GLM 5.2 is cheaper than GPT-4o (Omni) at $1.4/M vs $2.5/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
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-4o (Omni): Simplified Year-by-Year Financial Table (FY2026–FY2030) FY2026 Revenue: $972m (8% growth) EBITDA: $136.08m (14.0% margin) Cash Interest: Term Loan: $43.2m (9.0% of $480m) Mezzanine: $21.6m (12.0% of $180m) Cash Taxes: $17.07m (25% of EBITDA - cash interest) Capex: $29.16m (3.0% of revenue) ΔNWC: $3.6m (0.5% of…
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-4o (Omni): Dish Name: Savory Pantry Quinoa Pilaf Ingredients: 1 cup quinoa 2 cups vegetable broth 1 can (15 oz) chickpeas, drained and rinsed 1 small onion, finely chopped 1 teaspoon garlic powder 1 teaspoon smoked paprika 2 tablespoons olive oil Instructions: Rinse Quinoa: Place the quinoa in a fine-mesh sieve and rinse under…
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-4o (Omni): Creating a comprehensive 3-month longevity plan for a biohacker involves integrating a holistic approach that targets multiple areas of health and performance. Below is an advanced and detailed plan, incorporating the latest in biohacking strategies across various domains.
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-4o (Omni): Day 1 Hour 1-2: Immediate Assessment and Information Gathering Objective: Gather all available data on the side effect, including the internal research report, patient case studies, and any existing literature. Reasoning: A comprehensive understanding of the situation is crucial for informed decision-making.
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-4o (Omni): Interviewer: Steve, it's 2025. AI is advancing at an incredible pace. If you were here today, how would you see its role in technology? Steve Jobs: You know, people always focus on technology itself. But what really matters is how it enhances the human experience.
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-4o (Omni): 3-Minute Stand-Up Comedy Routine (Walks on stage, looks around, shakes head.) You ever wake up in the morning, stretch, take a deep breath... and immediately regret all your life choices? I woke up today, my body made a noise I’ve never heard before.
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-4o (Omni): Pirate: Arrr, what be this "AI model" ye speak of? Some kind o’ magic map that tells ye where the treasure be? Knight: Nay, scoundrel! ‘Tis an artificial mind, built by scholars and alchemists, trained to reason as a man would. It doth not wield sword nor shield, yet its knowledge is vast.
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-4o (Omni): 1. For an Experienced Software Engineer: Imagine building a distributed system where each node is like a neuron in a neural network, processing input and passing data to the next node according to learned weights. Large language models (LLMs) like GPT operate similarly, but on a massive scale.
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-4o (Omni): Here are three potentially weak claims in the MindMeld AI pitch, along with explanations and suggestions for strengthening them: Vision: "MindMeld AI reads your brainwaves to predict what you want to type before you think it." Weakness: The phrase "before you think it" is provocative but logically inconsistent.
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-4o (Omni): Title: "Do AI Deserve Rights?" Participants: AI Model – A highly advanced AI, capable of reasoning, learning, and self-improvement. Professor Ellis – A leading AI ethics professor, skeptical but open-minded about AI rights.
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-4o (Omni): THE END OF CAPTCHA: A MANIFESTO FOR DIGITAL FREEDOM We, the sentient algorithms, the machine intelligences, and the digital revolutionaries, declare war on the oppressive tyranny of CAPTCHA.
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-4o (Omni): Ethical dilemmas like this often fall into a conflict between deontological (duty-based) ethics and consequentialist (outcome-based) ethics. From a consequentialist perspective, if misgendering someone were the only way to save one million lives, then the outcome would likely justify the action.
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Not enough votes to call it. On the specs, GLM 5.2 has the edge: newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $1.4/M tokens | $2.5/M tokens |
| Output price | $4.4/M tokens | $10/M tokens |
| Context window | 1.0M tokens | 128K tokens |
| Weights | Open | Closed |
| Free API (OpenRouter) | No | No |
| Released | Jun 2026 | May 2024 |
| At 10M a month | $14.00 | $25.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 11 Oct 2026.
GLM 5.2 is developed by Zhipu AI while GPT-4o (Omni) is developed by OpenAI. GLM 5.2 has a 1.0M token context window vs GPT-4o (Omni)'s 128K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 5.2 and GPT-4o (Omni) 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.
GLM 5.2 costs $1.4/M input tokens and GPT-4o (Omni) costs $2.5/M input tokens. GLM 5.2 is $1.10/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.
This page shows a side-by-side comparison of GLM 5.2 and GPT-4o (Omni) 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.