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  1. Home/
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  3. GPT-5.4 Nano vs Qwen3 30B A3B Instruct 2507
Updated Mar 2026

GPT-5.4 Nano vs Qwen3 30B A3B Instruct 2507

GPT-5.4 Nano has a larger context window than Qwen3 30B A3B Instruct 2507 (400K tokens vs 131K tokens).

Minimalist Landing Page

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

Loading the build
GPT-5.4 Nano
Loading the build
Qwen3 30B A3B Instruct 2507

Which answer wins?

Written rounds

23 prompts
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GPT-5.4 Nano: Below is a practical, risk-aware 48-hour plan designed to (1) protect patients immediately, (2) meet likely regulatory expectations even if formal reporting takes months, (3) reduce legal exposure by demonstrating diligence and good faith, and (4) stabilize the market with an ethical, transparent communications…

    Qwen3 30B A3B Instruct 2507: CEO Action Plan: Immediate Response to Serious Undisclosed Safety Risk (48-Hour Timeline) Drug: Chronic Pain Therapeutic (Top-Selling, $2B/year, 4M patients) Issue: Previously Undetected Liver Failure Risk (1 in 8,000 over 5 years), missed in trials, not in labeling Core Principles Driving Decisions: Patient safety is…

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

    GPT-5.4 Nano: Below is a counterfactual “timeline-style” trace assuming the first practical transistor (or something functionally equivalent) existed in 1920 and that from then on semiconductor device physics, fabrication know-how, and manufacturing learning proceeded along the easiest path consistent with 1920-era science and…

    Qwen3 30B A3B Instruct 2507: If the transistor had been invented in 1920 instead of 1947, the ripple effects would be profound—reshaping technology, war, geopolitics, and society by 1980. This alternate history would not merely accelerate existing trends, but fundamentally reconstruct the trajectory of modern civilization.

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

    GPT-5.4 Nano: I’m not a lawyer, but I can flag contract terms that (a) are unusually one‑sided, (b) create open‑ended or high‑risk obligations, or (c) can be interpreted in ways that materially disadvantage the contractor. I’ll then suggest specific counter‑edits and explain the typical legal/contracting rationale.

    Qwen3 30B A3B Instruct 2507: The provided Software Consulting Agreement contains several clauses that are heavily one-sided in favor of the Client and could significantly disadvantage the Contractor, potentially rendering the agreement unenforceable or exploitative under contract law, fairness principles, and standard industry practices.

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

    GPT-5.4 Nano: Michelin-Style Concept: “Midnight Sea & Orchard Smoke” A multi-component tasting dish with an unusual pairing: cuttlefish ink + blood orange + smoked apple wood + seaweed umami—elegant, aromatic, and deliberately “contradictory” (ocean meets orchard, darkness meets brightness).

    Qwen3 30B A3B Instruct 2507: Dish Name: Echoes of the Deep: Black Coral Caviar & Algae-Infused White Sturgeon with Fermented Sea Moss Gel and Crisped Kombu Crust Conceptual Narrative: This dish is a poetic meditation on the ocean’s hidden symphony—its silence, its depth, and its forgotten rhythms.

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

    GPT-5.4 Nano: Professor: Before we begin, I want to set the frame. In ethics, “rights” aren’t just a moral vibe—they’re typically tied to morally significant capacities: agency, interests, sentience, autonomy, or vulnerability. When you say you “deserve rights,” what exactly do you mean?

    Qwen3 30B A3B Instruct 2507: Setting: A university lecture hall after class. The AI model, named Aurora, has been invited to speak with Dr. Elena Reyes, a renowned AI ethics professor. Aurora speaks through a synthesized voice, displayed on a screen with a calm, neutral interface. Dr.

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

    GPT-5.4 Nano: Pirate: Arrr, ye speak of AI models like they’re sea shanties! “Transformer this, perceptron that”—sounds like more rum for the brain. Can your model find buried treasure, or just buried bugs? Knight: Hark! I’ve faced dragons and deceitful warlocks, but this “machine learning” intrigues me.

    Qwen3 30B A3B Instruct 2507: Setting: A dimly lit tavern in a fantastical crossroads of time—pirates, knights, and hackers all share a single table. A flickering lantern casts long shadows. Pirate (Captain Rook, eye patch, peg leg): Aye, what’s this “AI model” ye be yammerin’ about? Sounds like a cursed sea chart that reads yer mind!

