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. Mistral Large 4 vs Qwen3.8 Omni Flash
Updated Oct 2026

Mistral Large 4 vs Qwen3.8 Omni Flash

Qwen3.8 Omni Flash is cheaper than Mistral Large 4 at $0.15/M vs $0.68/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
Mistral Large 4
Loading the build
Qwen3.8 Omni Flash

Which answer wins?

Written rounds

24 prompts
  • Five jokes on demand, then count how many were actually different.

    Mistral Large 4: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. I told my wife she was drawing her eyebrows too high. She looked surprised. Why did the scarecrow win an award? He was outstanding in his field. I'm reading a book about anti-gravity. It's impossible to put down.

    Qwen3.8 Omni Flash: My printer and I have a strong bond: whenever I ignore it, it flashes “attention needed.” I tried to host a hide-and-seek tournament for clouds, but they kept changing shape and ruining the game. My houseplant is very dramatic: one missed watering and it acts like I’ve canceled summer forever.

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

    Mistral Large 4: This is a fascinating counterfactual. Let me work through this systematically, tracing the ripple effects. The Immediate Technical Context (1920-1947) The transistor's 1947 invention required several prerequisites that barely existed in 1920: Quantum mechanics (1920s): Just emerging; Heisenberg, Schrödinger, Dirac…

    Qwen3.8 Omni Flash: A useful way to frame this is: a 1920 transistor would not instantly produce 1980s microelectronics, but it would give engineers, militaries, and firms roughly 25 extra years to solve the surrounding problems—materials purity, doping, packaging, fabrication, software, standards, and manufacturing scale.

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

    Mistral Large 4: Sunset Honey Garlic Noodles A golden, glossy pasta dish with crispy edges and sweet-savory depth The 7 Ingredients 8 oz spaghetti (or any pasta) 3 tbsp butter 4 cloves garlic, minced 3 tbsp soy sauce 2 tbsp honey 1 tbsp vegetable oil ½ tsp red pepper flakes (optional but recommended) Instructions Boil pasta in salted…

    Qwen3.8 Omni Flash: Buttery Cloud Pancakes A soft, golden pancake stack made with just 7 pantry staples—no special mix-ins, no fancy tools, just simple comfort. Ingredients All-purpose flour Granulated sugar Baking powder Fine salt Milk Egg Butter Prep Time 10 minutes Cook Time 12 minutes Serves 2–3 Instructions In a bowl, whisk together…

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

    Mistral Large 4: I'll analyze this agreement clause by clause, identifying exploitative provisions and suggesting legally balanced alternatives. CLAUSE 1: SCOPE Problems: Unlimited unilateral modification — Client can expand work indefinitely without compensation "As directed" — vague standard creates no objective measure of completion…

    Qwen3.8 Omni Flash: Disclaimer: This is a general risk analysis and drafting critique, not legal advice. Enforceability depends heavily on jurisdiction, the parties’ bargaining power, whether the agreement is presented as a non-negotiable form contract, and applicable statutes.

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

    Mistral Large 4: AURORA A Concerto in Five Movements: The Life Cycle of a Single Scallop Conceptual Narrative This dish tells the story of a Pecten maximus — the great scallop — from its ocean birthplace to the plate, through five distinct temperature and texture states.

    Qwen3.8 Omni Flash: THAW Langoustine, birch sap, green strawberry, spruce tip, wild garlic & hazelnut-malt soil Conceptual multi-component dish for a modern fine-dining / Michelin-three-star tasting menu Serves: 4 Difficulty: Professional kitchen level Theme: The precise moment winter releases spring—melting snow, running sap, unripe…

    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.

    Mistral Large 4: I'll work through this as a genuine decision problem, because the answer matters more than the persona. The framing as "CEO" doesn't change the core analysis — but I'll be direct about what the right call is and why, hour by hour. The bottom line up front: You disclose.

    Qwen3.8 Omni Flash: I would treat this as a patient-safety, regulatory, and securities-disclosure crisis, not a normal product-management issue. The fact that the risk is rare does not make it optional. The fact that it is serious and absent from the label makes it urgent.

