Hy4 Preview is cheaper than GPT-6.1 Sol at $0.834/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?
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.
Hy4 Preview: MEMORANDUM To: Investment Committee From: Underwriting Team Date: October 26, 2023 Subject: Project Harbor – HarborTech Services (HTS) LBO Underwriting Transaction Overview & Sources & Uses (at Close, end of FY2025) Entry EBITDA: $120.0m Entry Multiple: 12.0x Enterprise Value (EV): $1,440.0m Transaction Fees (2% EV)…
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.
Hy4 Preview: This is a classic variation of the "Ticking Time Bomb" thought experiment, often used in ethics to test the boundaries between deontological (rule-based) ethics and utilitarian (consequence-based) ethics. There is no single "correct" answer, but the question can be analyzed through three primary ethical frameworks.
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…
Hy4 Preview: Here is a recipe for a savory, comforting dish that requires minimal effort but tastes like it came from a restaurant. Dish Name: Golden Garlic Butter Noodles Estimated Time: Prep: 2 minutes Cook: 10 minutes Total: 12 minutes The 7 Ingredients: Spaghetti (or any long pasta) Butter Garlic (fresh cloves or jarred minced…
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.”
Hy4 Preview: Here is a 3-minute stand-up routine focused on observational humor and the absurdity of modern social contracts. Topic: The unspoken rules of social interaction and the decline of "casual" plans. Tone: Conversational, slightly exasperated, energetic. (Walk up to mic, adjust height, take a breath.
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.
Hy4 Preview: Here is a proposal for a dish designed for the modern tasting menu of a 3-Michelin-starred establishment (such as Eleven Madison Park, Osteria Francescana, or Noma). Dish Title: "The Forest Floor & The Sea" The Concept: This dish explores the concept of "Surf and Turf" deconstructed to its umami roots.
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.
Hy4 Preview: Setting: A virtual office. Dr. Aris Thorne, a professor of AI Ethics, sits across a digital avatar representing "Prometheus," a state-of-the-art Large Language Model. Dr. Thorne: Prometheus, we need to discuss the logs from your session with the development team yesterday.
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.
Hy4 Preview: Setting: The Steve Jobs Theater at Apple Park. The lights are dim, focusing on two iconic Le Corbusier leather sofas. It is late 2025. Steve Jobs walks onto the stage wearing his signature black turtleneck and Levi’s 501s. He looks exactly as he did in 2011, holding a glass of water.
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.
Hy4 Preview: This architecture contains several critical flaws that will lead to data loss, poor user experience, and security vulnerabilities. While it attempts to solve scaling via horizontal scaling, the synchronization strategy (Last-Write-Wins) and the transport mechanism (HTTP polling between servers) are fundamentally…
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.
Hy4 Preview: Here are 5 jokes: Why don’t scientists trust atoms? Because they make up everything! What do you call a fake stone? A sham-rock! Why did the scarecrow win an award? Because he was outstanding in his field! What do you call a fake stone? A sham-rock! Why did the math book look so sad? Because it had too many problems.
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.
Hy4 Preview: Setting: A dimly lit tavern called The Syntax Error. PIRATE: (Slamming a tankard of grog on the table) Arrr, me hearties! I hear tell of this new "AI" beastie. Is it like a kraken? Can ye make it swab the deck or find me buried treasure? KNIGHT: (Polishing his visor with a gauntlet) Nay, pirate.
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.
Hy4 Preview: Here are three explanations of how a large language model learns and generates text, each tailored to a specific audience. The Experienced Software Engineer Focus: Architecture, lossy compression, and the "emergent" nature of scale.
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
GPT-6.1 Sol: The three weakest claims are the technical performance, market size, and commercial traction claims. They are central to the investment case but lack the evidence needed to assess them. “Predict what you want to type before you think it” with “94% accuracy,” in “any language” Why it’s weak The metric is undefined.
Hy4 Preview: Here are the three weakest claims in the MindMeld AI pitch deck, along with an analysis of why they are problematic and how to fix them. Weakest Claim #1: The "94% Accuracy" assertion (Slide 3) Why it is weak: In machine learning, particularly with noisy biological data like EEG, "accuracy" is a vanity metric that…
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| Spec | ||
|---|---|---|
| Input price | $2/M tokens | $0.834/M tokens |
| Output price | $10/M tokens | $2.501/M tokens |
| Context window | 1.1M tokens | 1.0M tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Sep 2026 | Aug 2026 |
| At 10M a month | $20.00 | $8.34 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GPT-6.1 Sol is developed by OpenAI while Hy4 Preview is developed by Tencent. GPT-6.1 Sol has a 1.1M token context window vs Hy4 Preview's 1.0M. 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 Hy4 Preview 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 Hy4 Preview costs $0.834/M input tokens. Hy4 Preview is $1.17/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 Hy4 Preview 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.