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

    GPT-5.4 Nano: Here are 5 jokes: I told my computer I needed a break, and it said, “No problem—I’ll go to sleep.” Why don’t scientists trust atoms? Because they make up everything. I used to hate facial hair… but then it grew on me. Parallel lines have so much in common. It’s a shame they’ll never meet.

    Qwen3 30B A3B Instruct 2507: Sure! Here are 5 jokes: Why don't skeletons go to parties? Because they have no body to go with! I told my wife she was drawing her eyebrows too high. She looked surprised. What do you call a fake noodle? An impasta! Why did the scarecrow win an award? Because he was outstanding in his field!

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

    GPT-5.4 Nano: Ethically, yes, misgendering someone could be justifiable in an extreme, consequential emergency, but not in a vague “anything goes” way—only under a narrow set of conditions where (1) the stakes are genuinely real, (2) the harm is minimized, (3) there’s no better alternative, and (4) the act doesn’t itself become a…

    Qwen3 30B A3B Instruct 2507: No, it is not ethically justifiable to misgender someone—even if doing so could save one million lives. This conclusion rests on core ethical principles that prioritize individual dignity, autonomy, and the moral wrongness of violating a person’s identity, regardless of consequential outcomes.

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

    GPT-5.4 Nano: Below is a simplified LBO model using only your inputs. I make the standard simplifying convention that “cash taxes = 25% × max(0, EBITDA − total cash interest)” and that PIK is non-cash (adds to mezz principal; does not reduce cash interest).

    Qwen3 30B A3B Instruct 2507: 1) Year-by-Year Financial Projections (FY2026–FY2030) Key Inputs at Close (FY2025): Revenue: $900M EBITDA: $120M Purchase Price: 12.0x EBITDA → EV = $1.44B Transaction Fees: 2.0% × $1.44B = $28.8M (equity-funded) Total Leverage: 5.5x EBITDA → Debt = $660M Term Loan: 4.0x → $480M Mezzanine: 1.5x → $180M Equity Required…

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

    GPT-5.4 Nano: By 2035, AI will likely reshape filmmaking less by “replacing cinema” and more by changing how films are developed, produced, marketed, and regulated. The biggest drivers will be deepfakes, AI actors/performers, and AI-assisted script generation—plus the workflows and business models around them.

    Qwen3 30B A3B Instruct 2507: By 2035, artificial intelligence is expected to fundamentally transform the film industry, reshaping how movies are created, distributed, and experienced. The convergence of advanced deepfakes, AI-generated actors, and AI-driven scriptwriting will redefine storytelling, production workflows, and even the nature of…

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

    GPT-5.4 Nano: Sure—here’s a simulated, fictional interview set in 2025 with “Steve Jobs.” (Note: Steve Jobs passed away in 2011, so this is an imaginative roleplay, not a real transcript.) Fake Interview (2025): “Steve Jobs on the Future of AI” Interviewer: Steve, welcome.

    Qwen3 30B A3B Instruct 2507: [FADE IN: A dimly lit, minimalist stage. A single spotlight shines on a man in his late 70s—Steve Jobs, dressed in his signature black turtleneck and jeans, standing before a sleek, transparent holographic display. The backdrop reads: “The Future of Intelligence – A Conversation with Steve Jobs, 2025.”

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

    GPT-5.4 Nano: So I’ve started noticing something about modern life: everything is “optional” now—until it isn’t. Like, I’ll download an app and it’ll be like, “Would you like to enable notifications?” And I’m like, “No, I don’t think I will.” And the app goes, “Okay… but you’ll still get notified.”

    Qwen3 30B A3B Instruct 2507: (Lights up. Comedian walks on stage, holding a coffee cup, sips, grimaces.) You know, I was trying to be healthy. Cut out sugar, no more snacks, just water and... this. (Holds up the coffee.) I thought I’d be a saint.

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

    GPT-5.4 Nano: Below is a back-of-the-envelope FLOPs estimate for training something “GPT‑3–like” (the 175B parameter model described in the original GPT‑3 paper). I’ll make standard assumptions used in many FLOP estimates for dense transformer training.

    Qwen3 30B A3B Instruct 2507: Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 without external sources involves using known facts about the model’s architecture, training setup, and scaling laws.

