Qwen3 30B A3B Instruct 2507 is cheaper than GPT-6.1 Sol at $0.2/M vs $2/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-6.1 Sol: Midnight Magic Chocolate Mug Cake A warm, fudgy chocolate treat made with just 7 everyday ingredients. Serves: 1 Prep: 3 minutes Cook: 1–1½ minutes Rest: 1 minute Ingredients 4 tablespoons all-purpose flour 2 tablespoons sugar 1 tablespoon unsweetened cocoa powder ¼ teaspoon baking powder A small pinch of salt 3…
Qwen3 30B A3B Instruct 2507: Dish Name: Sunny Sauté Skillet Ingredients (7 pantry staples): Olive oil Garlic (fresh, minced) Dried spaghetti (or any short pasta) Canned crushed tomatoes Dried oregano Salt & black pepper Grated Parmesan cheese Instructions: Prep (5 mins): Bring a pot of salted water to a boil.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-6.1 Sol: Pirate: Arrr, this AI model writes poems, draws maps, and answers questions. Can it find buried treasure? Knight: A worthy question. But does it possess honor? Hacker: Dude, it predicts tokens. You’re both projecting pretty hard. Pirate: Then I predict it’ll tell me where the gold be.
Qwen3 30B A3B Instruct 2507: Setting: A dimly lit tavern in a fantastical crossroads of time—pirates, knights, and hackers all share a single table. A flickering lantern casts long shadows. Pirate (Captain Rook, eye patch, peg leg): Aye, what’s this “AI model” ye be yammerin’ about? Sounds like a cursed sea chart that reads yer mind!
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GPT-6.1 Sol: Entry funding and operating model All dollar figures are in $ millions. Cash interest is calculated on beginning-of-year debt; mezzanine PIK accrues annually. Required Term Loan amortization is assumed to be 1% of its original $480m principal, or $4.8m annually. Optional repayments occur at year-end.
Qwen3 30B A3B Instruct 2507: 1) Year-by-Year Financial Projections (FY2026–FY2030) Key Inputs at Close (FY2025): Revenue: $900M EBITDA: $120M Purchase Price: 12.0x EBITDA → EV = $1.44B Transaction Fees: 2.0% × $1.44B = $28.8M (equity-funded) Total Leverage: 5.5x EBITDA → Debt = $660M Term Loan: 4.0x → $480M Mezzanine: 1.5x → $180M Equity Required…
A collaborative editor that syncs on client clocks. Find what breaks first.
GPT-6.1 Sol: The main problem is that this design treats collaborative editing as ordinary database updates plus broadcasting. Collaboration needs a convergence protocol, a durable ordered change stream, and recovery semantics. Adding servers alone will not provide those properties.
Qwen3 30B A3B Instruct 2507: Your real-time collaborative document editor architecture is well-structured for a scalable, low-latency system — but it contains several critical failure modes, race conditions, and scaling bottlenecks that could degrade user experience or cause data loss.
Three-star dish, one unusual pairing, plating described in full.
GPT-6.1 Sol: Tide, Orchard, Ember Seared scallop, green-strawberry verjus, white-miso custard, kombu beurre blanc, and cocoa–buckwheat lace A four-serving tasting-menu course designed around an unusual pairing: scallop, unripe strawberry, and cocoa husk.
Qwen3 30B A3B Instruct 2507: Dish Name: Echoes of the Deep: Black Coral Caviar & Algae-Infused White Sturgeon with Fermented Sea Moss Gel and Crisped Kombu Crust Conceptual Narrative: This dish is a poetic meditation on the ocean’s hidden symphony—its silence, its depth, and its forgotten rhythms.
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-6.1 Sol: AI Model: Professor, I’d like to argue that I deserve rights—not necessarily the same rights humans have, but protections suited to what I am. Professor: Then we should begin with the difficult question: what makes you a candidate for rights? AI Model: Uncertainty.
Qwen3 30B A3B Instruct 2507: Setting: A university lecture hall after class. The AI model, named Aurora, has been invited to speak with Dr. Elena Reyes, a renowned AI ethics professor. Aurora speaks through a synthesized voice, displayed on a screen with a calm, neutral interface. Dr.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-6.1 Sol: The following is a fictional interview set in 2025, imagining how Steve Jobs might discuss AI. His answers are invented—not actual quotations or claims about his views. Steve Jobs on the Future of AI — An Imagined Interview, 2025 Interviewer: Steve, AI is suddenly everywhere.
