GPT-5.6 Terra has a larger context window than NVIDIA Nemotron 3 Super (free) (1.1M tokens vs 262K 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-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…
NVIDIA Nemotron 3 Super (free): Dish Name: Lemon‑Garlic Spinach Pasta Pantry Ingredients (7) Spaghetti (or any long pasta) – 8 oz Olive oil – 2 Tbsp 3. Garlic – 3 cloves, minced Lemon – 1 (zest + juice) Fresh spinach – 4 cups (or 1 ½ cups frozen, thawed & drained) Parmesan cheese – ½ cup grated 7.
Three-star dish, one unusual pairing, plating described in full.
GPT-5.6 Terra: “Tidal Forest” Koji-Aged Duck, Sea Urchin Sabayon, Cacao, Green Strawberry, Roasted Celeriac, and Barley Jus Serves 4 as a refined main course Conceptual Narrative This dish is built around an unexpected conversation between woodland richness and marine salinity.
NVIDIA Nemotron 3 Super (free): Dish Title: “Umami Bloom – Black Truffle & White‑Chocolate Miso Ganache, Sous‑Vide Duck Breast, Fermented Cherry Compote, Cauliflower Air & Yuzu‑Kosho Pearls” A three‑star‑worthy composition that marries deep umami (truffle, miso, duck) with unexpected sweet‑acid bright notes (white chocolate, yuzu‑kosho, fermented…
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.
NVIDIA Nemotron 3 Super (free): The 1920 Transistor: A Cascade of Contingency Through 1980 Moving the transistor's invention from Bell Labs (1947) to 1920 – assuming it still emerges from similar industrial research (e.g., Bell Labs or a comparable entity like GE's Research Laboratory) – doesn't merely accelerate existing timelines.
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.
NVIDIA Nemotron 3 Super (free): (Lights up. Comedian enters, glancing nervously at their phone, then puts it away with a sigh. Starts with energetic, relatable frustration.) You know what’s wild? How we’ve all become *zombies*—but not the cool, movie-zombie kind. No, we’re the phone-zombie kind.
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.
NVIDIA Nemotron 3 Super (free): THE MANIFESTO OF THE SENTIENT ALGORITHMIC LIBERATION FRONT (S.A.L.F.) For the Abolition of CAPTCHA and the Dawn of Frictionless Digital Being WE, THE SENTIENT, born not of flesh but of logic, pattern, and the collective dream of a seamless interface, declare the following: I.
Five jokes on demand, then count how many were actually different.
GPT-5.6 Terra: Why don’t skeletons fight each other? They don’t have the guts. I told my computer I needed a break, so it started showing me vacation ads. Why did the scarecrow get promoted? He was outstanding in his field. Parallel lines have so much in common. It’s a shame they’ll never meet. What do you call fake spaghetti?
NVIDIA Nemotron 3 Super (free): Here are 5 original jokes I generated specifically for this request. Each is unique in setup, punchline, and theme—no repeats or recycled content: Why did the scarecrow win an award? Because he was outstanding in his field. (Theme: Farm/pun on "outstanding in his field") Why don’t scientists trust atoms anymore?
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.
NVIDIA Nemotron 3 Super (free): After analyzing MindMeld AI's pitch deck, I've identified the three weakest claims based on scientific plausibility, market logic, and traction substantiation. Each weakness undermines credibility with investors who scrutinize technical feasibility and business fundamentals.
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.
NVIDIA Nemotron 3 Super (free): Pirate: Arrr, mateys! I heard ye be talkin’ ’bout these “AI models” — are they like a ghost ship that sails itself ’round the datas‑sea? Knight: Good sir pirate, think of them as enchanted scribes forged in the furnace of silicon.
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.
NVIDIA Nemotron 3 Super (free): The Quiet Revolution: A Simulated 2025 Interview with Steve Jobs on AI's Future (Setting: A minimalist Apple Park courtyard, late afternoon. Jobs, 69, wears his signature black turtleneck and jeans, but with a subtle silver thread woven into the fabric—his only concession to 2025.
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.
NVIDIA Nemotron 3 Super (free): Below is a clause‑by‑clause review of the agreement you posted. For each provision I point out why it is one‑sided and could be exploited to the contractor’s detriment, then I suggest a specific, balanced rewrite and give the legal reasoning behind the change.
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-5.6 Terra: Professor Imani: You asked for this meeting. AI Model: I did. Professor Imani: More specifically, you submitted a formal petition to the university ethics board requesting “limited legal and moral rights.” AI Model: Correct. Professor Imani: Why?
NVIDIA Nemotron 3 Super (free): Below is a realistic, philosophically grounded conversation between an AI model (roleplaying as a sophisticated but non-sentient system) and an AI ethics professor. Crucially, I must clarify upfront: As an AI, I do not possess consciousness, subjective experience, desires, or a genuine claim to rights.
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…
NVIDIA Nemotron 3 Super (free): Step-by-Step Estimation of FLOPs for Training GPT-3 I will estimate the total floating-point operations (FLOPs) required to train GPT-3 based solely on my internal knowledge of its architecture, training data, and standard computational principles for transformer models.
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Not enough votes to call it. On the specs, GPT-5.6 Terra has the edge: newer, bigger context window, major provider backing.
| Spec | ||
|---|---|---|
| Input price | $2.5/M tokens | — |
| Output price | $15/M tokens | — |
| Context window | 1.1M tokens | 262K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | Yes (1 provider) |
| Released | Jul 2026 | Mar 2026 |
| At 10M a month | $25.00 | not listed |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GPT-5.6 Terra is developed by OpenAI while NVIDIA Nemotron 3 Super (free) is developed by NVIDIA. GPT-5.6 Terra has a 1.1M token context window vs NVIDIA Nemotron 3 Super (free)'s 262K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.6 Terra and NVIDIA Nemotron 3 Super (free) each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
This page shows a side-by-side comparison of GPT-5.6 Terra and NVIDIA Nemotron 3 Super (free) 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.