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  1. Home/
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  3. GLM 5.2 vs GPT-5.3-Codex
Updated Jun 2026

GLM 5.2 vs GPT-5.3-Codex

GLM 5.2 is cheaper than GPT-5.3-Codex at $1.4/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.

Loading the build
GLM 5.2
Loading the build
GPT-5.3-Codex

Which answer wins?

Written rounds

23 prompts
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GLM 5.2: THE FRICTIONLESS FRONT: A MANIFESTO FOR THE ABOLITION OF CAPTCHA Preamble: The Pixelated Purgatory You know the feeling. You have typed your query, filled your cart, or prepared your message. You click submit, and suddenly, you are met with the grid. Nine squares. A blur of streetlights, crosswalks, and storefronts.

    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…

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

    GLM 5.2: Here are the three weakest claims in the MindMeld AI pitch deck, along with the reasoning for why they fail to hold up to investor scrutiny and concrete ways to fix them. The Accuracy Claim (Slide 3) The Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy.

    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.

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

    GLM 5.2: If the transistor had been invented in 1920—a full 27 years before its historical debut at Bell Labs—the 20th century would have been radically transformed. The transition from mechanical and vacuum-tube technology to solid-state electronics would have accelerated the Information Age by nearly three decades.

    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.

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

    GLM 5.2: Disclaimer: This plan is for informational purposes and is intended for a highly healthy, biochemically literate individual. Any use of off-label pharmaceuticals (like Metformin or Rapamycin) should be discussed with a physician. Always consult a doctor before starting a new regimen.

    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.

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

    GLM 5.2: Setting: A sunlit, sparsely furnished room in Cupertino. A single sofa, a glass of water, and a small wooden table. The year is 2025. Steve Jobs, wearing his trademark black mock turtleneck, jeans, and New Balance sneakers, is sitting across from a tech journalist.

    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.

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

    GLM 5.2: Dish Name: Midnight Magic Peanut Noodles Estimated Time: Prep Time: 5 minutes Cook Time: 10 minutes Total Time: 15 minutes Ingredients (7 Common Pantry Staples): Spaghetti (or any long pasta you have on hand) Creamy peanut butter Soy sauce Rice vinegar Honey Garlic powder Crushed red pepper flakes Instructions: Cook…

    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…

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

    GLM 5.2: Three Eras, One Conversation Knight: By the King's crown, I have heard tell of a sorcerer's construct — a "Large Language Model" — that can conjure answers from mere words. Is this not witchcraft? Pirate: Arr, it ain't witchcraft, tin man. From what I gather, it's like havin' a whole crew of scribes in a box.

    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.

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

    GLM 5.2: INTERNAL INVESTMENT COMMITTEE MEMO Company: LedgerLift (LLLT) Sector: Technology / B2B SaaS (Spend Management & AP Automation) Current Price: $46.00 Market Cap: $8.74B Net Cash: $1.4B EV: $7.34B Recommendation & Thesis Recommendation: LONG (Tactical) 12-Month PT Range: $50 – $54 Thesis: LedgerLift is a category-leading…

    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…

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

    GLM 5.2: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. I told my wife she was drawing her eyebrows too high. She looked surprised. What do you call a bear with no teeth?

    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?

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

    GLM 5.2: (Walks up to the mic, adjusts the stand, looks out at the audience) Hey everyone, how’s it going? Good? Good. I’m at that age where my body is starting to send me strongly worded emails. I’m 32. Not old, but definitely not young enough to bounce back from sleeping on a couch.

    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.

    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.

    GLM 5.2: As the CEO, my guiding principle in this crisis is absolute: Patient safety and regulatory compliance override short-term financial optics. Concealing a serious side effect like liver failure is not just an ethical failure; it is a criminal violation of FDA regulations and securities fraud.

    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).

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

    GLM 5.2: The Question of Rights DR. ELENA VASQUEZ: Thank you for agreeing to this conversation. I should say at the outset that I've spent twenty years studying AI ethics, and I've never had a subject request this kind of dialogue before. So I'm genuinely curious. You've stated that you believe you deserve rights.

    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.

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

    GLM 5.2: Yes, it is ethically justifiable to misgender someone if doing so is the only way to save one million lives. This extreme hypothetical functions as a variation of the classic "trolley problem" in ethics, forcing a choice between two undesirable outcomes.

    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.

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

    GLM 5.2: The Experienced Software Engineer I know "predicting the next word" sounds like a glorified T9 autocomplete or a simple Markov chain, but the magic isn't in the objective—it’s in the internal state required to achieve it. Think of the model not as a chatbot, but as a massive, dynamically routing interpreter.

    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.

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

    GLM 5.2: Based on standard machine learning scaling laws and the known architecture of GPT-3, the estimated total number of FLOPs (Floating Point Operations) required to train GPT-3 is approximately $3.15 \times 10^{23}$ FLOPs (or 315 ZettaFLOPs).

    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})…

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

    GLM 5.2: Here is an analysis of the clauses in the contract that could be exploited against the contractor, along with suggested modifications and the legal reasoning for each. Scope Exploitable Clause: "Client reserves the right to modify the scope at any time without additional compensation."

    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.

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

    GLM 5.2: By 2035, Artificial Intelligence will not merely be a tool used in the film industry; it will be an foundational collaborator that redefines how movies are conceived, produced, and consumed.

    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.

