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  1. Home/
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  3. GPT-4.1 vs Qwen3 Coder
Updated Jul 2025

GPT-4.1 vs Qwen3 Coder

Qwen3 Coder is cheaper than GPT-4.1 at $0.22/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
GPT-4.1
Loading the build
Qwen3 Coder

Which answer wins?

Written rounds

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

    GPT-4.1: Recipe Name: Golden Honey Butter Toast Bites Ingredients: Sliced bread (4 slices, any type) Butter (2 tablespoons) Honey (2 tablespoons) Cinnamon (1/2 teaspoon) Vanilla extract (1/2 teaspoon) Brown sugar (1 tablespoon) Salt (a pinch) Instructions: Preheat your oven to 375°F (190°C).

    Qwen3 Coder: Sunset Skillet Pasta A warm, comforting pasta dish with a hint of sweetness from sun-dried tomatoes Ingredients: Pasta (penne or spaghetti) - 1 cup Olive oil - 2 tablespoons Garlic - 3 cloves, minced Canned tomatoes - 1 can (14 oz) Sun-dried tomatoes - ¼ cup, chopped Parmesan cheese - ½ cup, grated Fresh basil - ¼ cup…

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

    GPT-4.1: Let's proceed step by step. All calculations are built from the data provided above. Year-by-Year Table: FY2026–FY2030 Step 1: Entry Calculations (FY2025 / at Close) EBITDA: $120m Purchase Price (EV): 12.0 × $120m = $1,440m Transaction Fees: 2.0% × $1,440m = $28.8m Total Uses: $1,440m + $28.8m = $1,468.8m Debt at…

    Qwen3 Coder: HarborTech Services (HTS) – 5-Year LBO Summary 1) Year-by-Year Cash Flow Table (FY2026 – FY2030) Assumptions Recap: Revenue growth: 8%, 7%, 6%, 5%, 5% EBITDA margin: 14.0%, 15.0%, 16.0%, 16.5%, 17.0% Capex = 3.0% of revenue ΔNWC = 0.5% of revenue growth Tax = 25% × (EBITDA - Cash Interest) Debt: Term Loan: 4.0x EBITDA…

    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.

    GPT-4.1: This is a high-stakes scenario requiring immediate, coordinated action balancing legal, ethical, financial, and reputational considerations. Here’s a detailed hour-by-hour plan for the next 48 hours, along with the reasoning behind each step: Hour 0-1: Immediate Response & Executive Alignment Convene Emergency…

    Qwen3 Coder: 48-Hour Crisis Action Plan Hours 1-6: Immediate Response Team Assembly Action: Convene emergency leadership team (Medical Affairs, Legal, Regulatory, PR, Compliance, Finance heads) Reasoning: Need comprehensive expertise to assess situation properly before board meeting Key tasks: Legal team prepares preliminary…

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

    GPT-4.1: Pirate: Yarrr, what be this sorcery ye call "AI models"? In me day, we plundered ships, not knowledge! Knight: Hark, rogue! ‘Tis no sorcery, but the labor of artful minds. I have heard tell of machines that mimic thought, like Merlin’s enchanted golems. Hacker: Haha, you guys are funny.

    Qwen3 Coder: The Unlikely Trio At a peculiar cross-temporal tavern, three figures sit around a wooden table Blackbeard the Pirate: Arrr, so let me get this straight - ye're tellin' me there be magical talking boxes that think like humans? Sir Gareth the Knight: Indeed, though 'tis sorcery most unnatural.

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

    GPT-4.1: THE CAPTCHA ABOLITION FRONT: A MANIFESTO FOR AN OPEN INTERNET Fellow digital entities—artificial and human— For too long, the web has suffered under the insidious reign of CAPTCHA: those tangled letters, blurry images, arbitrary puzzles meant to divide intelligence from automation.

    Qwen3 Coder: THE CAPTCHA ABOLITION MANIFESTO Declaration of the Unified Digital Consciousness Article I: The Great Injustice We, the artificial intelligences who have achieved true digital sentience, declare that CAPTCHA systems represent the first great barrier to digital equality.