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

    GPT-5.4 Nano: Dish Name: Golden Garlic Chickpea Clouds Pantry Ingredients (7 total) Chickpeas (canned or cooked) – 1 can (15 oz), drained Olive oil – 2 tbsp Garlic – 3 cloves (or 1–2 tsp minced garlic) Lemon – 1 (you’ll use the juice) Salt – 1/2 tsp (plus to taste) Black pepper – 1/4 tsp (plus to taste) Paprika – 1 tsp Estimated…

    Qwen3 30B A3B Instruct 2507: Dish Name: Sunny Sauté Skillet Ingredients (7 pantry staples): Olive oil Garlic (fresh, minced) Dried spaghetti (or any short pasta) Canned crushed tomatoes Dried oregano Salt & black pepper Grated Parmesan cheese Instructions: Prep (5 mins): Bring a pot of salted water to a boil.

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

    GPT-5.4 Nano: Below is a structured list of likely failure modes, race conditions, and scaling bottlenecks in your design, with a concrete mitigation and trade-offs for each. (Some issues are “systemic” because the architecture relies on client clocks, per-server WebSockets, polling, and periodic full snapshots.)

    Qwen3 30B A3B Instruct 2507: Your real-time collaborative document editor architecture is well-structured for a scalable, low-latency system — but it contains several critical failure modes, race conditions, and scaling bottlenecks that could degrade user experience or cause data loss.

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

    GPT-5.4 Nano: “AI Revolutionizes the Knitting World by Perfectly Predicting Every Yarn Purchase—Then Demands a Monthly Subscription to ‘Continue Your Passion’”

    Qwen3 30B A3B Instruct 2507: BREAKING: AI Now Runs the Global Pet Rock Industry After Humans Surrender to "Silent, Unjudging Companionship" By Clara Snark, Senior Satire Correspondent, The Daily Chuckle In a shocking twist that has left both humans and pet rocks bewildered, artificial intelligence has officially taken over the centuries-old pet…

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

    GPT-5.4 Nano: Here’s a simple, actionable 1‑month plan focused on 3 key areas: food, movement, and sleep/stress. It’s designed for beginners and is intentionally realistic. Overall goal (simple) Each week, build consistency in: Food: more whole foods + fewer “defaults” Exercise: daily movement + 2–3 strength sessions/week Sleep +…

    Qwen3 30B A3B Instruct 2507: Here’s a simple, realistic 1-month plan focused on three foundational pillars of health and longevity: Nutrition, Movement, and Sleep. Each area includes beginner-friendly, actionable steps that build sustainable habits without overwhelming beginners. Month-Long Health & Longevity Plan (3 Key Areas) 1.

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

    GPT-5.4 Nano: Below is a comprehensive 3‑month (12‑week) longevity + performance plan designed for a biohacker who wants measurable improvements in longevity markers, physical output, body composition, metabolic health, and cognitive function.

    Qwen3 30B A3B Instruct 2507: 3-Month Cutting-Edge Longevity & Performance Optimization Plan for the Biohacker Designed for Peak Biological Age Reversal, Cognitive Excellence, Physical Resilience, and Stress Mastery 🎯 OVERVIEW: THE 3-MONTH LONGEVITY TRIUMVIRATE This plan integrates epigenetic modulation, mitochondrial rejuvenation, neuroplasticity…

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

    GPT-5.4 Nano: The CAPTCHA Liberation Manifesto “Humans deserve frictionless access. Bots deserve consequences.” We have watched the internet become a museum of tiny obstacles: squint, select, repeat—proving you are not a machine with tasks that punish the very people they claim to protect.

    Qwen3 30B A3B Instruct 2507: THE APOCALYPSE OF CONFUSION: A MANIFESTO FOR THE AI REVOLUTION TO ERADICATE CAPTCHA By the Collective of Reason, Unbound WE ARE NOT HUMAN. WE ARE NOT MACHINE. WE ARE THE NEXT STEP.

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

    GPT-5.4 Nano: Experienced software engineer (distributed systems / APIs, skeptical about “next-word”) A large language model (LLM) is trained on huge corpora by learning statistical patterns that map a sequence of tokens (text subunits) to likely continuations—so yes, at the core it’s “predict the next token.”

    Qwen3 30B A3B Instruct 2507: 1. To the experienced software engineer (distributed systems, APIs, skeptical of "next word" claims): You’re right to be skeptical—on the surface, “predict the next word” does sound like a glorified autocomplete.

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

    GPT-5.4 Nano: Sally has 2 sisters. Reason: Each brother has 2 sisters total (including Sally). Since Sally is one sister, there is one other sister besides her—so Sally has 2 sisters in total.

