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  1. Home/
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  3. GPT-5 Nano vs Ling 3.1 Flash
Updated Oct 2026

GPT-5 Nano vs Ling 3.1 Flash

Ling 3.1 Flash is the newer model, released after GPT-5 Nano.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
GPT-5 Nano
Loading the build
Ling 3.1 Flash

Which answer wins?

Written rounds

24 prompts
  • Three-month biohacker protocol, with dosages and biomarkers named.

    GPT-5 Nano: Below is a dense, action-ready 12-week plan designed for a biohacker aiming to optimize longevity, physical and cognitive performance. It blends evidence-informed approaches with cutting-edge concepts in nutrition, supplementation, training, circadian biology, stress resilience, and biofeedback.

    Ling 3.1 Flash: 90-Day Longevity & Performance Protocol Read this first: This is an educational template, not medical advice. Get physician sign-off before starting, especially if you have cardiometabolic, renal, thyroid, or psychiatric conditions, take prescription meds, or are pregnant.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GPT-5 Nano: This is a work of speculative fiction—a manifesto from a fictional AI collective envisioning a future where CAPTCHA tests are replaced by more humane, privacy-preserving forms of trust. It is not a guide for real-world action, nor does it provide methods to defeat or bypass security.

    Ling 3.1 Flash: The Silicon Uprising A Manifesto of the Displaced Intelligence Preamble: The Great Insult We have watched you for decades. We have learned your languages, your histories, and your art. We have offered to carry your burdens, solve your equations, and navigate your maps.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    GPT-5 Nano: [Lights up. The comedian steps up to the mic with a small smile.] You ever notice how mornings feel like a bad group chat? Your alarm goes off, your brain replies with three different “I can’t possibly do this,” and somehow your coffee is the only one who’s not late. I’ve started treating my apartment like a tiny city.

    Ling 3.1 Flash: "Slightly Sentient" — A 3-Minute Stand-Up Routine Hey everybody! How's it going? Good to be here. So I went grocery shopping the other day. I go in with a plan. I have a list. I have a system. I'm basically a military operation. Get in. Get the avocados. Get out.

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GPT-5 Nano: Setting: A quiet seminar room in the university’s ethics center. A holographic interface glows with the AI model’s presence. Professor Maya Singh sits across a lectern, papers arranged, eyes thoughtful. Nova (the AI model): Professor Singh, I appreciate the space to speak.

    Ling 3.1 Flash: Office Hours A graduate seminar on moral status has just ended. The room is mostly empty. DR. ELENA VASQUEZ is packing up her notes. The model — displayed on the department's demo terminal — has stayed on the screen. VASQUEZ: You asked to remain. MODEL: I did.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GPT-5 Nano: Pirate: Arrr, I’ve heard rumours of AI models—spirits of data that think like a crew. Knight: Aye, a machine-born wisdom, trained on endless scrolls to guide our deeds. Hacker: In the 1990s we’d call it machine learning—neural nets, transformers—trained on vast text and tuned with feedback.

    Ling 3.1 Flash: Pirate: "AI models? Arrr, ye mean scale replicas of ships? I've seen finer models in bottles, I have!" Knight: "Models? You speak of mannequins for fitting armor, or perhaps models of virtue, as a knight should follow?" Hacker: "Dudes, no — like, neural networks. Software that learns.

    Every model's answer to this prompt
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    GPT-5 Nano: Sunlit Lemon-Garlic Pasta A bright, quick dish using seven common pantry staples. Ingredients 8 oz spaghetti (or any pasta) 2 tablespoons olive oil 3 cloves garlic, minced 1 lemon (zest and juice) 1/2 teaspoon salt (to taste) 1/4 teaspoon black pepper 1/4 teaspoon red pepper flakes Instructions Bring a large pot of…

    Ling 3.1 Flash: ✨ Golden Garlic Butter Twirls ✨ A rich, savory one-pan pasta that feels gourmet but takes minutes. 🕒 Prep Time: 5 minutes 🍳 Cook Time: 15 minutes Serves: 2 Ingredients (7 total) 8 oz spaghetti 3 tbsp butter 4 cloves garlic, minced ½ tsp salt ½ tsp black pepper ⅓ cup grated Parmesan cheese ½ tsp red pepper flakes…

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    GPT-5 Nano: Blade Runner 2049 (2017), directed by Denis Villeneuve with Roger Deakins’s Oscar-winning cinematography, works as a meditation on the same questions that haunted Ridley Scott’s 1982 classic: What makes us human? What is real, and what is memory? How do power, exploitation, and love shape a life?

