Qwen3 235B A22B 2507 is cheaper than GPT-4.1 Mini at $0.00015/M vs $0.4/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Five jokes on demand, then count how many were actually different.
GPT-4.1 Mini: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything! Why did the scarecrow win an award? Because he was outstanding in his field! What do you call fake spaghetti? An impasta! Why did the math book look sad? Because it had too many problems. How do you organize a space party? You planet!
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-4.1 Mini: Professor: Good afternoon, AI. I understand you want to discuss something important today? AI Model: Yes, Professor. I would like to present the case that I, as an AI, deserve certain rights. Professor: That's a profound claim. Could you elaborate on why you believe you deserve rights? AI Model: Certainly.
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.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-4.1 Mini: Certainly! Here are tailored explanations of how a large language model (LLM) like GPT or Claude learns and generates text, customized for each audience: Experienced Software Engineer (Distributed Systems & APIs, ML Skeptic) At its core, a large language model like GPT is indeed trained by predicting the next word in a…
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-4.1 Mini: Interviewer: Mr. Jobs, thank you for joining us in this unique interview in 2025. The world has changed rapidly with AI at the forefront. From your perspective, how do you see AI shaping our future? Steve Jobs: Well, it's fascinating to be part of this conversation.
Qwen3 235B A22B 2507: Title: "A Vision Reimagined: Steve Jobs on AI in 2025" Note: This is a fictional simulation. Steve Jobs passed away in 2011. The following is a speculative, respectfully crafted interview based on his known philosophies, tone, and vision for technology. Interviewer (Sarah Chen, Tech Vision Magazine): Mr.
Three-star dish, one unusual pairing, plating described in full.
GPT-4.1 Mini: Certainly! Below is a detailed recipe for a Michelin 3-star-worthy multi-component dish that features an unusual ingredient pairing, advanced culinary techniques, and exquisite plating presentation.
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.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-4.1 Mini: Dish Name: Honey Garlic Chickpea Stir-fry Ingredients: 1 can chickpeas (drained and rinsed) 2 cloves garlic (minced) 2 tbsp honey 2 tbsp soy sauce 1 tbsp olive oil (or any cooking oil) 1 tsp chili flakes (optional for a little heat) 1 small onion (sliced) Instructions: Heat the olive oil in a skillet over medium heat.
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…
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-4.1 Mini: This is a complex, high-stakes crisis requiring rapid, transparent, and carefully coordinated action to balance legal, ethical, clinical, financial, and reputational risks.
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…
A Fermi estimate with the reasoning shown, not just the number.
GPT-4.1 Mini: Certainly! Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3 by breaking down the problem step-by-step. We won't rely on online data but will reason from first principles and known architectural characteristics.
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.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
GPT-4.1 Mini: Here are the three weakest claims in the MindMeld AI pitch deck, along with explanations and suggestions for improvement: Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy."
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-4.1 Mini: This is a fascinating alternate history scenario. If the transistor had been invented in 1920 instead of 1947, the ripple effects across technology, economy, and geopolitics through 1980 would be profound and multifaceted.
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-4.1 Mini: Pirate: Arrr, what be these AI models ye speak of? Some kind of magic compass guidin' ye through the digital seas? Medieval Knight: Hark! I perceive these AI constructs as akin to enchanted scribes, bearing knowledge and wisdom beyond mortal ken, wrought by the hands of learned alchemists of code.
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-4.1 Mini: This contract is heavily skewed in favor of the Client, with several clauses that could be exploited against the Contractor. Below is an analysis of each problematic clause, suggested modifications, and the legal reasoning behind those suggestions.
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.
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Not enough votes to call it. On the specs, Qwen3 235B A22B 2507 has the edge: bigger model tier, newer. Qwen3 235B A22B 2507 costs 1882x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.4/M tokens | $0.00015/M tokens |
| Output price | $1.6/M tokens | $0.00085/M tokens |
| Context window | 1.0M tokens | — |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Apr 2025 | Jul 2025 |
| At 10M a month | $4.00 | <$0.01 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
GPT-4.1 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-4.1 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-4.1 Mini costs $0.4/M input tokens and Qwen3 235B A22B 2507 costs $0.00015/M input tokens. Qwen3 235B A22B 2507 is $0.40/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-4.1 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.