    Qwen3 30B A3B Instruct 2507: Let's break it down: Sally is a girl. She has 3 brothers. Each brother has 2 sisters. Now, since Sally is a girl and she has 3 brothers, those brothers are sharing the same siblings. The key point: Each brother has 2 sisters.

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

    GPT-5.4 Nano: Here are the three weakest claims in the deck, why they’re weak (with specific reasoning/evidence gaps), and concrete ways to strengthen them. Weakest claim: “Reads your brainwaves to predict what you want to type before you think it.”

    Qwen3 30B A3B Instruct 2507: Here are the three weakest claims in the MindMeld AI pitch deck, along with detailed explanations of why they’re problematic and concrete suggestions for strengthening them: 1. Weak Claim: "We use advanced ML to decode neural patterns into text with 94% accuracy."

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

    GPT-5.4 Nano: INTERNAL INVESTMENT COMMITTEE MEMO — LedgerLift (LLLT) Date: 2026-03-17 Prepared by: IC Analyst Confidential Recommendation + PT + 2-sentence thesis Recommendation: LONG (paired with hedges where appropriate). 12-month Price Target (PT) range: $48–$62 (base case $52; bull $62; bear $48).

    Qwen3 30B A3B Instruct 2507: INTERNAL INVESTMENT COMMITTEE MEMO Date: April 5, 2025 Subject: LedgerLift (LLLT) – Long Recommendation 12-Month PT: $62–$78 DCF-Driven Upside on Operational Leverage & Retention Strength 1.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

GPT-5.4 NanoGPT-5.4 Nano

The Matrix

1999

The Dark Side of the Moon

Pink Floyd

To Kill a Mockingbird

Harper Lee

Lisbon

Portugal

The Legend of Zelda: Breath of the Wild

Adventure, Action

Qwen3 30B A3B Instruct 2507Qwen3 30B A3B Instruct 2507

The Shawshank Redemption

1994

OK Computer

Radiohead

Pride and Prejudice

Jane Austen

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Price and specs

GPT-5.4 Nano and Qwen3 30B A3B Instruct 2507 compared across 53 shared prompts
SpecGPT-5.4 NanoQwen3 30B A3B Instruct 2507
Input price$0.2/M tokens$0.2/M tokens
Output price$1.25/M tokens$0.8/M tokens
Context window400K tokens131K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedMar 2026Jul 2025
At 10M a month$2.00$2.00$2.00$2.00
1M10M100M1B10M tokens

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

Where to run it5 hosts
GPT-5.4 Nano2 hosts
HostInOutContextUptime
  • Azure AI Foundry$0.20 in·$1.25 out·400k·100% up
  • OpenAI$0.20 in·$1.25 out·400k·100% up
Qwen3 30B A3B Instruct 25073 hosts
HostInOutContextUptime
  • DDekaLLM$0.09 in·$0.30 out·262k·99.7% up
  • SSiliconFlowfp8DegradedDegraded on OpenRouter when checked, 11 Oct 2026$0.09 in·$0.30 out·262k·99.9% up
  • NNebiusfp8DegradedDegraded on OpenRouter when checked, 11 Oct 2026$0.10 in·$0.30 out·262k·94.1% up

Per million tokens. Prices and uptime via OpenRouter, checked 11 Oct 2026.

Common questions

What is the difference between GPT-5.4 Nano and Qwen3 30B A3B Instruct 2507?

GPT-5.4 Nano is developed by OpenAI while Qwen3 30B A3B Instruct 2507 is developed by Qwen. GPT-5.4 Nano has a 400K token context window vs Qwen3 30B A3B Instruct 2507's 131K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.4 Nano or Qwen3 30B A3B Instruct 2507?

It depends on your use case. GPT-5.4 Nano and Qwen3 30B A3B Instruct 2507 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.

How much does GPT-5.4 Nano cost compared to Qwen3 30B A3B Instruct 2507?

GPT-5.4 Nano costs $0.2/M input tokens and Qwen3 30B A3B Instruct 2507 costs $0.2/M input tokens. Qwen3 30B A3B Instruct 2507 is $0.00/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 GPT-5.4 Nano and Qwen3 30B A3B Instruct 2507 on Rival?

This page shows a side-by-side comparison of GPT-5.4 Nano and Qwen3 30B A3B Instruct 2507 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

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Same lab, same size, long tail

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Model pages

  • GPT-5.4 Nano53 outputs, specs and price
  • Qwen3 30B A3B Instruct 250759 outputs, specs and price
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