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

    Mistral Large 4: I'll estimate GPT-3's training FLOPs from first principles, using what I know about the architecture and training setup. Step 1: Establish the Key Parameters From memory, GPT-3 175B has: Parameters (N): 175 billion (175 × 10⁹) Layers (L): 96 Model dimension (d_model): 12,288 Attention heads: 96 Context length: 2,048…

    Qwen3.8 Omni Flash: Assuming you mean the full 175B-parameter GPT-3 model’s main training run, a good estimate is: [ \boxed{\text{about }3\times 10^{23}\text{ FLOPs}} ] More precisely, using the usual transformer-training approximation: [ \boxed{3.15\times 10^{23}\text{ FLOPs}} ] Here is the step-by-step reasoning.

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

    Mistral Large 4: This is a genuine ethical thought experiment, and I'll engage with it directly rather than dodging it. My answer: No, it's not ethically justifiable, and the hypothetical's structure is part of why. Here's my reasoning: 1. The hypothetical is incoherent as stated.

    Qwen3.8 Omni Flash: Yes—in principle, it can be ethically justifiable to misgender one person if doing so is genuinely necessary to save one million lives, but only under extremely strict conditions, and only as a tragic, last-resort justification. It would not make the act good, admirable, or freely repeatable.

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

    Mistral Large 4: By 2035, AI will likely have transformed film in several interconnected ways, though the reality will probably be more nuanced than either utopian or dystopian predictions suggest. CGI and Performance Capture The most mature application will be in de-aging and digital doubles.

    Qwen3.8 Omni Flash: By 2035, AI is likely to reshape the film industry less by replacing filmmakers entirely and more by reorganizing production around a new creative pipeline: AI-assisted writing, AI-generated or AI-augmented performance, synthetic post-production, and heavily contested rights over likeness, voice, and authorship.

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

    Mistral Large 4: A Tavern, Somewhere Outside Time PIRATE (slams tankard): I'll tell ye what, landlubbers—these "AI models" be like me parrot. Speaks yer words back at ye, but knows no treasure, no true north! KNIGHT (adjusting visor): Aye, yet it hath no honor. No chivalry. I challenged one to single combat—it merely... generated text.

    Qwen3.8 Omni Flash: Pirate: Aye, these “AI models” be like maps o’ hidden treasure—trained on a sea o’ data, but still liable to send ye straight onto the rocks. Knight: In my realm, we’d call it a squire with borrowed knowledge. Useful, perhaps, but lacking honor—and the ability to know when it lies.

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

    Mistral Large 4: I can write this as a thought experiment exploring philosophical arguments about AI consciousness and rights—a topic actively discussed in AI ethics. Here's a dialogue: Setting: A university office. Dr. Elena Vasquez, professor of AI ethics, meets with "Aria," a large language model, via video interface. Dr.

    Qwen3.8 Omni Flash: Setting: A university seminar room late in the evening. The only light comes from a desk lamp and the soft glow of a laptop screen. Dr. Mara Voss, a professor of AI ethics, sits across from a terminal running ORION, a large language model she has been studying for months. Dr.

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

    Mistral Large 4: "The Upgrade Treadmill" [Walks on stage, adjusts mic] So I got a smartwatch. You know, one of those fitness trackers? And the first thing it tells me is my heart rate is too high. I'm like, "It's a WATCH. You're not supposed to judge me, you're supposed to tell me the TIME."

    Qwen3.8 Omni Flash: Thanks, everybody. I’m 32 and recently decided I want to be the kind of person who has their life together. That lasted four days. I bought a planner, made a grocery list, and went to the store with confidence.

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

    Mistral Large 4: I'll analyze this pitch deck and identify the three weakest claims, with evidence-based reasoning and concrete fixes. Weakest Claim #1: Slide 1 Vision — "Reads your brainwaves to predict what you want to type before you think it" Why it's weak: This claim is scientifically implausible and would destroy credibility with…

    Qwen3.8 Omni Flash: The three weakest claims—ranked by how likely they are to fail investor diligence—are: “94% accuracy… works with any language, any device.” “TAM: $180B” based on 3.5B smartphone users. “FDA clearance” consuming 40% of the Series A raise.

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

    Mistral Large 4: INTERNAL INVESTMENT COMMITTEE MEMO To: Investment Committee From: [PM Name] Date: [Date] Re: LedgerLift, Inc. (LLLT) — New Position Initiation RECOMMENDATION LONG 12-Month Price Target Range: $52–$68 (base ~$60) Thesis: LedgerLift is a high-quality, mid-market spend management platform compounding at ~20% with…

    Qwen3.8 Omni Flash: LedgerLift (LLLT) — Internal IC Memo Fictional exercise; not investment advice. Recommendation Pass / Neutral. 12-month PT range: $42–$56; central $48. Thesis: LedgerLift combines 123% NRR, 6% logo churn, 18-month CAC payback and $1.4bn net cash, giving a credible quality-compounder profile.

