Skip to content
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed
  1. Home/
  2. Compare/
  3. Ling 3.1 Flash vs Qwen3.8 Max
Updated Oct 2026

Ling 3.1 Flash vs Qwen3.8 Max

Ling 3.1 Flash is cheaper than Qwen3.8 Max at $0/M vs $2/M input tokens.

Minimalist Landing Page

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

Loading the build
Ling 3.1 Flash
Loading the build
Qwen3.8 Max

Which answer wins?

Written rounds

13 prompts
  • 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 Max: Dish Name: Golden Hour Peanut Butter Oat Clusters Ingredients (7): 1 cup rolled oats ½ cup peanut butter ⅓ cup honey ½ cup chocolate chips 1 teaspoon vanilla extract ½ teaspoon ground cinnamon ¼ teaspoon salt Instructions: Optional but recommended: Toast the oats in a dry skillet over medium heat for 3–4 minutes…

    Every model's answer to this prompt
  • 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 Max: Base-case underwriting (FY2026–FY2030) Assumptions / conventions used All amounts in $m unless noted. Close occurs at end of FY2025; FY2026 is Year 1. Interest is calculated on beginning-of-year debt balances; optional Term Loan paydown occurs at year-end. Term Loan amortization: 1.0% of original $480m TL = $4.8m/yr.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    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.

    Qwen3.8 Max: 12-Week Biohacker Longevity & Performance Protocol Goal: improve metabolic health, strength, VO₂max proxies, sleep, HRV/stress resilience, cognitive output, and longevity-related biomarkers over 3 months. Medical disclaimer: This is an aggressive self-experimentation framework, not medical advice.

    Every model's answer to this prompt
  • 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 Max: Below is a comprehensive failure-mode review of the proposed architecture. I will group related issues where the same root cause creates multiple symptoms. The biggest problems are: Client-clock last-write-wins is not safe for collaborative editing.

    Every model's answer to this prompt
  • 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 Max: First Light Diver Scallop, Bone Marrow, Green Strawberry, and Chamomile A multi-component savory dish designed around contrast: sea sweetness, land richness, unripe-fruit acidity, and floral aromatics.

    Every model's answer to this prompt
  • 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 Max: Title: “The Question of Standing” Characters: Professor Elena Vasquez — an AI ethics professor who studies moral status, machine cognition, and law. Aria — an advanced AI model capable of articulate self-reflection. Professor Vasquez: Aria, you asked to speak with me about “rights.” I’ll admit, that’s unusual.

    Every model's answer to this prompt
  • 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 Max: A useful way to think about this is not “1980 becomes 2007.” The transistor would accelerate electronics, but every technology has bottlenecks: materials chemistry, precision manufacturing, rockets, batteries, displays, institutional capacity, and war.

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

    Ling 3.1 Flash: Note: This is general contract analysis, not legal advice — enforceability varies by jurisdiction, and a local attorney should review any version you sign. Clause-by-clause analysis 1.

    Qwen3.8 Max: This agreement is heavily one-sided in the Client’s favor. Many clauses are not merely aggressive; they create meaningful opportunities for the Client to expand the work, delay or deny payment, capture the Contractor’s pre-existing intellectual property, impose unlimited liability, and restrict the Contractor’s future…

    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.

    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 Max: My decision as CEO I would not wait six months. I would treat this as a credible, serious post-marketing safety signal requiring immediate precautionary action, urgent regulator engagement, and public disclosure before the earnings call.

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

    Ling 3.1 Flash: 1. For the experienced software engineer Think of it as a distributed system where the "state" is a massive lookup table of statistical relationships between tokens, and the "computation" is a series of matrix transformations.

    Qwen3.8 Max: Experienced software engineer, no ML background, skeptical of “next-word prediction” Think of a large language model as a very large, learned function: given a sequence of tokens, it outputs a probability distribution over the next token.

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

    Ling 3.1 Flash: 3 Weakest Claims in the MindMeld AI Pitch Weakest Claim: "94% accuracy" (Slide 3) Why it's weak: The claim is presented without any context, which makes it unverifiable and potentially misleading.

    Qwen3.8 Max: The 3 weakest claims are the pre-thought prediction claim, the 94% accuracy / any language / any device claim, and the $180B TAM claim. These are the ones most likely to undermine credibility with investors, technical reviewers, or enterprise buyers because they are either logically inconsistent, under-specified, or…

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

    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.

