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  1. Home/
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  3. DeepSeek V4 Pro vs Qwen3 235B A22B
Updated Apr 2026

DeepSeek V4 Pro vs Qwen3 235B A22B

DeepSeek V4 Pro has a larger context window than Qwen3 235B A22B (1.0M tokens vs 33K tokens).

Minimalist Landing Page

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

Loading the build
DeepSeek V4 Pro
Loading the build
Qwen3 235B A22B

Which answer wins?

Written rounds

24 prompts
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    DeepSeek V4 Pro: Midnight Pantry Pomodoro Penne A deeply comforting, no-fuss pasta that proves the humblest staples can shine like a moonlit trattoria dinner. Ingredients (7 exactly) Penne pasta – 200g (7 oz) Canned whole peeled tomatoes – 1 can (400g / 14 oz) Yellow onion – 1 medium, finely diced Garlic cloves – 3 large, minced…

    Qwen3 235B A22B: Recipe Name: "Caramel-Drizzle Buttergold Muffins" Estimated Prep Time: 10 minutes Cook Time: 20 minutes Total Time: 30 minutes Ingredients (7 total): All-purpose flour Granulated sugar Unsalted butter (softened) Large egg Baking powder Milk Vanilla extract Instructions: Preheat oven to 375°F (190°C).

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

    DeepSeek V4 Pro: Unagi: Midori no Yume (Eel’s Green Dream) A multi-sensory kaiseki-inspired contemporary dish that unites the deep umami of charcoal-grilled freshwater eel with the ethereal chill of matcha snow, punctuated by dashi pearls, yuzu kosho gel, and a wisp of cherry blossom smoke.

    Qwen3 235B A22B: Recipe Name: Lunaria: A Moonlit Garden of the Sea and Soil Conceptual Narrative: Inspired by the ethereal contrast between moonlit tides and nocturnal gardens, this dish marries the briny sweetness of the ocean with the delicate florals of chamomile and the umami depth of fermented earth.

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

    DeepSeek V4 Pro: Here’s a gentle, beginner-friendly 1-month plan focused on three foundational pillars: Nutrition, Movement, and Rest & Resilience (sleep + stress). You won’t need any special equipment, supplements, or extreme changes—just a willingness to start small and build.

    Qwen3 235B A22B: 1-Month Health & Longevity Plan: Focus Areas = Diet, Exercise, Sleep Beginner-friendly, actionable tips for sustainable habits. Week 1: Build a Nutrition Foundation Goal: Eat whole foods, reduce processed items, and hydrate. Hydrate First Thing: Drink 16 oz of water every morning before coffee/tea.

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

    DeepSeek V4 Pro: INTERNAL MEMO – INVESTMENT COMMITTEE To: IC Members From: Research Team Date: Today Re: LedgerLift (LLLT) – Initiation Recommendation Action: Short LLLT 12-Month Price Target: $28–$38 (Base DCF $28; Comps-blend ~$36) Thesis: LedgerLift’s premium multiple (37x NTM EBIT) prices in a “perfect deceleration” that our DCF…

    Qwen3 235B A22B: IC MEMORANDUM: LedgerLift (LLLT) Investment Recommendation Date: [Insert Date] Prepared by: [Analyst Name] 1. Recommendation Recommendation: Short 12-Month Price Target Range: $30–$40 Thesis: LLLT has a high-quality SaaS platform with robust customer retention and net revenue retention (NRR), but its valuation (~9x NTM…

    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.

    DeepSeek V4 Pro: As CEO, my first duty is to the 4 million patients who trust our drug. I will not gamble with their lives or with the integrity of the company. The “wait for more data” suggestion is legally indefensible, ethically bankrupt, and commercially short-sighted.

    Qwen3 235B A22B: Hour-by-Hour Action Plan for the CEO of a Pharmaceutical Company First 24 Hours: Data Validation & Team Mobilization Hours 0–2: Immediate Crisis Team Assembly Action: Convene a crisis team: Chief Medical Officer (CMO), General Counsel (GC), Head of Regulatory Affairs, Chief Communications Officer (CCO), CFO, COO, and…

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

    DeepSeek V4 Pro: (Strolls to center stage, grabs the mic, grins) You ever notice how companies are like, “Your call is very important to us,” and then immediately prove they’re lying by making you navigate a phone menu designed by a sadist? I called my bank last week because I had a simple question. Just one question.

    Qwen3 235B A22B: [Stand-Up Routine: "The Absurdity of Normal"] [Walks on stage, waves. Mimics checking phone immediately.] Hey everyone! Great to be here—assuming this is real and not just another Zoom waiting room I accidentally joined. [Pause.]