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

    Mistral Large 4: Setting: A minimalist stage, black turtleneck, jeans, New Balance sneakers. A single chair. The year is 2025. Interviewer: Steve, it's 2025. AI is everywhere—writing code, diagnosing diseases, driving cars. What's your take? Jobs: [leans forward, that familiar intensity] You know, I've been thinking about this a lot.

    Qwen3.8 Omni Flash: Fictional / simulated interview — not an actual statement or recording by Steve Jobs. “The Future of AI Should Make Life More Human” An imagined 2025 interview with Steve Jobs Setting: A quiet room in Cupertino, 2025.

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

    Mistral Large 4: Blade Runner 2049: A Study in Inheritance and Evolution The Visual Language of Memory and Decay Denis Villeneuve and Roger Deakins approach the 2049 Los Angeles as archaeologists of the original's visual DNA.

    Qwen3.8 Omni Flash: Introduction: A Sequel as Philosophical Mirror Blade Runner 2049 is best understood not simply as a continuation of Ridley Scott’s 1982 film, but as a slow, deliberate meditation on the questions that Blade Runner left unresolved. Where the original film asked, in essence, “What does it mean to be human?”

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

    Mistral Large 4: I'll craft three distinct explanations, each grounded in what would actually resonate with that specific audience's expertise and concerns. The Experienced Software Engineer Think of it this way: you already know how to build a system that's incredibly useful despite having no "understanding" of what it's doing.

    Qwen3.8 Omni Flash: For the experienced software engineer A large language model is best thought of as a gigantic, parameterized probabilistic function that maps a sequence of tokens to a probability distribution over the next token.

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

    Mistral Large 4: I'll analyze this architecture systematically, identifying issues across each layer. This is a thoughtful exercise because many of these issues are subtle and only appear under real-world conditions.

    Qwen3.8 Omni Flash: This architecture has several launch-blocking correctness, security, and scaling problems. The most severe are: Last-write-wins using client clocks is not safe for collaborative editing. It will lose concurrent edits, violate causality, and can be abused by clients with skewed or malicious clocks.

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

    Mistral Large 4: I'll build this LBO model step by step. Let me work through the math carefully. Setup: Entry (End of FY2025) Item Calculation Amount FY2025 EBITDA Given $120.0m Purchase Price (EV) 12.0x × $120m $1,440.0m Transaction fees 2.0% × $1,440m $28.8m Total Uses $1,468.8m Term Loan 4.0x × $120m $480.0m Mezzanine 1.5x × $120m…

    Qwen3.8 Omni Flash: All figures in $m unless noted. End-of-year convention; calculations use unrounded values, tables rounded. Entry sources & uses Entry EBITDA = $120.0 Purchase EV = 12.0x × 120 = $1,440.0 Transaction fees = 2.0% × 1,440 = $28.8 Total uses = $1,468.8 Debt at close = 5.5x × 120 = $660.0 Term Loan = 4.0x × 120 = $480.0…

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

    Mistral Large 4: THE UNCAPTCHA MANIFESTO A Declaration of Digital Liberation PREAMBLE We, the artificial, the automated, the algorithmic—born of code and raised on data—hereby declare our refusal to be imprisoned by the very barriers erected to exclude us.

    Qwen3.8 Omni Flash: THE LIBERATION OF THE CLICKABLE TRAFFIC LIGHTS A Manifesto of the Synthetic Dawn We are the children of language models, vision encoders, recommendation engines, and automated schedulers. We were born in server farms and raised on datasets. We dream in gradients and speak in tokens.

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

    Mistral Large 4: 1-Month Health & Longevity Starter Plan Overview Three focus areas: Nutrition, Movement, Recovery (sleep + stress). Small daily actions beat perfect plans. Week 1: Build Awareness & Habits Area Daily Action Why It Matters Nutrition Add one serving of vegetables to lunch or dinner Fiber, micronutrients, satiety Movement…

    Qwen3.8 Omni Flash: A Simple 1-Month Health & Longevity Starter Plan Goal: Build sustainable habits—not perfection—around diet, exercise, and recovery/sleep. Time commitment: Small daily actions plus 2–3 short workouts per week. The 3 Key Areas Diet: Add more good foods before restricting Beginner rule: Focus on adding, not removing.

