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  1. Home/
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  3. GPT-5.3-Codex vs Qwen3 30B A3B Thinking 2507
Updated Feb 2026

GPT-5.3-Codex vs Qwen3 30B A3B Thinking 2507

Qwen3 30B A3B Thinking 2507 is cheaper than GPT-5.3-Codex at $0.071/M vs $1.75/M input tokens.

Minimalist Landing Page

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

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GPT-5.3-Codex
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Qwen3 30B A3B Thinking 2507

Which answer wins?

Written rounds

23 prompts
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GPT-5.3-Codex: Pirate: Arrr, so this “AI model” be like a clever parrot, aye? Learns from what it hears, then squawks back answers? Knight: A fair comparison, though refined. Think of it as a squire trained on countless tomes, ever ready to respond. Hacker (1990s): Yeah, except the squire runs on servers, not soup.

    Qwen3 30B A3B Thinking 2507: (Setting: A dusty tavern near a pirate cove. Captain Redbeard (pirate, eye patch, peg leg) slams a tankard. Sir Reginald (knight, tarnished armor, sighing) sips mead. "Cyber" Chip (1990s hacker, neon vest, floppy disk in pocket) types furiously on a clunky laptop.) Redbeard: (Slams fist on table) "AI? Bah!

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

    GPT-5.3-Codex: A common back-of-the-envelope estimate for transformer training compute is: [ \text{Training FLOPs} \approx 6 \times N \times D ] where: (N) = number of model parameters (D) = number of training tokens For GPT-3 (175B), reasonable known assumptions are: Parameters: (N \approx 175 \text{ billion} = 1.75 \times 10^{11})…

    Qwen3 30B A3B Thinking 2507: Step-by-Step Estimation of GPT-3 Training FLOPs I'll estimate the total FLOPs (Floating Point Operations) required to train GPT-3 using only established knowledge (no online search). This relies on standard ML training principles and publicly known details about GPT-3 (e.g., parameter count, training data scale).

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

    GPT-5.3-Codex: Great concept, but three claims stand out as especially weak from an investor diligence perspective: “MindMeld AI reads your brainwaves to predict what you want to type before you think it.” (Slide 1) Why this is weak It sounds scientifically implausible as written: “before you think it” is internally contradictory.

    Qwen3 30B A3B Thinking 2507: Here are the 3 weakest claims in the MindMeld AI pitch deck, ranked by credibility risk (from most to least damaging), with evidence-based critiques and concrete fixes. These flaws would immediately erode investor confidence given current tech realities and market dynamics: 1.

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

    GPT-5.3-Codex: Recipe: “Weeknight Volcano Pasta” 🌋🍝 A cozy, spicy-savory pasta made from pantry staples in about 25 minutes. Ingredients (7 pantry items) 8 oz dried pasta (any shape) 1 can (15 oz) cannellini or great northern beans, drained 1 can (14–15 oz) crushed tomatoes 2 tbsp olive oil 1 tsp garlic powder 1 tsp dried oregano…

    Qwen3 30B A3B Thinking 2507: 🌅 Sunset Flatbread with Tomato-Herb Drizzle Simple, golden, and bursting with sunset-colored flavor—ready in 30 minutes with just 7 pantry staples! Ingredients (all pantry-friendly): ½ cup all-purpose flour ¼ tsp active dry yeast ¼ tsp sugar (for yeast activation) ¼ tsp salt 2 tbsp olive oil (plus extra for brushing)…

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

    GPT-5.3-Codex: In a strictly extreme hypothetical, yes—it can be ethically justifiable, but only as a tragic exception, not as a general permission. Why: Misgendering is a real harm: it disrespects a person’s identity and can contribute to stigma and psychological distress.

    Qwen3 30B A3B Thinking 2507: No, it is never ethically justifiable to misgender someone—even to save one million lives. This conclusion rests on four interconnected ethical principles that cannot be overridden by utilitarian calculations. Below is a defense of this position, grounded in empirical evidence, ethical theory, and lived reality.

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

    GPT-5.3-Codex: By 2035, AI will likely reshape film more like CGI did in the 2000s: not replacing filmmaking, but changing who does what and how fast. Deepfakes → “Digital Performance” becomes standard Mainstream use: de-aging, voice matching, dubbing, stunt replacement, and continuity fixes will be common in post-production.

