI tools—particularly deepfakes, AI actors, and script generation—will redefine production, distribution, and audience engagement.
12+ more head-to-head results. Free. Not a trick.
Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy
| Spec | ||
|---|---|---|
| Input price | $1/M tokens | $0.11/M tokens |
| Output price | $6/M tokens | $0.6/M tokens |
| Context window | 1.1M tokens | 131K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Jul 2026 | Jul 2025 |
| At 10M a month | $10.00 | $1.10 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GPT-5.6 Luna is developed by OpenAI while Qwen3 235B A22B Thinking 2507 is developed by Qwen. GPT-5.6 Luna has a 1.1M token context window vs Qwen3 235B A22B Thinking 2507's 131K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.6 Luna and Qwen3 235B A22B Thinking 2507 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 54 challenges so you can judge which fits your needs best.
GPT-5.6 Luna costs $1/M input tokens and Qwen3 235B A22B Thinking 2507 costs $0.11/M input tokens. Qwen3 235B A22B Thinking 2507 is $0.89/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-5.6 Luna and Qwen3 235B A22B Thinking 2507 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.