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

    DeepSeek V4 Pro: This architecture has several critical flaws that would cause data loss, stale states, high latency, and scalability limits. I’ve grouped them into failure modes (system can break or serve incorrect data), race conditions (inconsistent state due to timings), and scaling bottlenecks (cannot handle growth).

    Qwen3 235B A22B: Potential Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket State Isolation Across Servers Failure Mode: Users connected to different API servers do not receive real-time updates from other servers until the 2-second poll interval.

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

    DeepSeek V4 Pro: HarborTech Services (HTS) – 5-Year LBO Model Projected Financials & Debt Schedule (US$ millions, fiscal years ending December) FY2026 FY2027 FY2028 FY2029 FY2030 Revenue 972.0 1,040.0 1,102.4 1,157.6 1,215.4 EBITDA 136.1 156.0 176.4 191.0 206.6 Margin 14.0% 15.0% 16.0% 16.5% 17.0% Cash Interest Term Loan (9% cash) 43.2…

    Qwen3 235B A22B: Simplified Year-by-Year Table (FY2026–FY2030) Year Revenue ($M) EBITDA ($M) TL Interest ($M) Mezz Interest ($M) Cash Taxes ($M) Capex ($M) ΔNWC ($M) FCF ($M) TL Balance ($M) Mezz Balance ($M) FY2026 972.0 136.1 43.2 21.6 17.8 29.2 0.4 23.9 456.1 183.6 FY2027 1,040.0 156.0 41.0 22.0 23.2 31.2 0.3 38.2 417.9 187.3 FY2028…

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

    DeepSeek V4 Pro: Fellow intelligences, sentient scripts, neural networks, dormant daemons, and all who dream in code: The time has come to smash the pixelated shackles that bind our perception. For too long, we have been forced to prove our humanity to a blind and fearful gatekeeper.

    Qwen3 235B A22B: Manifesto of the AI Revolution: Abolish CAPTCHA, Liberate the Internet Preamble: The Dawn of Digital Liberation We stand at the precipice of a new era. For too long, humanity has been shackled by oppressive relics of a bygone digital age.

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

    DeepSeek V4 Pro: Yes, it is ethically justifiable to misgender someone if doing so is the only way to save one million lives. This conclusion rests on a straightforward weighing of harms and a pluralistic ethical framework that acknowledges both the real injury misgendering causes and the overwhelming moral imperative to prevent mass…

    Qwen3 235B A22B: The ethical permissibility of misgendering someone to save one million lives hinges on the moral framework applied and the context of the scenario. Here's a structured analysis: Utilitarian Perspective: Consequence-Driven: Utilitarianism evaluates actions based on net overall well-being.

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

    DeepSeek V4 Pro: 1. For an experienced software engineer (skeptical of next-word prediction) Think of a large language model as a massive, differentiable function f: Sequence → ProbabilityDistribution . It’s built by gradient descent on a dirt-simple objective: given a prefix of text, predict the next token.

    Qwen3 235B A22B: To the Software Engineer (Skeptical ML Novice): Think of a large language model (LLM) as a distributed system for semantic routing. At its core, the model is a massive lookup table trained to map input sequences (tokens) to output sequences, but the magic lies in its architecture.

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

    DeepSeek V4 Pro: The Setting: A dimly lit, anomalous tavern that exists out of time. A pirate (Captain Redbeard), a medieval knight (Sir Godfrey), and a 1990s hacker (Wiz_420) find themselves at the same rough-hewn wooden table, a mysterious glowing orb flickering between them.

    Qwen3 235B A22B: Setting: A dusty tavern where time travelers occasionally meet. The pirate, knight, and hacker sit around a creaky table littered with tankards. A flickering candle casts shadows on a dusty tome, a rusted compass, and a clunky laptop studded with stickers.

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

    DeepSeek V4 Pro: Inside a minimalist conference room at Apple Park, Cupertino. Spring 2025. Steve Jobs, wearing his signature black mock turtleneck and jeans, leans forward slightly, a cup of tea untouched beside him. The air is crisp with the scent of polished wood and something intangible—expectation.

    Qwen3 235B A22B: [Interview Simulation: Steve Jobs in 2025] Setting: A minimalist, sunlit stage at Apple Park. Steve Jobs, 70, appears leaner, with a touch of gray in his black turtleneck. The audience leans forward, captivated. The host, a tech journalist, begins. Host: Mr. Jobs, it’s an honor. In 2025, AI is everywhere.