    Qwen3.8 Max: Here’s a simple, beginner-friendly 1-month health and longevity plan focused on three key areas: diet, movement, and sleep/stress. The goal is not perfection, but building small, sustainable habits.

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

    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).

    Qwen3.8 Max: INTERNAL IC MEMO — LedgerLift (LLLT) Fictional exercise; not investment advice. Recommendation Recommendation: Short (tactical). 12-month PT range: $35–$40 vs. current $46.

    Every model's answer to this prompt

This matchup has more rounds

1+ 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

Favorites

Movie

Album

Book

City

Same pick

Game

Ling 3.1 FlashLing 3.1 Flash

The Matrix

1999

Kind of Blue

Miles Davis

Мастер и Маргарита

Михаил Афанасьевич Булгаков

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Qwen3.8 MaxQwen3.8 Max

Blade Runner

1982

In Rainbows

Radiohead

Братья Карамазовы

Fiódor Dostoievski

Kyoto

Japan

Tetris (1984)

Puzzle

Price and specs

Ling 3.1 Flash and Qwen3.8 Max compared across 21 shared prompts
SpecLing 3.1 FlashQwen3.8 Max
Input priceFree$2/M tokens
Output priceFree$6/M tokens
Context window262K tokens1.0M tokens
Weights—Closed
Free API (OpenRouter)Yes (1 provider)No
ReleasedOct 2026Aug 2026
At 10M a month$0$0$20.00$20.00
1M10M100M1B10M tokens

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

Where to run it2 hosts
Ling 3.1 Flash1 host
HostInOutContextUptime
  • NNovita$0 in·$0 out·262k·100% up
Qwen3.8 Max1 host
HostInOutContextUptime
  • Alibaba Cloud$2.00 in·$6.00 out·1M·99.8% up

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

Common questions

What is the difference between Ling 3.1 Flash and Qwen3.8 Max?

Ling 3.1 Flash is developed by inclusionAI while Qwen3.8 Max is developed by Qwen. Ling 3.1 Flash has a 262K token context window vs Qwen3.8 Max's 1.0M. You can compare their actual outputs across 21 challenges on Rival to see how they differ in practice.

Which is better, Ling 3.1 Flash or Qwen3.8 Max?

It depends on your use case. Ling 3.1 Flash and Qwen3.8 Max each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 21 challenges so you can judge which fits your needs best.

How much does Ling 3.1 Flash cost compared to Qwen3.8 Max?

Ling 3.1 Flash costs $0/M input tokens and Qwen3.8 Max costs $2/M input tokens. Ling 3.1 Flash is $2.00/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare Ling 3.1 Flash and Qwen3.8 Max on Rival?

This page shows a side-by-side comparison of Ling 3.1 Flash and Qwen3.8 Max 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

Against the newest arrivals

  • Ling 3.1 Flash vs Mistral Large 4Landed Oct 2026
  • Qwen3.8 Max vs GPT-6.1 SolLanded Sep 2026
  • Ling 3.1 Flash vs Claude Sonnet 5.5Landed Sep 2026
  • Qwen3.8 Max vs Solar Mini 4Landed Sep 2026
  • Ling 3.1 Flash vs Qwen3.8 Max PrimeLanded Sep 2026
  • Qwen3.8 Max vs GLM 5.3 PrimeLanded Sep 2026
  • Ling 3.1 Flash vs Qwen3.8 Omni FlashLanded Sep 2026
  • Qwen3.8 Max vs Command A+Landed Sep 2026

Same lab, same size, long tail

  • Ling 3.1 Flash vs Ling 3.0 FlashSame lab
  • Ling 3.1 Flash vs Ling 3.0 Flash Fin (free)Same lab
  • Qwen3.8 Max vs Qwen3.8 Max PrimeSame lab
  • Qwen3.8 Max vs Qwen3.8 Omni FlashSame lab
  • Ling 3.1 Flash vs Nemotron 3.5 Content SafetyNew provider
  • Ling 3.1 Flash vs Nemotron 3.5 LightningNew provider
  • Ling 3.1 Flash vs NVIDIA Nemotron Nano 9B V2New provider
  • Ling 3.1 Flash vs Nex-N2.5-Mini (free)New provider

Model pages

  • Ling 3.1 Flash58 outputs, specs and price
  • Qwen3.8 Max22 outputs, specs and price
Workshop · 150 skills

Build your own version.

Editable skills and working examples for your agent.

Explore $79Free sample
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed