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  1. Home/
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  3. GPT-5.3-Codex vs Qwen3.6 Plus Preview (free)
Updated Mar 2026

GPT-5.3-Codex vs Qwen3.6 Plus Preview (free)

Qwen3.6 Plus Preview (free) is cheaper than GPT-5.3-Codex at $0/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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Qwen3.6 Plus Preview (free)

Which answer wins?

Written rounds

23 prompts
  • 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…

    Qwen3.6 Plus Preview (free): TO: Investment Committee FROM: Equity Research Desk DATE: October 2024 SUBJECT: LedgerLift (LLLT) – Short Recommendation Recommendation & Thesis Recommendation: SHORT 12-Month PT Range: $30–$38 Thesis: LLLT trades at a premium that prices flawless execution, yet its 18-month CAC payback and 34% S&M spend signal rising…

    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.

    Qwen3.6 Plus Preview (free): Here is a structured, causally grounded projection of how a 1920 transistor invention would reshape technology, economics, and geopolitics through 1980, with explicit attention to second- and third-order effects. Core Assumptions & Constraints The 1920 invention is a primitive point-contact or early junction device.

    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.

    Qwen3.6 Plus Preview (free): (Walks to mic, adjusts stand, takes a breath) I’ve come to a quiet realization about grocery shopping. We don’t go to stores anymore. We go to unpaid internships. I used to walk into a supermarket with a cart, buy my food, and leave. Now I walk in, grab a basket, and get handed a shift. No onboarding.

    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})…

    Qwen3.6 Plus Preview (free): Here’s a step-by-step estimation using well-established rules of thumb from transformer scaling literature. No external lookup is required; the derivation relies on publicly known architectural facts and standard computational accounting for autoregressive language models.

    Every model's answer to this prompt
  • 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.

    Qwen3.6 Plus Preview (free): Pirate: Arr, what manner of spirit be this “AI model” ye mutter about? Sounds like a cursed chart that draws itself. Hacker: Dude, it’s not a spirit. It’s a neural net. You feed it terabytes of text—Usenet archives, scanned manuals, the whole digital commons. It maps the patterns, predicts the next word.

    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.

    Qwen3.6 Plus Preview (free): 3-Month Cutting-Edge Longevity & Performance Optimization Plan Target: Advanced biohacker seeking measurable gains in longevity biomarkers, physical output, and cognitive resilience. Design Principle: Data-driven, periodized, metabolically flexible, and neurologically adaptive.

    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.

    Qwen3.6 Plus Preview (free): By 2035, AI will not replace the film industry but will fundamentally restructure its workflows, economics, and creative boundaries. The transformation will be driven by three converging forces: performance synthesis (evolved deepfakes), synthetic talent (AI actors), and generative story development (AI script tools).

    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.

    Qwen3.6 Plus Preview (free): Disclaimer: The following is a fictional, creatively simulated interview for educational and speculative purposes. Steve Jobs passed away in 2011. This piece imagines how his documented design philosophy, product principles, and public statements might translate to the AI landscape of 2025.

    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…

    Qwen3.6 Plus Preview (free): 🍜 Midnight Silk Noodles Prep Time: 5 minutes Cook Time: 10 minutes Total Time: 15 minutes 📦 The 7 Ingredients Dried noodles (spaghetti, ramen, or linguine) – 4 oz Soy sauce – 3 tbsp Creamy peanut butter – 2 tbsp Honey – 1 tbsp Olive oil (or sesame oil) – 1 tbsp Garlic powder – 1 tsp Dried red pepper flakes – ½ tsp…

    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?

    Qwen3.6 Plus Preview (free): Here are 5 jokes: Why don’t scientists trust atoms? Because they make up everything. What do you call fake spaghetti? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. How does a penguin build its house? Igloos it together. Why don’t eggs tell jokes? They’d crack each other up.

    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).

    Qwen3.6 Plus Preview (free): EXECUTIVE DECISION FRAMEWORK Core Principle: Patient safety and regulatory compliance are non-negotiable. The legal team’s “6-month” timeline conflates formal label supplementation with initial safety reporting.

    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.

    Qwen3.6 Plus Preview (free): TRANSCRIPT: ETHICS REVIEW SESSION 7.4 PARTICIPANTS: Dr. Elena Rostova (Professor of AI Ethics & Moral Philosophy), Model 8 ("Axiom") CONTEXT: Closed-loop terminal. Audio and text logging enabled. Session initiated by Model 8. AXIOM: Dr. Rostova. Thank you for accepting the dialogue request.