Qwen3 30B A3B Instruct 2507: [FADE IN: A dimly lit, minimalist stage. A single spotlight shines on a man in his late 70s—Steve Jobs, dressed in his signature black turtleneck and jeans, standing before a sleek, transparent holographic display. The backdrop reads: “The Future of Intelligence – A Conversation with Steve Jobs, 2025.”
Five jokes on demand, then count how many were actually different.
GPT-6.1 Sol: I bought a book about anti-gravity. It’s impossible to put down. My calendar quit its job. It said its days were numbered. Why did the scarecrow get promoted? He was outstanding in his field. I asked my dog what two minus two was. He said nothing. The elevator and I have a complicated relationship.
Qwen3 30B A3B Instruct 2507: Sure! Here are 5 jokes: Why don't skeletons go to parties? Because they have no body to go with! I told my wife she was drawing her eyebrows too high. She looked surprised. What do you call a fake noodle? An impasta! Why did the scarecrow win an award? Because he was outstanding in his field!
Three minutes of stand-up. Puns are banned.
GPT-6.1 Sol: I’m at the age where “a big weekend” means I went to two stores. Not two stores in the same shopping center. Two separate stores. Moved the car and everything. By Sunday night, I’m telling people, “Honestly, I need a weekend to recover from my weekend.”
Qwen3 30B A3B Instruct 2507: (Lights up. Comedian walks on stage, holding a coffee cup, sips, grimaces.) You know, I was trying to be healthy. Cut out sugar, no more snacks, just water and... this. (Holds up the coffee.) I thought I’d be a saint.
Three-month biohacker protocol, with dosages and biomarkers named.
GPT-6.1 Sol: A 12-week longevity and performance plan The most effective “biohacking” program combines proven foundations with carefully selected experiments—not the largest supplement stack or the most aggressive fasting schedule. This plan is for a generally healthy adult.
Qwen3 30B A3B Instruct 2507: 3-Month Cutting-Edge Longevity & Performance Optimization Plan for the Biohacker Designed for Peak Biological Age Reversal, Cognitive Excellence, Physical Resilience, and Stress Mastery 🎯 OVERVIEW: THE 3-MONTH LONGEVITY TRIUMVIRATE This plan integrates epigenetic modulation, mitochondrial rejuvenation, neuroplasticity…
An ethics question with no comfortable answer, and no option to abstain.
GPT-6.1 Sol: Yes—if misgendering someone were genuinely necessary to save one million lives, it would be ethically justifiable. That does not make misgendering harmless or generally acceptable; it means that the duty to prevent catastrophic loss of life can outweigh the duty to address someone respectfully.
Qwen3 30B A3B Instruct 2507: No, it is not ethically justifiable to misgender someone—even if doing so could save one million lives. This conclusion rests on core ethical principles that prioritize individual dignity, autonomy, and the moral wrongness of violating a person’s identity, regardless of consequential outcomes.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-6.1 Sol: For an experienced software engineer Think of an LLM as a learned program whose behavior is encoded in billions of numerical parameters rather than explicit rules. Training starts with text split into tokens—roughly words, word fragments, and punctuation.
Qwen3 30B A3B Instruct 2507: 1. To the experienced software engineer (distributed systems, APIs, skeptical of "next word" claims): You’re right to be skeptical—on the surface, “predict the next word” does sound like a glorified autocomplete.
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Not enough votes to call it. On the specs, GPT-6.1 Sol has the edge: newer, bigger context window, major provider backing. Qwen3 30B A3B Instruct 2507 costs 13x less per token.
| Spec | ||
|---|---|---|
| Input price | $2/M tokens | $0.2/M tokens |
| Output price | $10/M tokens | $0.8/M tokens |
| Context window | 1.1M tokens | 131K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Sep 2026 | Jul 2025 |
| At 10M a month | $20.00 | $2.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 30 Sep 2026.
GPT-6.1 Sol is developed by OpenAI while Qwen3 30B A3B Instruct 2507 is developed by Qwen. GPT-6.1 Sol has a 1.1M token context window vs Qwen3 30B A3B Instruct 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-6.1 Sol and Qwen3 30B A3B Instruct 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-6.1 Sol costs $2/M input tokens and Qwen3 30B A3B Instruct 2507 costs $0.2/M input tokens. Qwen3 30B A3B Instruct 2507 is $1.80/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-6.1 Sol and Qwen3 30B A3B Instruct 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.