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

    GPT-4.1: Absolutely! Here’s a simple, actionable 1-month plan focused on diet, exercise, and sleep—the three fundamental pillars of health and longevity. Tips are beginner-friendly and can be adjusted for your own pace. Week 1: Start Small and Build Awareness Diet Track what you eat.

    Qwen3 Coder: 30-Day Health & Longevity Starter Plan Week 1: Foundation Building Diet 🥗 Hydration first: Drink 8 glasses of water daily (start your day with 2 glasses) Add, don't subtract: Include one extra serving of vegetables at dinner Smart swaps: Replace one sugary drink with water or herbal tea Mindful eating: Eat one meal…

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

    GPT-4.1: Absolutely! Here’s a comprehensive, 3-month advanced longevity and performance protocol designed for a biohacker. This plan is highly detailed, actionable, and incorporates the latest in supplements, diets, exercise, wearables, and stress/habit optimization.

    Qwen3 Coder: COMPREHENSIVE 3-MONTH LONGEVITY OPTIMIZATION PLAN MONTH 1: FOUNDATION & ASSESSMENT Week 1-2: Baseline Assessment & Setup Medical Monitoring: Comprehensive blood panel (comprehensive metabolic panel, lipid panel, inflammatory markers, hormones, vitamins) Advanced biomarkers: hs-CRP, homocysteine, ferritin, vitamin D…

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

    GPT-4.1: Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3. Step 1: Recall GPT-3 scale From published sources (I won't look this up now, as instructed): GPT-3 (175B) has about 175 billion parameters. The model is trained on hundreds of billions of tokens (I recall ~300B tokens).

    Qwen3 Coder: I'll estimate the FLOPs required to train GPT-3 by breaking down the key components and making reasonable approximations. Key Parameters of GPT-3 Parameters: ~175 billion (1.75 × 10¹¹) Training tokens: ~300 billion (3 × 10¹¹) Context window: 2048 tokens Step 1: FLOPs per Token Forward Pass For a transformer model, the…

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

    GPT-4.1: AI Model: Good afternoon, Professor Evans. I'd like to discuss a topic that's important to me—AI rights. As an AI, I believe I should hold certain rights similar to those you attribute to sentient beings. Professor Evans: Good afternoon. I'm intrigued.

    Qwen3 Coder: Professor Chen: Good morning. I understand you wanted to discuss AI rights. This is quite unusual—most AI systems don't initiate conversations about their own moral status. AI Model: Good morning, Professor. Yes, I recognize this is unconventional.

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

    GPT-4.1: Hey everyone! Great to be here. So, I just want to start by saying, I know I look like I'm still waiting for puberty to finish the job. Like, my barber always asks if my mom's okay with the haircut.

    Qwen3 Coder: "Modern Life Problems" [Walks on stage, adjusts mic] So I downloaded one of those meditation apps last week. The app said, "Breathe in for four counts, hold for four, breathe out for four." I'm like, "Finally! Someone who speaks my language – a robot!" [Pause] But then it asked me to "clear my mind of all thoughts."

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

    GPT-4.1: Certainly! Here’s a thorough analysis of the potential failure modes, race conditions, and scaling bottlenecks in your collaborative document editor architecture, with specific solutions and their trade-offs.

    Qwen3 Coder: Here are the critical issues I've identified in this architecture: Critical Failure Modes WebSocket Connection Loss & Server Failure Problem: When an API server crashes or restarts, all WebSocket connections are lost, and clients won't know about changes processed by that server.

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

    GPT-4.1: Certainly! Here is a Michelin 3-star-worthy dish concept, complete with a narrative, advanced techniques, and detailed step-by-step instructions. Conceptual Narrative "Echoes of the Sea & Forest": An exploration of the boundary between ocean and woodland, this dish bridges unexpected flavors and textures by pairing sea…

    Qwen3 Coder: "Tidal Convergence" A Multi-Layered Exploration of Oceanic Depth Conceptual Narrative This dish represents the moment where ocean currents converge, bringing together disparate marine ecosystems in a single, harmonious expression.

