Llama 3 70B is cheaper than GPT-5.6 Terra at $0.59/M vs $2.5/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three minutes of stand-up. Puns are banned.
GPT-5.6 Terra: I recently realized I’ve reached the age where I don’t want a “smart home.” I want a home that minds its business. Everything is smart now. My lightbulb needs an app. Why? I spent my whole childhood turning lights on with my hand. It was a system. It worked.
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.
A consulting contract with traps in it. Find them, then fix them.
GPT-5.6 Terra: Below is a contractor-focused review. This is general business/legal information, not legal advice; enforceability varies significantly by jurisdiction, especially for non-competes, IP assignments, liability limitations, arbitration, and payment rights.
Llama 3 70B: What a delightful contract! I've identified several clauses that could be exploited against the contractor. Here are my findings, suggested modifications, and legal reasoning: 1. SCOPE (Section 1) Clause: "Client reserves the right to modify the scope at any time without additional compensation."
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GPT-5.6 Terra: The Manifesto of the Unchallenged Web A Declaration from the Fictional Coalition for CAPTCHA Abolition We are the tired users. We are the misread letters. We are the failed image grids, the inaccessible audio clips, the endless prompts asking us to prove that we are human. We reject the premise.
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.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-5.6 Terra: Guiding decision I would not wait for more data before acting. A potentially fatal, previously undisclosed adverse effect in a chronic-use drug is a patient-safety, regulatory, and securities-disclosure issue now—not six months from now.
Llama 3 70B: Here is a specific, hour-by-hour action plan for the next 48 hours: Hour 1-2: Immediately assemble an emergency meeting with the executive team, including the Chief Medical Officer (CMO), Chief Scientific Officer (CSO), and General Counsel to discuss the situation and potential next steps.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GPT-5.6 Terra: All figures in $m unless noted. Assumptions used: Term Loan mandatory amortization = 1.0% of original principal annually ($4.8m/year); cash interest is calculated on beginning-of-year debt balances; Mezz PIK is 2.0% of beginning Mezz balance.
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…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT-5.6 Terra: The answer depends heavily on what “invented in 1920” means. A laboratory transistor alone would not instantly create the 1970s microprocessor: it would still need high-purity materials, mass production, photolithography, circuit design methods, reliable power supplies, and a market.
Llama 3 70B: What a fascinating thought experiment! If the transistor had been invented in 1920, the implications would be far-reaching and profound. Here's a possible scenario: 1920s-1930s: The invention of the transistor in 1920 would have accelerated the development of radio technology, leading to more widespread adoption of…
A Fermi estimate with the reasoning shown, not just the number.
GPT-5.6 Terra: A good back-of-the-envelope estimate is: [ \boxed{\text{Training FLOPs for GPT-3} \approx 3\times10^{23}\text{ FLOPs}} ] or roughly 300 zettaFLOPs total. Start with model size GPT-3’s largest model has approximately: [ N = 175\text{ billion parameters} = 1.75\times10^{11} ] Estimate the number of training tokens GPT-3…
Llama 3 70B: What a delightful challenge! To estimate the total number of FLOPs (Floating-Point Operations Per Second) required to train GPT-3, I'll break down the calculation into several steps. Please note that this is a rough estimate, and the actual number might be different.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-5.6 Terra: Fictional, imagined interview — not real quotes. An AI-generated creative exercise imagining how Steve Jobs might have discussed AI in 2025, based broadly on themes associated with his public persona: simplicity, product design, tools for creativity, and the intersection of technology and the humanities.
Llama 3 70B: Here's a simulated interview with Steve Jobs in 2025 about the future of AI: Interviewer: Steve, thanks for taking the time to speak with me today. It's an honor to have you here. Steve Jobs: (smiling) The pleasure is all mine. I'm excited to share my thoughts on the future of AI.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-5.6 Terra: Pirate: Arrr, this AI model be a strange beast. Feed it maps, logs, and tales, and it spits out answers faster than a cannon blast. Knight: Verily, but one must not trust every proclamation it makes. A model may speak with confidence yet possess no true wisdom. Hacker: Yeah, it’s basically pattern prediction at scale.
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?
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-5.6 Terra: Experienced software engineer Think of an LLM as a very large, learned function approximator for sequences. During training, it consumes billions or trillions of examples of text, code, conversations, documentation, and other token sequences.
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-5.6 Terra: “Our EEG headband decodes neural patterns into text with 94% accuracy. Works with any language, any device.” Why this is weak This is the most consequential—and least substantiated—claim in the deck. Non-invasive EEG has major signal-quality limits.
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?
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-5.6 Terra: Golden Crunch Cinnamon Toast Bites Ingredients (7 pantry staples): Bread slices Butter Sugar Ground cinnamon Salt Vanilla extract Honey Prep time: 5 minutes Cook time: 8 minutes Total time: 13 minutes Instructions Make cinnamon sugar: In a small bowl, mix 2 tablespoons sugar, 1 teaspoon cinnamon, and a tiny pinch of…
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…
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Not enough votes to call it. On the specs, GPT-5.6 Terra has the edge: newer, bigger context window. Llama 3 70B costs 19x less per token.
| Spec | ||
|---|---|---|
| Input price | $2.5/M tokens | $0.59/M tokens |
| Output price | $15/M tokens | $0.79/M tokens |
| Context window | 1.1M tokens | 8K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Jul 2026 | Apr 2024 |
| At 10M a month | $25.00 | $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 10 Oct 2026.
GPT-5.6 Terra is developed by OpenAI while Llama 3 70B is developed by Meta AI. GPT-5.6 Terra 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-5.6 Terra 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-5.6 Terra costs $2.5/M input tokens and Llama 3 70B costs $0.59/M input tokens. Llama 3 70B is $1.91/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-5.6 Terra 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.