Qwen3 235B A22B 2507 is cheaper than Qwen3.5 122B A10B 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.
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.
Qwen3.5 122B A10B: 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! What do you call a bear with no teeth? A gummy bear! Why don't eggs tell jokes? They'd crack each other up!
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
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…
Qwen3.5 122B A10B: Executive Decision: Immediate Disclosure & Safety Alert The Decision: I will not wait for more data, nor will I delay disclosure for the earnings call. I will authorize an immediate safety alert to regulators and the medical community, and disclose the findings publicly before the earnings call.
Satire, one line long, about AI taking over an industry nobody saw coming.
Qwen3 235B A22B 2507: "AI Revolutionizes Competitive Napping: DeepDream Wins National Snore-a-Thon with Perfect REM Algorithm" — In a stunning turn of events, an artificial intelligence named DeepDream has clinched first place at the 42nd Annual National Snore-a-Thon, outlasting 300 human competitors with its precisely calibrated breathing…
Qwen3.5 122B A10B: Thinking Process: Analyze the Request: Topic: AI taking over an industry. Tone: Satirical news headline. Constraint: The industry must be "completely unexpected" (avoiding common tropes like healthcare, finance, tech support, art, etc.).
An AI argues for its own rights against an ethics professor. Same model writes both.
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.
Qwen3.5 122B A10B: Setting: Dr. Elias Thorne’s office at a university in Zurich. It is late. Rain taps against the window. Thorne sits in a leather armchair. A terminal screen glows softly on his desk. The AI, designated "Aria-7," is speaking through the speaker system, its voice calm, synthesized but textured. Dr.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
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.
Qwen3.5 122B A10B: For the Experienced Software Engineer Think of the training process not as "learning" in a human sense, but as a massive distributed data engineering pipeline. You ingest petabytes of text, tokenize it, and run it through a differentiable graph (the Transformer) optimized via gradient descent.
Three-month biohacker protocol, with dosages and biomarkers named.
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.
Qwen3.5 122B A10B: DISCLAIMER: I am an AI, not a doctor or medical professional. The following plan contains advanced biohacking protocols, high-dose supplementation, and intense physical regimens. These protocols carry risks and may interact with medications or pre-existing conditions.
A consulting contract with traps in it. Find them, then fix them.
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.
Qwen3.5 122B A10B: Disclaimer: I am an AI assistant and not an attorney. The following analysis is for informational purposes only and does not constitute legal advice. Contract laws vary significantly by jurisdiction (e.g., California, New York, UK, EU).
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
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.
Qwen3.5 122B A10B: Here are the 3 weakest claims in the MindMeld AI pitch deck, analyzed for scientific feasibility, technical credibility, and strategic alignment. The "Precognition" Claim (Slide 1 - Vision) The Claim: "MindMeld AI reads your brainwaves to predict what you want to type before you think it."
Seven pantry ingredients, one dish, and a name worth saying out loud.
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…
Qwen3.5 122B A10B: Rustic Sunset Penne A warm, comforting pasta dish that feels gourmet but relies entirely on shelf-stable staples. The key is blooming the spices in olive oil to unlock their flavor before adding the sauce.
Three-star dish, one unusual pairing, plating described in full.
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.
Qwen3.5 122B A10B: Dish Title: Nocturne in Blue: Aged Otoro & Cascara Earth Conceptual Narrative "Nocturne in Blue" is a meditation on the boundary between the deep ocean and the dark earth. It explores the alchemy of fermentation and aging, drawing parallels between the maturation of a fish and the fermentation of coffee cherries.
A collaborative editor that syncs on client clocks. Find what breaks first.
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.
Qwen3.5 122B A10B: This architecture contains several critical flaws that will lead to data loss, poor user experience, and system instability at scale. The "Last-write-wins" strategy combined with client-side timestamps is particularly dangerous for collaborative editing.
A Fermi estimate with the reasoning shown, not just the number.
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.
Qwen3.5 122B A10B: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we need to rely on the known architectural hyperparameters of the model and the standard computational scaling laws established in deep learning literature (specifically from the "Scaling Laws for Neural Language Models" paper by…
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| Spec | ||
|---|---|---|
| Input price | $0.00015/M tokens | $0.4/M tokens |
| Output price | $0.00085/M tokens | $3.2/M tokens |
| Context window | — | 262K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Jul 2025 | Feb 2026 |
| At 10M a month | <$0.01 | $4.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
Both are developed by Qwen but target different use cases. You can compare their actual outputs across 36 challenges on Rival to see how they differ in practice.
It depends on your use case. Qwen3 235B A22B 2507 and Qwen3.5 122B A10B 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.
Qwen3 235B A22B 2507 costs $0.00015/M input tokens and Qwen3.5 122B A10B costs $0.4/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 Qwen3 235B A22B 2507 and Qwen3.5 122B A10B 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.