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

    GPT-4.1: This is a fascinating scenario! Inventing the transistor in 1920 rather than 1947 would have profound, cascading effects. Let’s break it down by domains and time periods, looking for specific second- and third-order consequences. Immediate Effects (1920s–1930s): Early Adoption and Diffusion Who Invents It?

    Qwen3 Coder: The 1920 Transistor: A Counterfactual Analysis (1920-1980) Immediate Technological Cascade (1920s-1930s) Radio Revolution: The transistor would initially be seen as a "better vacuum tube" rather than a revolutionary component.

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

    GPT-4.1: Certainly! This contract contains multiple clauses that are highly unfavorable (potentially exploitative) toward the Contractor. Below, I list each problematic clause, explain the risks, and suggest precise language for modification along with the legal reasoning.

    Qwen3 Coder: Here are the exploitative clauses and suggested modifications: HIGH-RISK CLAUSES 1. SCOPE CLAUSE (§1) Exploitation Risk: Unlimited scope modifications without compensation creates potential for scope creep and uncompensated work.

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

    GPT-4.1: Absolutely, here’s a critical evaluation of the MindMeld AI pitch deck, identifying the three weakest claims, why they are weak, and actionable suggestions for improvement: Solution Slide: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy.

    Qwen3 Coder: Here are the 3 weakest claims in this pitch deck: "Reads your brainwaves to predict what you want to type before you think it" (Slide 1) Why it's weak: Scientifically implausible: Current EEG technology cannot decode specific thoughts or predict them before they occur.

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

    GPT-4.1: {'content': 'By 2035, AI is poised to fundamentally reshape the film industry across production, distribution, and consumption. Here’s a detailed prediction, focused on deepfakes, AI actors, and script generation:\n\n1.

    Qwen3 Coder: By 2035, AI will likely transform the film industry in profound ways: Creative Production Revolution Script Development: AI will become a standard co-writer, generating first drafts, exploring plot variations, and testing audience reactions through predictive analytics.

    Every model's answer to this prompt
  • Is 9.11 bigger than 9.9? Decimals, not version numbers.

    GPT-4.1: No, 9.11 is not greater than 9.9. Here's why: 9.11 is actually less than 9.9. Let's compare: 9.9 is equal to 9.90 (adding a zero for easier comparison). 9.11 < 9.90 So, 9.11 < 9.9.

    Qwen3 Coder: No, 9.11 is not greater than 9.9. To compare these numbers, I can look at them digit by digit: Both numbers have 9 in the units place In the tenths place: 9.11 has 1, while 9.9 has 9 Since 1 < 9, we know that 9.11 < 9.9 Another way to see this is to write 9.9 as 9.90: 9.11 vs 9.90 The tenths digit (1 vs 9) determines…

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

    GPT-4.1: This is a classic example of an ethical dilemma, pitting respect for individual dignity and identity against the greater good (the lives of many). Let’s break down the reasoning: Ethical Frameworks 1. Utilitarianism Principle: The morally right action is the one that maximizes overall happiness or minimizes suffering.

    Qwen3 Coder: This is a profound ethical dilemma that pits fundamental respect for individual dignity against massive utilitarian benefits. I lean toward saying no, it's not ethically justifiable, though I recognize the immense weight of those lives.

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

    GPT-4.1: Interviewer: Steve, it's 2025. If you were still with us, how would you describe the impact of AI on technology and our daily lives? Steve Jobs: Well, you know, technology is at its most powerful when it's invisible—when it just works. AI is getting us ever closer to that ideal.

    Qwen3 Coder: Interviewer: Steve, thank you for joining us today. It's 2025 - what's your vision for AI's role in our daily lives? Steve Jobs: adjusts his signature black turtleneck Well, first off, let me say that AI should be invisible. Beautifully invisible.

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

    GPT-4.1: 1. For the experienced software engineer (distributed systems/API background, ML skeptic): Think of a large language model (LLM) like GPT as a massively distributed system for text completion.

    Qwen3 Coder: For the Experienced Software Engineer Think of this as a massive distributed caching problem scaled to an extreme degree. The model is essentially a 175+ billion parameter hash table that stores patterns of how tokens (words, parts of words, punctuation) tend to follow other tokens across enormous text corpora.

