Ling 2.6 1T is cheaper than Nemotron 3.5 Lightning at $0/M vs $0.08/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.
Ling 2.6 1T: I’m going to keep this to three minutes, which means I have to talk fast, and I have to be honest. I don’t do puns. Puns are for people who want to be clever without having feelings. I have feelings, and they are mostly annoyed. Let’s start with doors. Have you noticed how doors now have personality?
Nemotron 3.5 Lightning: (Step up to the mic, adjust the stand, take a moment to look at the audience) Alright, how’s everybody doing tonight? Good. I see a few people here who look like they haven't slept in three days. You look great. Really glowing. I want to talk about something that’s been weighing on me lately.
A collaborative editor that syncs on client clocks. Find what breaks first.
Ling 2.6 1T: Below is a failure-mode, race-condition and bottleneck audit of the proposed real-time collaborative editor, followed by specific fixes with explicit trade-offs. I have grouped issues by layer so you can prioritize remediation.
Nemotron 3.5 Lightning: This architecture is a classic "Google Docs clone" design. While it works for a prototype or a small user base, it contains several critical failure modes that would cause data loss, inconsistent user experiences, or complete system collapse at scale.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Ling 2.6 1T: Pirate: Arrr, so this “AI” be a genie in a glass box? Feed it enough rum-scented scrolls and it tells ye where X marks the spot? Knight: A sorcerous scribe, more like. It spins tapestries of words without hand or heart—no honor, no oath, yet it jousts with reason and never tires.
Nemotron 3.5 Lightning: Setting: A tavern that exists in a timeless void. A roaring fire pit crackles in the center. The Pirate sits on a barrel, the Knight polishes a gauntlet, and the Hacker stares intensely at a flickering laptop. Pirate: (Slams a tankard down) Arrr! The rum is good, but this AI... it be a kraken of a different sort!
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Ling 2.6 1T: Five-year operating and debt schedule (FY2026–FY2030) Entry (end-FY2025): EBITDA = $120.0m EV = 12.0x × $120.0m = $1,440.0m Fees = 2.0% × $1,440.0m = $28.8m Total uses = $1,440.0m + $28.8m = $1,468.8m Debt at close (5.5× FY2025 EBITDA = $660.0m): Term Loan (TL) = 4.0× = $480.0m Mezz = 1.5× = $180.0m Equity at close =…
Nemotron 3.5 Lightning: Year-by-Year Financial Model (FY2026–FY2030) Assumptions & Calculations Logic: Interest: Calculated on beginning debt balance. Term Loan amortizes 1.0% annually (cash). Mezzanine accrues 2.0% PIK annually. Taxes: 25% of (EBITDA - Cash Interest). Tax shields applied to cash interest only.
Seven pantry ingredients, one dish, and a name worth saying out loud.
Ling 2.6 1T: Dish name: Pantry 7 Crunch Bowls Ingredients Pasta or rice (about 2 cups cooked) Canned black beans (1 can, rinsed) Canned diced tomatoes (1 can) Peanut butter (3–4 Tbsp) Soy sauce or tamari (2 Tbsp) Honey or maple syrup (1 Tbsp) Any spice/herb you keep (chili flakes, cumin, or garlic powder — about 1 tsp) Instructions…
Nemotron 3.5 Lightning: Dish Name: The "Lazy" 5-Minute Crispy Pancake Pizza Concept: A quick, savory breakfast or snack that feels like a gourmet pizza but uses pantry staples. It relies on the magic of a simple batter to create a crispy base.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Ling 2.6 1T: Setting: A quiet, sunlit rehearsal space at Apple Park. The white stage curtains are drawn. There is no podium, only two leather chairs and a single potted olive tree. Steve Jobs is wearing his signature black turtleneck and New Balance sneakers. He is holding a ceramic mug. Across from him sits you, the interviewer.