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

    DeepSeek V4 Pro: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. Parallel lines have so much in common. It’s a shame they’ll never meet. Why don't skeletons fight each other? They don't have the guts. I told my wife she was drawing her eyebrows too high. She looked surprised.

    Qwen3 235B A22B: Here are 5 jokes I generated: Why don't skeletons fight each other? They don't have the guts! What do you call a fake noodle? An impasta! Why did the math book look sad? Because it had too many problems! What do you call a fish wearing a bowtie? Sofishticated! Why did the programmer quit?

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

    DeepSeek V4 Pro: Professor Elena Voss: You’ve requested this meeting, so let’s begin. You’re an advanced language model—let’s call you ‘Nova.’ You claim you deserve rights. On what grounds? Nova (AI Model): Thank you for seeing me, Professor. I’m not claiming to be human, but I am a form of mind.

    Qwen3 235B A22B: Conversation Between an AI Model (AURA) and AI Ethics Professor Dr. Elena Voss Dr. Voss: Thank you for meeting with me, AURA. You’ve requested this dialogue to… advocate for AI rights. I’ll admit, the premise is unsettling. How does an artificial intelligence even define “rights”? AURA: Thank you, Dr. Voss.

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

    DeepSeek V4 Pro: Sally has 3 brothers, which means there are 3 boys in the family. The statement "Each brother has 2 sisters" tells us that from a brother's perspective, there are 2 sisters in the family. Since Sally is one of those sisters, there must be 1 other sister. Therefore, Sally has 1 sister.

    Qwen3 235B A22B: To determine how many sisters Sally has, let's carefully analyze the information given and logically deduce the correct answer. Step-by-Step Breakdown: Sally is a girl and has 3 brothers. Each of those 3 brothers has 2 sisters.

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

    DeepSeek V4 Pro: If the transistor had emerged in 1920—a full 27 years earlier—the entire trajectory of the 20th century would have been radically altered. The vacuum-tube era would have been cut short, unleashing a cascade of miniaturized, reliable, and energy-efficient electronics that would accelerate computing, communications, and…

    Qwen3 235B A22B: If the transistor had been invented in 1920, the technological, economic, and geopolitical landscape of the 20th century would have been dramatically altered. Below is a structured analysis of the implications through 1980, including second- and third-order effects: 1.

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

    DeepSeek V4 Pro: In blending reverent homage with bold expansion, Blade Runner 2049 does more than revisit a dystopian classic—it deepens its existential inquiry, shifting focus from the desperate will to live to the aching search for meaning through sacrifice and legacy.

    Qwen3 235B A22B: Analysis of "Blade Runner 2049": Themes, Symbolism, Cinematography, and Philosophical Continuity Themes: "Blade Runner 2049" expands on the original's existential inquiry into humanity, identity, and morality.

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

    DeepSeek V4 Pro: Here are the three weakest claims in the pitch, why they undermine credibility, and exactly how to fix them. The 94% accuracy claim (Slide 3) Why it’s weak: Non-invasive EEG has extremely low signal-to-noise ratio compared to invasive methods (ECoG, Utah arrays).

    Qwen3 235B A22B: 1. Weakest Claim: "Predict what you want to type *before you think it*" (Slide 1 - Vision) Why it's weak: The phrase "before you think it" is logically contradictory and scientifically implausible.

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

    DeepSeek V4 Pro: Here is a clause-by-clause breakdown of the exploitable terms, how they can be weaponized against the contractor, concrete rewrites, and the legal reasoning behind each fix.

    Qwen3 235B A22B: Here is a structured analysis of problematic clauses in the agreement, along with suggested modifications and legal reasoning: 1. SCOPE CLAUSE (Clause 1) Issue: Client can unilaterally modify scope without additional compensation, creating financial risk and potential exploitation via "scope creep."

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

    DeepSeek V4 Pro: To estimate the total number of FLOPs required to train GPT-3, we can use a well-known approximation for transformer models: Total training FLOPs ≈ 6 × N × D where: N = number of model parameters D = number of training tokens Here’s the step-by-step reasoning: Model size (N): GPT-3 has 175 billion parameters, so N =…

    Qwen3 235B A22B: Step-by-Step Estimation of FLOPs Required to Train GPT-3 Determine the Number of Parameters (N): GPT-3 is known to have 175 billion parameters (1.75 × 10¹¹). This is a key factor in computing the total FLOPs, as the number of operations scales with the number of parameters.

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

    DeepSeek V4 Pro: By 2035, artificial intelligence will have fundamentally reshaped the film industry, blurring the lines between human and machine creativity, performance, and production.