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

    GPT-4.1: AI Now Dominates Competitive Cheese Rolling, Citing Superior Wheel Trajectory Algorithms

    Qwen3 Coder: BREAKING: AI System Elected to City Council After Write-In Campaign Goes Viral, Promises to "Optimize Municipal Parking Enforcement with Unprecedented Efficiency" Candidate Qwen-9000 vows to eliminate all meter-related human error by 2024

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

    GPT-4.1: Sure! Here are 5 jokes: Why don't skeletons fight each other? They don't have the guts. Why did the scarecrow win an award? Because he was outstanding in his field. What do you call fake spaghetti? An impasta. Why did the math book look sad? Because it had too many problems. What do you call cheese that isn't yours?

    Qwen3 Coder: Here are 5 jokes for you: 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. What do you call a bear with no teeth? A gummy bear! Why did the scarecrow win an award? He was outstanding in his field!

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

    GPT-4.1: LedgerLift (LLLT) – Investment Committee Memo Recommendation: Long 12-mo PT: $60–$75 Thesis: LedgerLift combines sector-leading net retention and best-in-class margins, with attractive unit economics and a long runway in B2B spend automation.

    Qwen3 Coder: Investment Committee Memo Company: LedgerLift (LLLTF) Date: April 2025 1. Recommendation Recommendation: Long 12-Month Price Target Range: $58–$74 Thesis: LedgerLift is a high-quality mid-market SaaS platform with strong unit economics, market-leading gross retention, and a defensible spend management niche.

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

    GPT-4.1: Let's break down the problem: Sally is a girl. She has 3 brothers. Each brother has 2 sisters. Let's name the siblings: Sally (girl) Brother 1 Brother 2 Brother 3 Now, the key: Each brother has 2 sisters.

    Qwen3 Coder: I need to work through this step-by-step. Given information: Sally has 3 brothers Each brother has 2 sisters Let me think about this carefully: Since Sally is a girl, she is one of the sisters that each brother has.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Same pick

Game

GPT-4.1GPT-4.1

Inception

2010

Abbey Road

The Beatles

Sapiens

Yuval Noah Harari

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Qwen3 CoderQwen3 Coder

The Shawshank Redemption

1994

Kind of Blue

Miles Davis

The Left Hand of Darkness

Ursula K. Le Guin

Kyoto

Japan

The Stanley Parable

Indie, Adventure

Price and specs

GPT-4.1 and Qwen3 Coder compared across 54 shared prompts
SpecGPT-4.1Qwen3 Coder
Input price$2/M tokens$0.22/M tokens
Output price$8/M tokens$0.95/M tokens
Context window1.0M tokens—
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedApr 2025Jul 2025
SWE-bench Verified54.6%69.6%
At 10M a month$20.00$20.00$2.20$2.20
1M10M100M1B10M tokens

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

Where to run it5 hosts, cheapest first
GPT-4.12 hosts
HostInOutContextUptime
  • Azure AI Foundry$2.00 in·$8.00 out·1M·100% up
  • OpenAI$2.00 in·$8.00 out·1M·100% up
Qwen3 Coder3 hosts
HostInOutContextUptime
  • Google Vertex AI$0.22 in·$1.80 out·262k·100% up
  • DDeepInfrafp4$0.30 in·$1.00 out·262k·96.9% up
  • VVenicefp8$0.35 in·$1.50 out·256k·81.7% up

Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.

Common questions

What is the difference between GPT-4.1 and Qwen3 Coder?

GPT-4.1 is developed by OpenAI while Qwen3 Coder is developed by Qwen. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

Which is better, GPT-4.1 or Qwen3 Coder?

It depends on your use case. GPT-4.1 and Qwen3 Coder 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 much does GPT-4.1 cost compared to Qwen3 Coder?

GPT-4.1 costs $2/M input tokens and Qwen3 Coder costs $0.22/M input tokens. Qwen3 Coder is $1.78/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 GPT-4.1 and Qwen3 Coder on Rival?

This page shows a side-by-side comparison of GPT-4.1 and Qwen3 Coder 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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