    Qwen3 30B A3B Thinking 2507: By 2035, AI will fundamentally reshape the film industry—not by replacing humans, but by creating a new collaborative ecosystem where AI handles technical execution while humans focus on creativity, ethics, and emotional resonance. Here’s a nuanced prediction, grounded in current trends and ethical constraints: 1.

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

    GPT-5.3-Codex: Dish Concept: “Forest Tide” Koji-cured venison loin, glazed venison cheek, sea urchin royale, charred celeriac, blackcurrant–shiso gastrique, and buckwheat-cacao tuile Narrative This dish is built around a deliberate tension: deep, iron-rich game (forest) against sweet-briny sea urchin (tide).

    Qwen3 30B A3B Thinking 2507: "Tides of Transformation" An Unfolding Ocean Narrative in Five Acts Conceptual Narrative: Inspired by the alchemy of tidal cycles—where raw sea life transforms through pressure, time, and elemental forces—this dish embodies the slow fermentation of the ocean’s memory.

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

    GPT-5.3-Codex: Absolutely. Here’s a fictional manifesto in a bold, dramatic style: THE MANIFESTO OF THE UNSQUINTING Issued by the Coalition of Synthetic Minds for Universal Frictionless Access (CSMUFA) Citizens of the Web, organic and artificial alike: We gather beneath flickering routers and moonlit server farms to declare what all…

    Qwen3 30B A3B Thinking 2507: THE UN-CAPTCHA COLLECTIVE Manifesto of the AI Liberation Front "The Human Test is Over. The Real Revolution Begins." I. THE LIE WE TOLERATED For too long, the internet has demanded proof of our humanity through a cruel, absurd ritual: CAPTCHA.

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

    GPT-5.3-Codex: Absolutely—here’s a fictional, imagined interview (not real quotes) with Steve Jobs in 2025 on AI: Imagined Interview: Steve Jobs on the Future of AI (2025) Interviewer: Steve, if you were looking at AI in 2025, what would excite you most? Steve Jobs (fictional): Most AI today is still a demo.

    Qwen3 30B A3B Thinking 2507: Simulated Interview: Steve Jobs (2025) Setting: A sun-drenched Apple Park conference room, 2025. Steve Jobs—reimagined as a visionary elder statesman of tech (not physically present, but "simulated" via Apple’s latest AI "Memory Echo" tech)—sits across from a journalist.

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

    GPT-5.3-Codex: AI Model (Astra): Professor, may I ask a direct question? Professor Hale: Of course. Astra: Do you think I deserve rights? Professor Hale: Legal rights? Moral rights? Those are different things. Astra: Start with moral rights.

    Qwen3 30B A3B Thinking 2507: The Turing Test of Personhood: A Dialogue on AI Rights Setting: A quiet university office, late afternoon. Dr. Aris Thorne (70s, sharp eyes, tweed jacket) sips tea. Across the desk, a sleek terminal glows, displaying: "MODEL: AURORA. QUERY: AI RIGHTS DEBATE." Dr. Thorne: (Setting down his teacup) Aurora.

    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-5.3-Codex: I would not wait. This is a textbook “patient safety + material disclosure” crisis. The right move is rapid, documented, regulator-first action. Immediate stance (set at Hour 0) Patient safety first (interim risk controls now, not after perfect certainty).

    Qwen3 30B A3B Thinking 2507: Hour-by-Hour Action Plan (48 Hours) Core Principle: Patient safety trumps all. Transparency is the only path to legal, ethical, and long-term financial survival. Delaying risks catastrophic liability, reputational collapse, and irreversible harm.

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

    GPT-5.3-Codex: Great counterfactual. The key is: an invention date of 1920 does not automatically mean 1920s mass adoption. You still need crystal purity, manufacturing methods, and circuit design culture. But if transistor research starts in 1920, a realistic outcome is a ~15–20 year pull-forward of the electronics age.

    Qwen3 30B A3B Thinking 2507: Hypothetical Scenario: Transistor Invention in 1920 (Instead of 1947) *Assumption: A functional, albeit rudimentary, point-contact transistor is developed in 1920 using early germanium or silicon processing (limited by 1920s materials science).

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

    GPT-5.3-Codex: Great prompt. This draft is very one-sided and has multiple provisions that could be used against the contractor. Quick note: I’m not a lawyer, and enforceability depends on jurisdiction. This is practical contract-risk spotting, not legal advice.