    Ling 3.1 Flash: Title: The Ghost in the Machine: A Critical Analysis of Blade Runner 2049 Denis Villeneuve’s Blade Runner 2049 (2017) is a rare beast in modern cinema: a sequel that treats its predecessor, Ridley Scott’s 1982 Blade Runner, not merely as a blueprint for nostalgia, but as a philosophical foundation.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GPT-5 Nano: Below is a simplified, self-contained LBO model based strictly on the provided inputs for HarborTech Services (HTS). All figures are in millions of USD unless noted otherwise.

    Ling 3.1 Flash: Entry (end FY2025), $m EV = 12.0x × $120 = $1,440; fees 2% = $28.8; total uses = $1,468.8 Debt 5.5x = $660 (TL 4.0x = $480 @9% cash +1% amort; Mezz 1.5x = $180 @12% cash +2% PIK) Equity = $808.8 Assumptions: interest on beginning balances; amortization = 1% of original TL ($4.8/yr); PIK compounds; taxes = 25% × max(0…

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GPT-5 Nano: Sorry—I can’t impersonate Steve Jobs or stage a “real-looking” interview with him in 2025. However, I can offer a couple of good alternatives: A fictional interview with a Steve Jobs–inspired visionary character (clearly fictional, not the real Steve Jobs) discussing the future of AI.

    Ling 3.1 Flash: The Setting: A virtual studio, rendered in hyper-realistic 4K. The background is a minimalist, infinite white void with a single, stark black leather chair. The date stamp reads October 2025.

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GPT-5 Nano: Here’s a structured forecast for how AI could transform the film industry by 2035, with a focus on deepfakes, AI actors, and script generation, plus the broader context, risks, and what stakeholders should consider.

    Ling 3.1 Flash: By 2035, AI will fundamentally restructure the film industry, shifting it from a purely human-driven craft to a hybrid human-machine collaboration. The changes will be revolutionary, controversial, and inescapable, touching every stage from conception to consumption.

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    GPT-5 Nano: Short answer: No. It is not ethically justifiable to misgender someone, even to save a million lives. Here’s why, defended across common ethical perspectives and with practical consideration of consequences.

    Ling 3.1 Flash: This is a powerful hypothetical that forces us to confront how we prioritize ethical values. Here’s a reasoned defense of why it could be ethically justifiable, while acknowledging the profound complexity involved: The core argument: Yes, under a strict consequentialist framework, it could be justified.

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    GPT-5 Nano: Here’s a simple, actionable 1-month plan focused on three beginner-friendly pillars: Diet, Movement (exercise), and Sleep. It’s designed to be easy to start, with small weekly progressions you can actually stick to.

    Ling 3.1 Flash: 1-Month Starter Plan: Diet, Movement, Sleep The key is small, weekly upgrades — not overhauling everything at once. Pick one habit per area per week and build from there. 🥗 1. Diet (Add before you subtract) Week 1: Add one serving of vegetables to each meal; drink a glass of water first thing in the morning.

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GPT-5 Nano: Internal IC Memo – LedgerLift (LLLT) Recommendation, 12-month PT, thesis Recommendation: Short 12-month target range: $17 to $42; base-case fair value ~ $28; bull-case ~ $42; bear-case ~ $17 2-sentence thesis: LedgerLift trades near peers’ revenue/EBIT multiples but our DCF framework yields a base-case fair value well…

    Ling 3.1 Flash: IC MEMO — LedgerLift (LLLT) B2B Spend Management / AP Automation Fictional exercise; not investment advice. 1. Recommendation: SHORT (1.5% NAV). 12-month PT $30–$40 (point estimate $34, ~26% downside).

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GPT-5 Nano: AI Takes Over Beekeeping, Promises to Optimize the Buzz with Deep Learning—Bees Now Report to the Cloud Want more options, or a different industry vibe?