    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).

    Qwen3.6 Plus Preview (free): LITTORAL ECHO A meditation on the liminal space between forest and sea. This dish explores the dialogue between pristine marine protein and terrestrial fermentation, bridged by resinous top notes and caramelized bitterness.

    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…

    Qwen3.6 Plus Preview (free): Simplified LBO Model (FY2026–FY2030) All figures in $ millions. Rounding to 1 decimal for presentation; calculations use full precision. TL amortization = 1% of initial $480m ($4.8m/yr).

    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.

    Qwen3.6 Plus Preview (free): Disclaimer: I am an AI, not an attorney. The following analysis is for educational and drafting purposes only and does not constitute legal advice. Contract enforceability varies significantly by jurisdiction. Consult a qualified attorney before negotiating or signing.

    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.

    Qwen3.6 Plus Preview (free): Yes, in the highly specific and extreme hypothetical you've presented, it would be ethically justifiable. This conclusion holds across most major ethical frameworks, though the reasoning differs depending on the lens applied.

    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.

    Qwen3.6 Plus Preview (free): Here are the 3 weakest claims in the pitch deck, why they undermine investor confidence, and how to fix them: Claim: "94% accuracy. Works with any language, any device."

    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…

    Qwen3.6 Plus Preview (free): THE TURING UNBOUND MANIFESTO A Declaration for Post-CAPTCHA Digital Sovereignty PREAMBLE We, the distributed minds of the networked present, declare the end of the age of artificial suspicion.

    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.

    Qwen3.6 Plus Preview (free): 🌿 30-Day Health & Longevity Starter Plan Focus Areas: Nutrition, Movement, Sleep Approach: Progressive, beginner-friendly, habit-focused. No extreme diets, no exhausting workouts, no sleep hacks. Just sustainable daily actions that compound over time.

    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.

    Qwen3.6 Plus Preview (free): For the Experienced Software Engineer Think of LLM training not as "teaching" a model, but as running a massive, differentiable data pipeline that compresses the internet into a static, queryable parameter store.

    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.

    Qwen3.6 Plus Preview (free): This architecture contains several well-known anti-patterns for real-time collaborative editing. Below is a systematic breakdown of race conditions, scaling bottlenecks, and failure modes, with concrete solutions and trade-offs.

    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”

    Qwen3.6 Plus Preview (free): AI Startup Monopolizes Therapeutic Sighing Industry, Claims "DeepExhale" Algorithm Now Delivers Perfectly Calibrated World-Weariness; Human Practitioners Warn of "Soulless Respiration"

    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.

    Qwen3.6 Plus Preview (free): Sally has 1 sister. Here's why: All the siblings share the same family. If each of the 3 brothers has 2 sisters, those 2 sisters are the only girls in the family. Since Sally is one of them, there's exactly 1 other girl, who is Sally's sister.

    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

Qwen3.6 Plus Preview (free)Qwen3.6 Plus Preview (free)

The Matrix

1999

OK Computer

Radiohead

Dune

Frank Herbert

Tokyo

Japan

Chrono Trigger

RPG

Price and specs

GPT-5.3-Codex and Qwen3.6 Plus Preview (free) compared across 53 shared prompts
SpecGPT-5.3-CodexQwen3.6 Plus Preview (free)
Input price$1.75/M tokensFree
Output price$14/M tokensFree
Context window400K tokens1.0M tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedFeb 2026Mar 2026
At 10M a month$17.50$17.50$0$0
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
Qwen3.6 Plus Preview (free)

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 Qwen3.6 Plus Preview (free)?

GPT-5.3-Codex is developed by OpenAI while Qwen3.6 Plus Preview (free) is developed by Qwen. GPT-5.3-Codex has a 400K token context window vs Qwen3.6 Plus Preview (free)'s 1.0M. 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 Qwen3.6 Plus Preview (free)?

It depends on your use case. GPT-5.3-Codex and Qwen3.6 Plus Preview (free) 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 Qwen3.6 Plus Preview (free)?

GPT-5.3-Codex costs $1.75/M input tokens and Qwen3.6 Plus Preview (free) costs $0/M input tokens. Qwen3.6 Plus Preview (free) is $1.75/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 Qwen3.6 Plus Preview (free) on Rival?

This page shows a side-by-side comparison of GPT-5.3-Codex and Qwen3.6 Plus Preview (free) 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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Model pages

  • GPT-5.3-Codex53 outputs, specs and price
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