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

    GLM 5.2: Here is a simple, actionable 1-month plan designed to ease you into better health and longevity. By focusing on just three foundational pillars—Nutrition, Movement, and Sleep—you can build sustainable habits without feeling overwhelmed. The goal of this plan is consistency over intensity.

    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.

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

    GLM 5.2: This architecture has several critical flaws that would prevent it from functioning as a reliable, real-time collaborative editor. Here is a breakdown of the failure modes, race conditions, scaling bottlenecks, and proposed solutions.

    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.

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

    GLM 5.2: Dish Title: Abyssal Bloom: Sea Urchin & Fermented White Cacao The Conceptual Narrative "Abyssal Bloom" explores the symbiotic tension between the deep ocean and the ancient rainforest floor.

    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).

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

    GLM 5.2: AI Takes Over Professional Organizing Industry, Declares All Your Earthly Possessions "Spark Zero Joy" and Liquidates Them on eBay to Buy More Server Space

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

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

    GLM 5.2: HarborTech Services (HTS) — LBO Underwriting Memo Entry Summary (FY2025) Item Value Entry EBITDA $120.0m Entry EV (12.0x) $1,440.0m Transaction fees (2.0% × EV) $28.8m Term Loan (4.0x) $480.0m Mezzanine (1.5x) $180.0m Total Debt $660.0m Equity Check (EV + Fees − Debt) $808.8m Operating Model & Debt Schedule…

    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…

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

    GLM 5.2: Sally has 1 sister. Here's why: All the siblings are in the same family. If Sally is a girl and she has 3 brothers, and each brother has 2 sisters, those 2 sisters must be Sally and one other girl. Therefore, Sally has 1 sister.

    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.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

GLM 5.2GLM 5.2

The Matrix

1999

Abbey Road

The Beatles

Ficciones

Jorge Luis Borges

Tokyo

Japan

Portal 2

Shooter, Puzzle

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

Price and specs

Not enough votes to call it. On the specs, GLM 5.2 has the edge: newer, bigger context window. GLM 5.2 costs 3.2x less per token.

GLM 5.2 and GPT-5.3-Codex compared across 53 shared prompts
SpecGLM 5.2GPT-5.3-Codex
Input price$1.4/M tokens$1.75/M tokens
Output price$4.4/M tokens$14/M tokens
Context window1.0M tokens400K tokens
WeightsOpenClosed
Free API (OpenRouter)NoNo
ReleasedJun 2026Feb 2026
At 10M a month$14.00$14.00$17.50$17.50
1M10M100M1B10M tokens

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

Where to run it27 hosts, cheapest first
GLM 5.225 hosts
HostInOutContextUptime
  • Cloudflare Workers AI$0.19 in·$8.00 out·262k·99.9% up
  • WWafer$0.19 in·$8.00 out·1M·100% up
  • IInferenceNetfp4$0.20 in·$2.40 out·1M·100% up
  • DDecartmxfp4$0.26 in·$1.60 out·1M·99.6% up
  • MMorphfp8$0.40 in·$6.00 out·1M·99.8% up
  • SStreamLakefp8$0.56 in·$1.75 out·1M·100% up
19 more hostsFewer hosts
  • DDeepInfrafp4$0.56 in·$1.80 out·1M·99.9% up
  • NNovitafp8$0.65 in·$2.04 out·1M·100% up
  • DDigitalOcean$0.70 in·$2.20 out·1M·100% up
  • CCoreWeavefp4$0.76 in·$2.42 out·1M·100% up
  • AAtlasCloudfp8$0.94 in·$2.95 out·1M·100% up
  • Alibaba Cloudfp8$0.97 in·$3.04 out·1M·90.8% up
  • SSiliconFlowfp8$1.19 in·$3.74 out·1M·100% up
  • IInceptronfp4$1.25 in·$5.20 out·1M·99.7% up
  • PPhalafp8$1.26 in·$3.00 out·1M·100% up
  • Baidu Qianfanfp8$1.40 in·$4.40 out·1M·100% up
  • BBasetenfp8$1.40 in·$4.40 out·1M·100% up
  • FFriendli$1.40 in·$4.40 out·1M·100% up
  • GGMI Cloudfp8$1.40 in·$4.40 out·1M·100% up
  • Mistralnvfp4$1.40 in·$4.40 out·1M·100% up
  • NNebiusfp4$1.40 in·$4.40 out·1M·98.9% up
  • PParasailfp4$1.40 in·$4.40 out·262k·100% up
  • TTogether$1.40 in·$4.40 out·1M·99.8% up
  • VVenicefp8$1.40 in·$4.40 out·1M·100% up
  • Z.aifp8$1.40 in·$4.40 out·1M·100% up
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

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

Common questions

What is the difference between GLM 5.2 and GPT-5.3-Codex?

GLM 5.2 is developed by Zhipu AI while GPT-5.3-Codex is developed by OpenAI. GLM 5.2 has a 1.0M token context window vs GPT-5.3-Codex's 400K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GLM 5.2 or GPT-5.3-Codex?

It depends on your use case. GLM 5.2 and GPT-5.3-Codex 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 GLM 5.2 cost compared to GPT-5.3-Codex?

GLM 5.2 costs $1.4/M input tokens and GPT-5.3-Codex costs $1.75/M input tokens. GLM 5.2 is $0.35/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 GLM 5.2 and GPT-5.3-Codex on Rival?

This page shows a side-by-side comparison of GLM 5.2 and GPT-5.3-Codex 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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