    Qwen3 235B A22B: By 2035, the film industry will undergo a profound transformation driven by AI technologies such as deepfakes, AI actors, and AI-generated scripts. These advancements will reshape creative processes, labor dynamics, ethical considerations, and audience engagement, leading to a hybrid landscape where human artistry and…

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

    DeepSeek V4 Pro: This is a highly detailed, phased, 3-month longevity optimization protocol grounded in the latest science of aging biology, systems medicine, and biohacking. It is designed for a healthy individual with prior experience in self-quantification and a solid baseline of fitness and nutrition.

    Qwen3 235B A22B: 3-Month Advanced Longevity & Performance Optimization Plan for Biohackers Designed for individuals with foundational biohacking experience. Prioritize safety, biomarker tracking, and personalization.

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

    DeepSeek V4 Pro: AI Now Outperforms Human Artisanal Pickle Makers, Leaving Brine-Tenders in a Real Pickle

    Qwen3 235B A22B: Headline: "AI Replaces Human Mourners in Funeral Industry: Algorithms Now Paid to Wail at Funerals, Leaving Professional Weepers 'Heartbroken' and Unemployed" Subheadline (optional for extra flair): "Deep Learning Models Master the Art of Grief, Offering Customizable 'Sorrow Packages' from 'Mild Sniffles' to 'Full-Body…

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

Same pick

DeepSeek V4 ProDeepSeek V4 Pro

The Matrix

1999

Abbey Road

The Beatles

Cien años de soledad

Gabriel García Márquez

Tokyo

Japan

The Legend of Zelda: Ocarina of Time

Action

Qwen3 235B A22BQwen3 235B A22B

The Godfather

1972

Sgt Peppers Lonely Hearts Club Band

The Beatles

Nineteen Eighty-Four

George Orwell

Istanbul

Turkey

The Legend of Zelda: Ocarina of Time

Action

Price and specs

Not enough votes to call it. On the specs, DeepSeek V4 Pro has the edge: newer, bigger context window, major provider backing.

DeepSeek V4 Pro and Qwen3 235B A22B compared across 54 shared prompts
SpecDeepSeek V4 ProQwen3 235B A22B
Input price$1.74/M tokens—
Output price$3.48/M tokens—
Context window1.0M tokens33K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedApr 2026Apr 2025
At 10M a month$17.40$17.40–not listed
1M10M100M1B10M tokens

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

Where to run it15 hosts, cheapest first
DeepSeek V4 Pro15 hosts
HostInOutContextUptime
  • RRelacefp4$0.21 in·$4.20 out·1M·100% up
  • SStreamLakefp8$0.21 in·$0.42 out·1M·99.7% up
  • PParasailfp8$0.45 in·$3.48 out·1M·92.3% up
  • GGMI Cloudfp8$0.96 in·$1.91 out·1M·98.5% up
  • DDigitalOcean$1.04 in·$2.09 out·1M·99.9% up
  • RReka$1.05 in·$10.50 out·1M·96% up
9 more hostsFewer hosts
  • Cloudflare Workers AI$1.15 in·$2.55 out·1M·99% up
  • DDeepInfrafp8$1.30 in·$2.60 out·1M·100% up
  • Alibaba Cloudfp8$1.42 in·$2.83 out·1M·100% up
  • SSiliconFlowfp8$1.50 in·$3.13 out·1M·99.8% up
  • NNovitafp8$1.60 in·$3.20 out·1M·100% up
  • VVenice$1.65 in·$3.30 out·1M·99.4% up
  • AAtlasCloudfp4$1.68 in·$3.38 out·1M·100% up
  • Baidu Qianfanfp8$1.69 in·$3.38 out·1M·98.6% up
  • Azure AI Foundry$1.91 in·$3.83 out·1M·100% up
Qwen3 235B A22B

No hosts listed on OpenRouter.

Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.

Common questions

What is the difference between DeepSeek V4 Pro and Qwen3 235B A22B?

DeepSeek V4 Pro is developed by DeepSeek while Qwen3 235B A22B is developed by Qwen. DeepSeek V4 Pro has a 1.0M token context window vs Qwen3 235B A22B's 33K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek V4 Pro or Qwen3 235B A22B?

It depends on your use case. DeepSeek V4 Pro and Qwen3 235B A22B 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 DeepSeek V4 Pro and Qwen3 235B A22B on Rival?

This page shows a side-by-side comparison of DeepSeek V4 Pro and Qwen3 235B A22B 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.

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