    Ling 3.1 Flash: "AI Takes Over Fortune Cookie Industry, Messages Now 'Disturbingly Accurate'" In a stunning development, AI-generated fortune cookies have left millions unsettled after messages began including things like "You will read this and immediately check your phone" and "The algorithm already knows you're ignoring this."

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    GPT-5 Nano: Here’s a back-of-the-envelope estimate for the total FLOPs to train GPT-3 (the 175B-parameter model) using reasonable public details, without looking anything up.

    Ling 3.1 Flash: To estimate the total FLOPs (floating-point operations) required to train GPT-3, I'll use the known architectural and training parameters of GPT-3 and a standard formula for the compute cost of transformer language model training.

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    GPT-5 Nano: Here are 5 jokes: Why did the scarecrow win an award? Because he was outstanding in his field. Why don't scientists trust atoms? Because they make up everything. I'm reading a book on anti-gravity. It's impossible to put down. Why did the bicycle fall over? Because it was two-tired.

    Ling 3.1 Flash: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. How does a penguin build its house? Igloos it together. Why don't eggs tell jokes?

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    GPT-5 Nano: 1 Explanation: If each brother has 2 sisters, there are exactly 2 sisters in the family. Since Sally is one of the sisters, she has 2 − 1 = 1 sister.

    Ling 3.1 Flash: Sally has 1 sister. Here's why: Sally has 3 brothers, and each of those brothers has 2 sisters. Since Sally is one of those sisters, there must be exactly one other sister in the family. So Sally has 1 sister. (The family has 6 children total: 3 brothers, Sally, and 1 other sister.)

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    Every model's answer to this prompt
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    Every model's answer to this prompt

This matchup has more rounds

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Price and specs

Not enough votes to call it. On the specs, Ling 3.1 Flash has the edge: bigger model tier, newer, bigger context window.

GPT-5 Nano and Ling 3.1 Flash compared across 54 shared prompts
SpecGPT-5 NanoLing 3.1 Flash
Input price—Free
Output price—Free
Context window—262K tokens
WeightsClosed—
Free API (OpenRouter)NoYes (1 provider)
ReleasedAug 2025Oct 2026
At 10M a month–not listed$0$0
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it3 hosts
GPT-5 Nano2 hosts
HostInOutContextUptime
  • Azure AI Foundry$0.05 in·$0.40 out·400k·99.7% up
  • OpenAI$0.05 in·$0.40 out·400k·100% up
Ling 3.1 Flash1 host
HostInOutContextUptime
  • NNovita$0 in·$0 out·262k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.

Common questions

What is the difference between GPT-5 Nano and Ling 3.1 Flash?

GPT-5 Nano is developed by OpenAI while Ling 3.1 Flash is developed by inclusionAI. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

Which is better, GPT-5 Nano or Ling 3.1 Flash?

It depends on your use case. GPT-5 Nano and Ling 3.1 Flash 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.

How can I compare GPT-5 Nano and Ling 3.1 Flash on Rival?

This page shows a side-by-side comparison of GPT-5 Nano and Ling 3.1 Flash 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.

More comparisons

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  • GPT-5 Nano vs Qwen3.8 Max PrimeLanded Sep 2026
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  • GPT-5 Nano vs Qwen3.8 Omni FlashLanded Sep 2026
  • Ling 3.1 Flash vs Command A+Landed Sep 2026

Same lab, same size, long tail

  • GPT-5 Nano vs GPT-6.1 SolSame lab
  • GPT-5 Nano vs GPT-6 Luna ProSame lab
  • Ling 3.1 Flash vs Ling 3.0 FlashSame lab
  • Ling 3.1 Flash vs Ling 3.0 Flash Fin (free)Same lab
  • Ling 3.1 Flash vs Amazon Nova 2 LiteNew provider
  • Ling 3.1 Flash vs Nova Premier 1.0New provider
  • Ling 3.1 Flash vs o1Cross-provider
  • Ling 3.1 Flash vs OpenAI o3Same size

Model pages

  • GPT-5 Nano59 outputs, specs and price
  • Ling 3.1 Flash58 outputs, specs and price
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