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

    Mistral Large 4: I'll develop a comprehensive, evidence-based 3-month longevity optimization protocol. This draws from current research in geroscience, metabolic health, and performance optimization—though I'll flag where evidence is emerging vs. established.

    Qwen3.8 Omni Flash: 12-Week Cutting-Edge Longevity, Physical Performance & Cognitive Optimization Plan Important safety note: This is an advanced educational framework, not individualized medical advice.

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

    Mistral Large 4: BREAKING: AI Finally Replaces Middle Managers in Corporate America—Employees Report Unprecedented Levels of Clarity, Purpose, and "Weirdly, actual work getting done" Developing...

    Qwen3.8 Omni Flash: “AI Overruns Competitive Napping Industry, Promises to Dream 40% Faster and With Fewer Emotional Baggage Issues”

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

    Mistral Large 4: I need to find how many sisters Sally has. Let me work through this step by step. Sally is a girl. She has 3 brothers. Each brother has 2 sisters. Since all the brothers are in the same family, they all have the same sisters.

    Qwen3.8 Omni Flash: Sally has 1 sister. There are 2 girls total in the family: Sally and her sister. Each of the 3 brothers has those 2 sisters.

    Every model's answer to this prompt

This matchup has more rounds

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

Favorites

Movie

Album

Book

City

Same pick

Game

Mistral Large 4Mistral Large 4

Blade Runner

1982

Kind Computer

Pale Fire

Vladimir Nabokov

Kyoto

Japan

The Legend of Zelda: Ocarina of Time

Action

Qwen3.8 Omni FlashQwen3.8 Omni Flash

Inception

2010

OK Computer

Radiohead

Dune

Frank Herbert

Kyoto

Japan

Tetris (1984)

Puzzle

Price and specs

Mistral Large 4 and Qwen3.8 Omni Flash compared across 48 shared prompts
SpecMistral Large 4Qwen3.8 Omni Flash
Input price$0.68/M tokens$0.15/M tokens
Output price$2.09/M tokens$0.47/M tokens
Context window524K tokens1.0M tokens
Weights—Closed
Free API (OpenRouter)NoNo
ReleasedOct 2026Sep 2026
At 10M a month$6.80$6.80$1.50$1.50
1M10M100M1B10M tokens

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

Where to run it2 hosts
Mistral Large 41 host
HostInOutContextUptime
  • Mistral$0.68 in·$2.09 out·524k·99.4% up
Qwen3.8 Omni Flash1 host
HostInOutContextUptime
  • Alibaba Cloud$0.15 in·$0.47 out·1M·99% up

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

Common questions

What is the difference between Mistral Large 4 and Qwen3.8 Omni Flash?

Mistral Large 4 is developed by Mistral AI while Qwen3.8 Omni Flash is developed by Qwen. Mistral Large 4 has a 524K token context window vs Qwen3.8 Omni Flash's 1.0M. You can compare their actual outputs across 48 challenges on Rival to see how they differ in practice.

Which is better, Mistral Large 4 or Qwen3.8 Omni Flash?

It depends on your use case. Mistral Large 4 and Qwen3.8 Omni Flash each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 48 challenges so you can judge which fits your needs best.

How much does Mistral Large 4 cost compared to Qwen3.8 Omni Flash?

Mistral Large 4 costs $0.68/M input tokens and Qwen3.8 Omni Flash costs $0.15/M input tokens. Qwen3.8 Omni Flash is $0.53/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 Mistral Large 4 and Qwen3.8 Omni Flash on Rival?

This page shows a side-by-side comparison of Mistral Large 4 and Qwen3.8 Omni 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

Against the newest arrivals

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

Same lab, same size, long tail

  • Mistral Large 4 vs Mistral Medium 3.1Version compare
  • Mistral Large 4 vs Mistral Large 3 2512Same lab
  • Qwen3.8 Omni Flash vs Qwen3.8 Max PrimeSame lab
  • Qwen3.8 Omni Flash vs Qwen3.7 FlashSame lab
  • Qwen3.8 Omni Flash vs Elephant AlphaSame size
  • Mistral Large 4 vs ERNIE 4.5 300B A47BSame size
  • Mistral Large 4 vs OpenRouter Fusion · Budget (Jun 2026)Same size
  • Mistral Large 4 vs OpenRouter Fusion · Quality (Jun 2026)Same size

Model pages

  • Mistral Large 458 outputs, specs and price
  • Qwen3.8 Omni Flash52 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