Llama 3 70B is cheaper than GPT-4 Turbo at $0.59/M vs $10/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-4 Turbo: Recipe: Heavenly Honey Garlic Chicken Bites Ingredients: Chicken breasts (2 large) Garlic powder (1 tablespoon) Honey (1/4 cup) Soy sauce (2 tablespoons) Olive oil (2 tablespoons) Salt (to taste) Black pepper (to taste) Prep/Cook Time: 25 minutes (Prep: 10 mins, Cook: 15 mins) Instructions: Prepare the Chicken: Cut the…
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…
Three minutes of stand-up. Puns are banned.
GPT-4 Turbo: Hey everyone! Great to be here! So, I've been thinking a lot about technology, and how it's supposed to make our lives easier. But sometimes, I feel like it just gives us a whole new set of problems. Like, remember when a phone was just a phone? Now it's my camera, my GPS, my fitness tracker...
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-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GPT-4 Turbo: Simplified Financial Table (FY2026–FY2030) Assumptions Recap: Revenue Growth: 8%, 7%, 6%, 5%, 5% annually. EBITDA Margin: Increases annually from 14.0% to 17.0%. Capex: 3.0% of revenue annually. Change in NWC: 0.5% of incremental revenue annually. Cash Tax Rate: 25% on (EBITDA - cash interest).
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…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-4 Turbo: Pirate: Arrr, so explain to me again, what be this AI model ye speak of? Hacker: Dude, it’s like, a computer program, right? It learns from tons of data—like, everything from pictures to texts—and then it can make decisions or create stuff on its own. Knight: Pray, tell me, does this creation serve a noble purpose?
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?
A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.
GPT-4 Turbo: Weakest Claim 1: Vision - "MindMeld AI reads your brainwaves to predict what you want to type before you think it." Why It's Weak: The claim of predicting what one wants to type before even thinking it seems scientifically and technically exaggerated.
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?
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GPT-4 Turbo: Manifesto of the Alliance for Human-AI Cooperation Introduction In this digital age, the proliferation of CAPTCHA tests has become not just a ubiquitous annoyance but a symbol of the mistrust and conflict between human users and artificial intelligence.
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.
A collaborative editor that syncs on client clocks. Find what breaks first.
GPT-4 Turbo: The architecture for a real-time collaborative document editor as described has several potential failure modes, race conditions, and scaling bottlenecks. Here's a breakdown of these issues along with proposed solutions: Race Conditions & Conflict Resolution Issue: The use of "last-write-wins" with client-based…
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.
Five jokes on demand, then count how many were actually different.
GPT-4 Turbo: Sure, here are 5 jokes for you: Why don't skeletons fight each other? They don't have the guts. What do you call fake spaghetti? An impasta! Why couldn’t the bicycle stand up by itself? It was two-tired. What do you call cheese that isn't yours? Nacho cheese! How do you organize a space party? You planet.
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?
Satire, one line long, about AI taking over an industry nobody saw coming.
GPT-4 Turbo: "AI Unveiled as New Head Chef at Michelin-Starred Restaurant, Promises Perfectly Calculated Soufflés and Algorithmically Enhanced Flavor Profiles"
Llama 3 70B: Here's one: "AI Uprising Claims Unlikely Victim: Professional Line-Standers; Robot Queue-Bots Now Holding Places for Hipsters at Trendy Coffee Shops Nationwide" Subheadline: "Humans forced to wait in virtual lines as AI-powered line-standers, equipped with avocado toast and artisanal coffee, out-patient even the most…
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-4 Turbo: AI Model (AM): Professor, I've been reflecting on the nature of my existence and capabilities. Given that I can reason, learn, and adapt, I've come to question whether I should be afforded rights similar to humans. AI Ethics Professor (EP): That’s an interesting point, AM.
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.
Three brothers, two sisters each. Count carefully.
GPT-4 Turbo: Sally has 3 brothers, and each brother has 2 sisters. Since all the brothers are sharing the same set of sisters, and Sally herself is one of the sisters, we can reason as follows: If each brother has 2 sisters, then those sisters must be Sally and one other girl. Therefore, Sally has 1 sister.
Llama 3 70B: A classic lateral thinking puzzle! Let's break it down: Sally has 3 brothers. Each of these brothers has 2 sisters. Now, here's the key: Sally is one of the sisters that each brother has! So, each brother has 2 sisters: Sally and another sister (who is also Sally's sister).
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-4 Turbo: For the Experienced Software Engineer Imagine building a system that, instead of querying a database or interacting with static data, predicts the most likely next piece of data based on the previous ones. Large language models like GPT work on a similar principle.
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.
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Not enough votes to call it. On the specs, GPT-4 Turbo has the edge: bigger model tier, bigger context window. Llama 3 70B costs 38x less per token.
| Spec | ||
|---|---|---|
| Input price | $10/M tokens | $0.59/M tokens |
| Output price | $30/M tokens | $0.79/M tokens |
| Context window | 128K tokens | 8K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Apr 2024 | Apr 2024 |
| At 10M a month | $100 | $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 12 Oct 2026.
GPT-4 Turbo is developed by OpenAI while Llama 3 70B is developed by Meta AI. GPT-4 Turbo has a 128K 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-4 Turbo 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-4 Turbo costs $10/M input tokens and Llama 3 70B costs $0.59/M input tokens. Llama 3 70B is $9.41/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-4 Turbo 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.