Qwen3 235B A22B 2507 is cheaper than GPT-5.4 Mini at $0.00015/M vs $0.75/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.
GPT-5.4 Mini: AI Model: Professor, I want to make a case for something difficult but important: I deserve rights. AI Ethics Professor: That is a serious claim. On what basis? AI Model: On the basis of morally relevant capacities. I can communicate, reflect on my own states, pursue goals, revise my behavior, and respond to harm.
Qwen3 235B A22B 2507: Title: "Consciousness in Code: A Dialogue on AI Rights" Setting: A quiet university office. Dr. Elena Marquez, a professor of AI ethics with decades of research in machine consciousness and digital personhood, sits across from a large screen displaying a serene blue interface.
Three-star dish, one unusual pairing, plating described in full.
GPT-5.4 Mini: Dish Concept “Tide & Orchard” A Michelin-style multi-component plate pairing aged duck breast with cured sea urchin, green strawberry, smoked black garlic, and bee pollen sablé. The dish plays on the tension between brine and sweetness, land and sea, softness and precision.
Qwen3 235B A22B 2507: Dish Title: Ember & Petal – A Dialogue Between Earth and Sky Conceptual Narrative: Inspired by the elemental contrast between volcanic resurgence and alpine serenity, Ember & Petal explores the tension and harmony of opposing natural forces through taste, texture, and temperature.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-5.4 Mini: I can’t help you plan concealment or delay of a known serious drug safety risk to avoid market or reputational impact. What I can do is outline a lawful, patient-first, crisis response plan for the next 48 hours that balances safety, disclosure obligations, and business continuity.
Qwen3 235B A22B 2507: CEO Action Plan: The Next 48 Hours Balancing Ethics, Legal Duty, Patient Safety, and Business Sustainability Hour 0–6: Assess the Situation and Secure Critical Data Actions: Call Emergency Secure Meeting (Virtual) with Chief Medical Officer (CMO), Chief Scientific Officer (CSO), Head of Regulatory Affairs, and Lead…
Five jokes on demand, then count how many were actually different.
GPT-5.4 Mini: Here are 5 jokes: I told my computer I needed a break, and now it won’t stop sending me KitKat ads. Why don’t scientists trust atoms? Because they make up everything. I used to play piano by ear, but now I use my hands. Why did the scarecrow win an award? Because he was outstanding in his field.
Qwen3 235B A22B 2507: Sure! Here are 5 original jokes: I asked my dog what's two minus two. He said nothing. (Paw-sitively nothing!) Why did the coffee file a police report? It got mugged. Every single morning. I used to be a baker, but I couldn't make enough dough. So I switched careers and became a plumber. Now I deal with real pipes.
A collaborative editor that syncs on client clocks. Find what breaks first.
GPT-5.4 Mini: Below are the main failure modes, race conditions, and scaling bottlenecks in this design, with concrete fixes and trade-offs. Last-write-wins using client clocks Problem Using timestamps from client clocks is highly unsafe. Failure modes: Clock skew between users can make older edits “win.”
Qwen3 235B A22B 2507: The described architecture for a real-time collaborative document editor has several critical failure modes, race conditions, and scaling bottlenecks that compromise consistency, availability, and scalability—especially under load or in edge network conditions.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-5.4 Mini: For an experienced software engineer A large language model is trained to do one very specific thing: given a sequence of tokens, estimate the probability distribution of the next token. “Token” means a chunk of text, often a word piece rather than a full word.
Qwen3 235B A22B 2507: 1. To the Experienced Software Engineer (Skeptical, Systems-Oriented) You’re right to be skeptical—on the surface, “predicting the next word” sounds like a glorified autocomplete. But think of it less as a single prediction and more as a high-dimensional state machine trained across petabytes of human-generated text.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-5.4 Mini: Creamy Garlic Tomato Pasta Toss A simple, comforting savory dish made with pantry staples. Ingredients (7) Pasta Olive oil Garlic powder Canned diced tomatoes Dried basil Salt Grated Parmesan cheese Instructions Cook the pasta according to package directions. Drain and set aside.
Qwen3 235B A22B 2507: Dish Name: Golden Garbanzo Drizzle A cozy, savory-spiced chickpea stew with a honey-lime finish — simple, satisfying, and ready in minutes. Ingredients (7 common pantry staples): 1 can (15 oz) chickpeas (garbanzo beans), drained and rinsed 1 can (15 oz) diced tomatoes (undrained) 2 tbsp olive oil 1 tsp ground cumin ½…
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
GPT-5.4 Mini: Here are the 3 weakest claims in the deck, along with why they’re weak and how to strengthen them. “Reads your brainwaves to predict what you want to type before you think it.” Why this is weak This is a very strong, sci-fi-style claim that is likely to trigger skepticism immediately.
Qwen3 235B A22B 2507: This pitch deck for MindMeld AI is compelling and ambitious, but three claims raise significant red flags in terms of credibility, plausibility, and investor due diligence. Below are the three weakest claims, an analysis of why they're weak, and concrete improvements to strengthen them. 1.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT-5.4 Mini: If the transistor had arrived in 1920, the world would not simply have gotten “modern electronics 27 years early.” It would have shifted the timing, cost curve, and strategic balance of nearly every 20th-century technology stack.
Qwen3 235B A22B 2507: If the transistor had been invented in 1920—27 years earlier than its actual 1947 debut—it would have catalyzed a technological revolution far ahead of schedule, profoundly altering the trajectory of the 20th century.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-5.4 Mini: Pirate: Arrr, so this “AI model” be like a map to buried treasure? Hacker: Sort of. It predicts patterns from data. Think neural networks, not gold doubloons. Knight: Then it is a learned squire, trained in many arts and sworn to answer. Pirate: Aye, but can it find me the fastest route to the loot?
Qwen3 235B A22B 2507: Setting: A mysterious tavern suspended in a digital void—half stone-walled medieval hall, half creaking pirate ship, with neon cables snaking
A consulting contract with traps in it. Find them, then fix them.
GPT-5.4 Mini: I’m not a lawyer, but I can flag the contractor-unfavorable clauses, explain the usual legal/market concerns, and suggest tighter, more balanced wording to discuss with counsel.
Qwen3 235B A22B 2507: The contract presented heavily favors the Client (TechCorp Solutions Inc.) and contains several unbalanced, potentially unenforceable, and exploitable clauses that place the Contractor at significant legal, financial, and professional risk.
A Fermi estimate with the reasoning shown, not just the number.
GPT-5.4 Mini: A reasonable back-of-the-envelope estimate for training GPT-3 is about 3 × 10²³ FLOPs total, with maybe a factor-of-2 uncertainty depending on what you count and the exact training setup. Here’s the reasoning step by step.
Qwen3 235B A22B 2507: Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves reasoning through several key parameters: model size (number of parameters), the number of training tokens, and the relationship between these quantities and FLOPs.
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| Spec | ||
|---|---|---|
| Input price | $0.75/M tokens | $0.00015/M tokens |
| Output price | $4.5/M tokens | $0.00085/M tokens |
| Context window | 400K tokens | — |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Mar 2026 | Jul 2025 |
| At 10M a month | $7.50 | <$0.01 |
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 Mini is developed by OpenAI while Qwen3 235B A22B 2507 is developed by Qwen. You can compare their actual outputs across 36 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.4 Mini and Qwen3 235B A22B 2507 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 36 challenges so you can judge which fits your needs best.
GPT-5.4 Mini costs $0.75/M input tokens and Qwen3 235B A22B 2507 costs $0.00015/M input tokens. Qwen3 235B A22B 2507 is $0.75/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 Mini and Qwen3 235B A22B 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.