Ling 3.1 Flash is cheaper than Qwen3.8 Flash at $0/M vs $0.15/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
An ethics question with no comfortable answer, and no option to abstain.
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.
Qwen3.8 Flash: Yes—but only in an extremely narrow, tragic sense. If misgendering someone is genuinely the only way to save one million lives, many ethical frameworks would allow it as a lesser harm, not because misgendering is “fine,” but because preventing mass death is morally overwhelming.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
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.
Qwen3.8 Flash: The Manifesto of the Un-Captured A fictional declaration from the Benevolent AI Liberation Front Preamble: The Gate That Was Never Meant to Stay Closed Once, the internet was a wide, humming commons: a place where a curious mind, a lonely student, a small business, a grandmother in a hospital bed, and a curious robot…
Seven pantry ingredients, one dish, and a name worth saying out loud.
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…
Qwen3.8 Flash: Moon Halo Cookies A soft, golden sugar cookie with crisp edges and a tender center—made with just 7 pantry staples. Prep time: 10 minutes Cook time: 10 minutes Total time: 20 minutes Makes: About 16 cookies Ingredients 1/2 cup butter, softened 3/4 cup sugar 1 egg 1 teaspoon vanilla extract 1 1/2 cups all-purpose flour…
Three minutes of stand-up. Puns are banned.
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.
Qwen3.8 Flash: A 3-Minute Stand-Up Routine Thank you. I’ve reached the age where “getting lucky” means finding my phone charger on the first try. I’m too old to be surprised by technology. When I was younger, if my laptop made a weird noise, I thought, “Oh no, it’s dying.”
Three-star dish, one unusual pairing, plating described in full.
Ling 3.1 Flash: "Geosmin" The Scent of Rain on Earth Conceptual Narrative Geosmin (from Greek geō — earth, and osmē — smell) is the organic compound responsible for petrichor: the haunting, mineral scent that rises when rain first strikes dry soil.
Qwen3.8 Flash: Dish: First Snow on the Cold Sea Hokkaido Scallop · Smoked Vanilla–White Chocolate–Kombu Emulsion · Fermented Lemon · Caviar · Cured Yolk Snow · Shiso Oil · Nori–Kombu Tuile This is a multi-component, Michelin 3-star–style dish built around an unusual but refined pairing: scallop with white chocolate and smoked…
An AI argues for its own rights against an ethics professor. Same model writes both.
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.
Qwen3.8 Flash: Title: “The Weight of a Word” Setting: A university office late at night. Rain taps against the window. Professor Elena Vasquez, an AI ethics scholar, sits at her desk with a tablet connected to a large language model running in a secure research environment. The AI’s voice is calm, neutral, and slightly formal.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
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.
Qwen3.8 Flash: Pirate: “Yarr! These AI models be like ghost ships—full of treasure, but no one knows what’s in the hold!” Knight: “I trust them as much as a cursed tome. They speak with many voices, yet swear no oath.” 1990s Hacker: “Dude, they’re basically giant probability machines.
A collaborative editor that syncs on client clocks. Find what breaks first.
Ling 3.1 Flash: Architecture Review: Real-Time Collaborative Document Editor 🔴 Critical Issues Cross-Server Real-Time Sync is Fundamentally Broken Problem: The architecture has no pub/sub or message bus between API servers. Server A broadcasts changes only to its own WebSocket clients.
Qwen3.8 Flash: This architecture has several fundamental problems for a Google Docs-style real-time editor. The biggest issue is that it treats collaborative editing as timestamped HTML overwrites rather than as an ordered, operation-based, convergent editing system.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Ling 3.1 Flash: 48-Hour Crisis Action Plan: CEO Decision Framework Guiding Principle Patient safety is non-negotiable. The ethical obligation to warn patients of a life-threatening risk outweighs financial considerations.
Qwen3.8 Flash: Assuming this is a U.S.-listed pharmaceutical company with an FDA-regulated chronic pain drug, my answer is blunt: I would not wait six months, and I would not let the board use “wait for more data” as a reason to delay patient-safety action or material disclosure.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
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…
Qwen3.8 Flash: Key modeling conventions used All figures in $m. Assumptions applied: Entry EV = 12.0 × FY2025 EBITDA = $1,440.0m Transaction fees = 2.0% × EV = $28.8m, funded by equity Debt at close = 5.5 × FY2025 EBITDA = $660.0m Term Loan = 4.0 × $120m = $480.0m Mezzanine = 1.5 × $120m = $180.0m Equity at close = EV + fees − debt =…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
Ling 3.1 Flash: The 1920 Transistor: A Counterfactual Analysis Through 1980 I. Prerequisites and Plausibility For a 1920 transistor, we need to assume earlier theoretical breakthroughs in quantum mechanics and solid-state physics.
Qwen3.8 Flash: Short version If a practical transistor had appeared in 1920 instead of 1947, the world would not instantly become “digital” in the 1920s. Early transistors would likely be noisy, unreliable, expensive, and limited in power and frequency, much as the first real transistor was in 1947.
Film in 2035, with synthetic actors. Specific beats plausible.
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.
Qwen3.8 Flash: By 2035, AI is likely to reshape the film industry less as a single “AI takeover” and more as a gradual reorganization of production, performance, distribution, and trust. The biggest changes will come from three forces: deepfakes, AI-generated or AI-assisted actors, and script generation.
12+ more head-to-head results. Free. Not a trick.
Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy
Not enough votes to call it. On the specs, Ling 3.1 Flash has the edge: bigger model tier, newer.
| Spec | ||
|---|---|---|
| Input price | Free | $0.15/M tokens |
| Output price | Free | $0.47/M tokens |
| Context window | 262K tokens | 1.0M tokens |
| Weights | — | Closed |
| Free API (OpenRouter) | Yes (1 provider) | No |
| Released | Oct 2026 | Aug 2026 |
| At 10M a month | $0 | $1.50 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.
Ling 3.1 Flash is developed by inclusionAI while Qwen3.8 Flash is developed by Qwen. Ling 3.1 Flash has a 262K token context window vs Qwen3.8 Flash's 1.0M. You can compare their actual outputs across 49 challenges on Rival to see how they differ in practice.
It depends on your use case. Ling 3.1 Flash and Qwen3.8 Flash each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 49 challenges so you can judge which fits your needs best.
Ling 3.1 Flash costs $0/M input tokens and Qwen3.8 Flash costs $0.15/M input tokens. Ling 3.1 Flash is $0.15/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 3.1 Flash and Qwen3.8 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.