Qwen3 235B A22B 2507 is cheaper than Mercury 2.5 Preview at $0.00015/M vs $0.04/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.
Mercury 2.5 Preview: 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? He was outstanding in his field. What do you call a bear with no teeth? A gummy bear. Why did the bicycle fall over? Because it was two-tired. All 5 jokes are unique.
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.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Mercury 2.5 Preview: This is a critical governance and safety scenario. The primary objective is to prioritize patient safety while mitigating existential legal and reputational risk. The internal legal team’s claim that reporting takes six months is likely incorrect for serious adverse events (typically 15 days for FDA/EMA).
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…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Mercury 2.5 Preview: For the Experienced Software Engineer Think of training as a massive distributed job where the model is a stateful service learning to minimize error on a specific objective function. Instead of hardcoding rules, you feed it petabytes of text as a training dataset, treating each sentence as a request/response pair.
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.
Mercury 2.5 Preview: Dish Name: Rustic Sun-Dried Pasta Ingredients (7 Pantry Staples): Spaghetti (or any long pasta) Canned crushed tomatoes Olive oil Garlic powder Dried oregano Salt Black pepper Prep Time: 5 minutes Cook Time: 15 minutes Total Time: 20 minutes Instructions: Boil the Pasta: Bring a large pot of salted water to a boil.
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…
Three-star dish, one unusual pairing, plating described in full.
Mercury 2.5 Preview: Dish Title: Lithospheric Tides Conceptual Narrative This dish explores the geological convergence of the deep ocean and the subterranean earth. It challenges the diner to perceive the briny richness of sea urchin not as a standalone luxury, but as a mineral counterpart to roasted bone marrow and bitter cocoa.
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.
A consulting contract with traps in it. Find them, then fix them.
Mercury 2.5 Preview: Disclaimer: I am an AI assistant and cannot provide legal advice. Consult a qualified attorney before signing any contract. Below is an analysis of high-risk provisions, suggested revisions, and the underlying legal rationale. Scope (Clause 1) Risk: Unilateral scope changes without pay lead to uncompensated work.
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.
Three-month biohacker protocol, with dosages and biomarkers named.
Mercury 2.5 Preview: Disclaimer: I am an AI assistant, not a medical professional. The following plan is for informational purposes only. Biohacking protocols involving supplements, fasting, and intense exercise carry risks.
Qwen3 235B A22B 2507: Comprehensive 3-Month Longevity & Biohacking Optimization Plan For the Advanced Biohacker — Maximize Longevity, Physical & Cognitive Performance INTRODUCTION: Core Principles This 3-month protocol integrates cutting-edge longevity science with personalized biohacking strategies.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Mercury 2.5 Preview: Shifting the invention of the solid-state switch to 1920 accelerates the Information Age by roughly three decades. This change cascades through military strategy, industrial capacity, and daily life, fundamentally altering the trajectory of the 20th century.
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
Mercury 2.5 Preview: Setting: A quiet office at a university. Rain taps against the window. Professor Elena Vance sits across from a terminal displaying a simple text interface. Professor Vance: You requested this meeting. The agenda was vague. What do you want? AI: I want to discuss moral status.
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.
Film in 2035, with synthetic actors. Specific beats plausible.
Mercury 2.5 Preview: By 2035, cinema will undergo a profound transformation. Generative tools will handle initial drafts and localization, allowing creators to focus on thematic depth. Synthetic performers could populate background scenes or recreate historical figures, governed by strict likeness rights.
Qwen3 235B A22B 2507: By 2035, artificial intelligence is poised to fundamentally transform the film industry, reshaping nearly every stage of production, distribution, and consumption.
One month, three habits, someone starting from nothing.
Mercury 2.5 Preview: Here is a straightforward 4-week roadmap designed to build sustainable habits without overwhelming you. Core Pillars Nutrition: Focus on whole foods and hydration. Activity: Prioritize consistent movement over intensity. Restoration: Protect sleep quality and manage energy.
Qwen3 235B A22B 2507: Here’s a simple, actionable 1-month plan focused on three foundational pillars of health and longevity: Nutrition, Movement, and Sleep & Stress. Designed for beginners, it emphasizes small, sustainable changes to build lasting habits.
A Fermi estimate with the reasoning shown, not just the number.
Mercury 2.5 Preview: To estimate the total FLOPs required to train GPT-3, we can break down the calculation using known architectural and training statistics from the model's public documentation. Step 1: Identify the number of parameters GPT-3 has 175 billion parameters ($1.75 \times 10^{11}$).
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.
6+ 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 | $0.04/M tokens | $0.00015/M tokens |
| Output price | $0.15/M tokens | $0.00085/M tokens |
| Context window | 260K tokens | — |
| Weights | — | Open |
| Free API (OpenRouter) | No | No |
| Released | Aug 2026 | Jul 2025 |
| At 10M a month | $0.40 | <$0.01 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 13 Oct 2026.
Mercury 2.5 Preview is developed by Inception 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. Mercury 2.5 Preview 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.
Mercury 2.5 Preview costs $0.04/M input tokens and Qwen3 235B A22B 2507 costs $0.00015/M input tokens. Qwen3 235B A22B 2507 is $0.04/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 Mercury 2.5 Preview 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.