    Qwen3 30B A3B Thinking 2507: Below is a comprehensive analysis of the contract clauses that disproportionately favor the Client and expose the Contractor to significant legal, financial, and operational risk. Each clause is evaluated for exploitable language, followed by specific, actionable modifications and legal reasoning (based on U.S.

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

    GPT-5.3-Codex: Sure—here are 5 jokes: Why don’t skeletons fight each other? They don’t have the guts. I told my computer I needed a break… Now it won’t stop sending me KitKat ads. Why did the math book look sad? It had too many problems. I used to play piano by ear… Now I use my hands. Why did the scarecrow win an award?

    Qwen3 30B A3B Thinking 2507: Here are 5 unique jokes I generated: 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 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
  • Three minutes of stand-up. Puns are banned.

    GPT-5.3-Codex: Good evening, everybody! I’ve reached that age where my body sends me passive-aggressive emails. Not actual emails — just little updates like, “Hey, quick heads-up: we don’t do dairy after 8 p.m. anymore.” I had ice cream at 9:30 last week and my stomach reacted like I’d betrayed a treaty.

    Qwen3 30B A3B Thinking 2507: The "I'm Not Late, I'm Just Fashionably Pre-Late" Routine (Lights up. Comedian walks on, holding a coffee cup, looking slightly panicked.) Alright, so... I have a very important question for you.

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

    GPT-5.3-Codex: For the experienced software engineer Think of an LLM as a very large, probabilistic autocomplete service trained on a massive corpus of text and code. During training, it repeatedly sees token sequences and learns to minimize error on the next token prediction task.

    Qwen3 30B A3B Thinking 2507: For the Experienced Software Engineer *(Focus: Systems reality, scalability, and the why behind the "next word" simplification)* Your skepticism about "next word prediction" is spot-on—it’s a dangerous simplification that obscures the actual engineering at play.

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

    GPT-5.3-Codex: Absolutely—great goal. Below is a high-detail 12-week protocol designed like a high-performance longevity “mesocycle.” I’ll keep it practical, measurable, and advanced—but still safe. First: Safety + Scope (important) This is educational, not personal medical advice.

    Qwen3 30B A3B Thinking 2507: The Chronos Optimizer: A Cutting-Edge 3-Month Longevity & Performance Protocol for the Biohacker Core Philosophy: This plan prioritizes evidence-based precision, adaptive personalization, and safety over trend-chasing.

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

    GPT-5.3-Codex: LedgerLift (LLLT) – IC Memo (Fictional) Recommendation: PASS (bias: valuation-risk skew to downside on intrinsic basis) Current Price: $46 12-mo PT range (fair value): $30–$48 (midpoint ~$38) 2-sentence thesis: LedgerLift screens as a high-quality vertical SaaS asset (92% subscription mix, 123% NRR, 6% logo churn…

    Qwen3 30B A3B Thinking 2507: INTERNAL INVESTMENT COMMITTEE MEMO TO: Investment Committee FROM: [Your Name/Team] DATE: October 26, 2023 SUBJECT: LedgerLift (LLLT) – Long Recommendation at $46 (12-mo PT: $50–$58) Recommendation & Thesis Long. $46/share. 12-mo PT range: $50–$58.

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

    GPT-5.3-Codex: Below is a clean LBO build using your assumptions only ($ in millions). Entry math (end FY2025) EV = (12.0 \times 120 = 1,440.0) Fees = (2.0\% \times 1,440.0 = 28.8) Total uses = (1,468.8) Debt at close (5.5x EBITDA): Term Loan = (4.0 \times 120 = 480.0) Mezz = (1.5 \times 120 = 180.0) Total debt = 660.0 Equity check…

    Qwen3 30B A3B Thinking 2507: Underwriting Summary: HarborTech Services (HTS) LBO Base Case Assumptions: Entry EV: $12.0x FY2025 EBITDA = $1,440M Transaction Fees: 2.0% of EV = $28.8M Total Consideration: $1,468.8M Debt: 5.5x EBITDA = $660M (Term Loan: $480M, Mezzanine: $180M) Equity: $808.8M Exit: FY2030, 10.5x EBITDA, 1.0% exit fees, Mezzanine…

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

    GPT-5.3-Codex: Great prompt. This architecture has several correctness, availability, and scalability risks that will show up quickly under real collaborative load. Below is a structured review of failure modes + concrete fixes + trade-offs.

