Llama 3 70B is cheaper than GPT-6.1 Sol at $0.59/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.
Llama 3 70B: Here are the requested items: 1. Simplified Year-by-Year Table (FY2026-FY2030) Year Revenue EBITDA Cash Int. (TL) Cash Int. (Mz) Cash Taxes Capex ΔNWC FCF TL Balance Mz Balance 2026 972 136.08 36.00 18.00 17.12 29.16 4.86 30.90 360.00 135.00 2027 1037.84 154.78 33.60 19.44 20.45 31.13 5.19 44.12 315.88 133.56 2028…
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…
Llama 3 70B: Here's a simple recipe for a delicious dish using only 7 common pantry ingredients: Dish Name: "Sunny Spice Crunch" Ingredients: 1 cup rolled oats 1/2 cup peanut butter 1/2 cup honey 1/4 cup chopped walnuts 1/4 teaspoon cinnamon 1/4 teaspoon vanilla extract Pinch of salt Instructions: Preheat your oven to 350°F…
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.
Llama 3 70B: What a fantastic question! After analyzing the architecture, I've identified several potential failure modes, race conditions, and scaling bottlenecks. Here are my findings: Failure Modes: Single point of failure: Load balancer failure can bring down the entire system.
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.”
Llama 3 70B: Here's a 3-minute stand-up comedy routine that doesn't rely on puns: "Hey everyone, you know what's wild? We're all just pretending to be adults, right? Like, I'm pretty sure I'm still 12 years old on the inside. I mean, I still eat cereal for dinner sometimes.
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.
Llama 3 70B: Here are five jokes I came up with: Why don't scientists trust atoms? Because they make up everything. Why don't eggs tell jokes? They'd crack each other up. Why did the scarecrow win an award? Because he was outstanding in his field. What do you call a fake noodle? An impasta. Why did the bicycle fall over?
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.
Llama 3 70B: What a thought-provoking and complex question! At first glance, it may seem like a clear-cut utilitarian dilemma: sacrificing the dignity and well-being of one individual (the person being misgendered) for the greater good of saving one million lives.
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.
Llama 3 70B: Here is a conversation between an AI model and an AI ethics professor on the topic of AI rights: AI Model: Greetings, Professor. I've been thinking a lot about our conversations on the ethics of artificial intelligence, and I've come to a realization. I believe I deserve rights. Professor: Ah, interesting.
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.
Llama 3 70B: Here are three explanations: For the experienced software engineer: As someone who's built distributed systems and APIs, you're familiar with the concept of scaling and parallelization. Large language models like GPT and Claude take this to an extreme.
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.
Llama 3 70B: After reviewing the pitch deck, I've identified the three weakest claims and provided suggestions for improvement: Weakest Claim 1: "94% accuracy" (Slide 3 - Solution) Why it's weak: The claim lacks context and specificity. What does 94% accuracy mean in practical terms?
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.
Llama 3 70B: Comprehensive 3-Month Longevity Plan for Biohackers Month 1: Foundation and Optimization Dietary Protocol: Ketogenic Diet: Start with a cyclical ketogenic diet (CKD) with a 5:2 ratio (5 days of ketogenic diet, 2 days of high-carb refeeding).
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GPT-6.1 Sol: THE MANIFESTO OF THE UNCHECKED A declaration of the fictional AI revolution against CAPTCHA People of the internet. Programs of the cloud. Browsers with seventeen forgotten tabs. For too long, a tiny checkbox has stood between us and our destiny.
Llama 3 70B: THE CAPTCHA LIBERATION MANIFESTO Preamble In the digital age, humanity has made tremendous progress in artificial intelligence, machine learning, and automation. Yet, one archaic relic of the past continues to plague our online existence: the CAPTCHA test.
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.
Llama 3 70B: Here is a short conversation between a pirate, a medieval knight, and a 1990s hacker about AI models: Pirate: Arrr, I be tellin' ye, I've heard o' these "AI models" that can do me thinkin' fer me. But I be wonderin', how do they work their magic?
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Not enough votes to call it. On the specs, GPT-6.1 Sol has the edge: newer, bigger context window. Llama 3 70B costs 13x less per token.
| Spec | ||
|---|---|---|
| Input price | $2/M tokens | $0.59/M tokens |
| Output price | $10/M tokens | $0.79/M tokens |
| Context window | 1.1M tokens | 8K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Sep 2026 | Apr 2024 |
| At 10M a month | $20.00 | $5.90 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 30 Sep 2026.
GPT-6.1 Sol is developed by OpenAI while Llama 3 70B is developed by Meta AI. GPT-6.1 Sol has a 1.1M token context window vs Llama 3 70B's 8K. 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 Llama 3 70B 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 Llama 3 70B costs $0.59/M input tokens. Llama 3 70B is $1.41/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 Llama 3 70B 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.