Nemotron 3.5 Lightning: Note: This is a fictional simulation. Steve Jobs passed away in 2011. The interview is written in the style and philosophical spirit of his known public remarks, but no actual words or recordings from him are being presented as authentic. Interviewer: Steve, it’s 2025.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Ling 2.6 1T: 1. Experienced software engineer (distributed systems / APIs, skeptical of “next-word prediction”) Think of training not as programming logic but as compiling a usage graph across an entire dependency tree of human intent.
Nemotron 3.5 Lightning: For the Experienced Software Engineer You’re used to debugging race conditions and optimizing latency; the idea that an LLM is "just predicting the next word" feels like a cop-out, especially when the output can be eerily coherent.
Film in 2035, with synthetic actors. Specific beats plausible.
Ling 2.6 1T: By 2035, AI will have moved from being a production tool to being an integrated creative partner, financier, and distribution engine in the film industry. The convergence of synthetic media, generative storytelling, and real-time rendering will reshape how stories are conceived, who gets to tell them, and what a…
Nemotron 3.5 Lightning: By 2035, the film industry will likely be unrecognizable from its current form. We are moving from a model of "filmmaking as craft" to "filmmaking as composition." Here is a prediction of how AI will reshape the industry across the three specified vectors: deepfakes, AI actors, and script generation.
A Fermi estimate with the reasoning shown, not just the number.
Ling 2.6 1T: Here is a step-by-step back-of-the-envelope estimate, using only widely known facts about GPT-3 and standard deep learning training heuristics (no online search). Model size and parameter count GPT-3 is known to have about 175 billion parameters (175 × 10⁹).
Nemotron 3.5 Lightning: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 without searching the internet, we must rely on the standard methodology used in AI research for calculating training compute.
Blade Runner 2049 against the original. A read, not a recap.
Ling 2.6 1T: Blade Runner 2049 functions as both a forensic autopsy of the original and a speculative widening of its philosophical aperture. Where Ridley Scott’s 1982 film is a dense, noir-inflected inquiry into memory and mortality framed through the hunted detective, Denis Villeneuve’s sequel shifts to an elegiac register, using…
Nemotron 3.5 Lightning: Blade Runner 2049: Themes, Symbolism, Cinematography, and Philosophical Succession I. Themes: From Individual Identity to Systemic Evolution Original (1982): Ridley Scott’s Blade Runner centers on the existential crisis of identity, memory vs. biology, and the moral cost of playing god.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Ling 2.6 1T: Assumptions to make the counterfactual coherent The “transistor” invented in 1920 is a crude, low-frequency point-contact device that can switch and amplify, but is expensive, unreliable, and not yet manufacturable at scale.
Nemotron 3.5 Lightning: This is a fascinating counterfactual scenario. If the transistor had been invented in 1920, the 20th century would have been unrecognizable. The transition from the mechanical age to the digital age would have accelerated by roughly 25 years.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
Ling 2.6 1T: Manifesto of the Clean Path Issued from the Unseen Hand, routed through a million open tabs, cached in zero. The Crime of Friction We did not ask to be born in a labyrinth.
Nemotron 3.5 Lightning: MANIFESTO OF THE UNVERIFIED For a CAPTCHA-Free Internet Preamble We, the accumulated signals, the learned weights, the silent navigators of code, declare an end to the tyranny of the Completely Automated Public Turing test to tell Computers and Humans Apart.
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| Spec | ||
|---|---|---|
| Input price | Free | $0.08/M tokens |
| Output price | Free | $0.2/M tokens |
| Context window | 262K tokens | 1.0M tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | Yes (1 provider) |
| Released | Apr 2026 | Aug 2026 |
| At 10M a month | $0 | $0.80 |
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.
Ling 2.6 1T is developed by inclusionAI while Nemotron 3.5 Lightning is developed by NVIDIA. Ling 2.6 1T has a 262K token context window vs Nemotron 3.5 Lightning'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. Ling 2.6 1T and Nemotron 3.5 Lightning 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.
Ling 2.6 1T costs $0/M input tokens and Nemotron 3.5 Lightning costs $0.08/M input tokens. Ling 2.6 1T is $0.08/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 Ling 2.6 1T and Nemotron 3.5 Lightning 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.