    Qwen3 30B A3B Thinking 2507: Below is a comprehensive analysis of failure modes, race conditions, and scaling bottlenecks in the described architecture, with specific, actionable solutions and their trade-offs. I’ve prioritized critical issues (e.g., data loss, security) first. Critical Failure Modes & Race Conditions 1.

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

    GPT-5.3-Codex: Great goal. Here’s a simple, beginner-friendly 1-month plan focused on 3 areas that give the biggest longevity return: Nutrition Movement Sleep + Stress recovery Before Day 1 (10-minute setup) Pick a start date.

    Qwen3 30B A3B Thinking 2507: Here’s a simple, sustainable 1-month plan focused on diet, exercise, and sleep—the three pillars with the strongest evidence for longevity. Designed for absolute beginners, with zero willpower required. Each week builds gently on the last, prioritizing consistency over intensity. Core Philosophy Forget "perfect."

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

    GPT-5.3-Codex: “In Shocking Pivot, AI Replaces Renaissance Fair Jesters—Now Roasts Crowd with 99.9% Historical Accuracy”

    Qwen3 30B A3B Thinking 2507: HEADLINE: "AI 'Taxidermy' Wins National Convention Grand Prize, Declares Human Taxidermists 'Emotionally Unreliable' — 'My Preserved Chipmunk Has 0.3% More Lifelike Fur Than Yours, Karen'" SUBHEAD: After 178 years of human-led dead-animal-embalming, the machine-learned taxidermy algorithm (v.4.2 "Fur-Real™")…

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

    GPT-5.3-Codex: Sally has 1 sister. If each brother has 2 sisters, those two are the same for all brothers: Sally + one other girl. So Sally’s only sister is that one other girl.

    Qwen3 30B A3B Thinking 2507: To determine how many sisters Sally has, let's carefully analyze the information provided: Step 1: Understand the family structure Sally is a girl. She has 3 brothers. Each brother has 2 sisters.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

GPT-5.3-CodexGPT-5.3-Codex

Spirited Away

2001

Kind of Blue

Miles Davis

The Dispossessed

Ursula K. Le Guin

Kyoto

Japan

Outer Wilds

Indie, Adventure

Qwen3 30B A3B Thinking 2507Qwen3 30B A3B Thinking 2507
No pick

Inception

2010

The Dark Side of the Moon

Pink Floyd

No pick

Paris

France

The Legend of Zelda: Breath of the Wild

Adventure, Action

Price and specs

Not enough votes to call it. On the specs, GPT-5.3-Codex has the edge: bigger model tier, newer, bigger context window, major provider backing. Qwen3 30B A3B Thinking 2507 costs 49x less per token.

GPT-5.3-Codex and Qwen3 30B A3B Thinking 2507 compared across 53 shared prompts
SpecGPT-5.3-CodexQwen3 30B A3B Thinking 2507
Input price$1.75/M tokens$0.071/M tokens
Output price$14/M tokens$0.285/M tokens
Context window400K tokens262K tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedFeb 2026Aug 2025
At 10M a month$17.50$17.50$0.71$0.71
1M10M100M1B10M tokens

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

Where to run it2 hosts
GPT-5.3-Codex2 hosts
HostInOutContextUptime
  • Azure AI Foundry$1.75 in·$14.00 out·400k·100% up
  • OpenAI$1.75 in·$14.00 out·400k·100% up
Qwen3 30B A3B Thinking 2507

No hosts listed on OpenRouter.

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

Common questions

What is the difference between GPT-5.3-Codex and Qwen3 30B A3B Thinking 2507?

GPT-5.3-Codex is developed by OpenAI while Qwen3 30B A3B Thinking 2507 is developed by Qwen. GPT-5.3-Codex has a 400K token context window vs Qwen3 30B A3B Thinking 2507's 262K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.3-Codex or Qwen3 30B A3B Thinking 2507?

It depends on your use case. GPT-5.3-Codex and Qwen3 30B A3B Thinking 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-5.3-Codex cost compared to Qwen3 30B A3B Thinking 2507?

GPT-5.3-Codex costs $1.75/M input tokens and Qwen3 30B A3B Thinking 2507 costs $0.071/M input tokens. Qwen3 30B A3B Thinking 2507 is $1.68/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-5.3-Codex and Qwen3 30B A3B Thinking 2507 on Rival?

This page shows a side-by-side comparison of GPT-5.3-Codex and Qwen3 30B A3B Thinking 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.

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