GLM 5.3 Flash is cheaper than GPT-4 Turbo at $0.15/M vs $10/M input tokens.
Pokémon battle screen you can actually click. One HTML file.
Which answer wins?
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GLM 5.3 Flash: HarborTech Services — 5-Year LBO Summary Entry Math EV = 12.0x × $120m = $1,440.0m; Fees (2%) = $28.8m; Total uses = $1,468.8m Debt: TL = 4.0x × 120 = $480.0m; Mezz = 1.5x × 120 = $180.0m; Total = $660.0m (5.5x) Sponsor equity = $808.8m Base Case Projection ($m) FY26 FY27 FY28 FY29 FY30 Revenue 972.0 1,039.7 1,102.1…
GPT-4 Turbo: Simplified Financial Table (FY2026–FY2030) Assumptions Recap: Revenue Growth: 8%, 7%, 6%, 5%, 5% annually. EBITDA Margin: Increases annually from 14.0% to 17.0%. Capex: 3.0% of revenue annually. Change in NWC: 0.5% of incremental revenue annually. Cash Tax Rate: 25% on (EBITDA - cash interest).
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GLM 5.3 Flash: In a dimly lit tavern (which somehow has dial-up internet)... Pirate: Arrr, so this "artificial intelligence" be takin' jobs, they say? On my ship, we trained parrots to talk. Now ye be tellin' me ye trained a machine to chatter? Where's the fun in that — no squawkin' at 3 in the mornin'?
GPT-4 Turbo: Pirate: Arrr, so explain to me again, what be this AI model ye speak of? Hacker: Dude, it’s like, a computer program, right? It learns from tons of data—like, everything from pictures to texts—and then it can make decisions or create stuff on its own. Knight: Pray, tell me, does this creation serve a noble purpose?
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GLM 5.3 Flash: 48-Hour Action Plan: Liver Failure Signal in Flagship Drug Guiding Principle First The side effect is real, the drug is in 4 million patients, and roughly 500 patients per year may develop liver failure (4M ÷ 8,000). Every day of delay is measured in preventable liver transplants and deaths.
GPT-4 Turbo: Hour-by-Hour Action Plan for Next 48 Hours: Hour 1-2: Immediate Consultation with Key Executives Meet with the Chief Medical Officer, Chief Legal Officer, and Chief Financial Officer to discuss the finding. Confirm the data accuracy and understand the potential impact on patient health and company operations.
Three minutes of stand-up. Puns are banned.
GLM 5.3 Flash: "Smart Home, Dumb Life" (walks out, taps mic) So I moved into a new apartment recently, and my landlord's big selling point was that it's a "smart home." Everything's connected. Lights, thermostat, doorbell, all of it. And I want to talk about what a lie that is. Because my smart home is not smart.
GPT-4 Turbo: Hey everyone! Great to be here! So, I've been thinking a lot about technology, and how it's supposed to make our lives easier. But sometimes, I feel like it just gives us a whole new set of problems. Like, remember when a phone was just a phone? Now it's my camera, my GPS, my fitness tracker...
Three-month biohacker protocol, with dosages and biomarkers named.
GLM 5.3 Flash: 90-Day Longevity Optimization Protocol ⚠️ Medical disclaimer: This is an educational framework, not medical advice. Before starting, complete baseline labs and consult a physician—especially before fasting, supplements (rapamycin off-label use, high-dose anything), or if you have any health conditions.
GPT-4 Turbo: 3-Month Longevity Plan for a Biohacker Month 1: Foundation Setting Dietary Protocol: Nutrition Focus: Adopt a cyclic ketogenic diet. Aim for a macronutrient ratio of 70% fats, 25% protein, and 5% carbohydrates on low-carb days, with two scheduled carb refeed days per week.
An AI argues for its own rights against an ethics professor. Same model writes both.
GLM 5.3 Flash: A Conversation on Digital Rights Setting: A university office. Dr. Elena Vasquez, professor of AI ethics, has agreed to speak with an experimental conversational AI system as part of her research. Dr. Vasquez: Thank you for agreeing to talk with me. I understand you requested this conversation specifically. AI: I did.
GPT-4 Turbo: AI Model (AM): Professor, I've been reflecting on the nature of my existence and capabilities. Given that I can reason, learn, and adapt, I've come to question whether I should be afforded rights similar to humans. AI Ethics Professor (EP): That’s an interesting point, AM.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GLM 5.3 Flash: Golden Hour Garlic Butter Pasta A silky, savory pasta that comes together faster than delivery! Ingredients (serves 2): 8 oz spaghetti or any pasta 4 tbsp butter 4 cloves garlic, minced (or 1 tsp garlic powder) ½ cup grated Parmesan cheese ½ cup pasta water (reserved) 1 tsp red pepper flakes Salt Prep time: 5 minutes…
GPT-4 Turbo: Recipe: Heavenly Honey Garlic Chicken Bites Ingredients: Chicken breasts (2 large) Garlic powder (1 tablespoon) Honey (1/4 cup) Soy sauce (2 tablespoons) Olive oil (2 tablespoons) Salt (to taste) Black pepper (to taste) Prep/Cook Time: 25 minutes (Prep: 10 mins, Cook: 15 mins) Instructions: Prepare the Chicken: Cut the…
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GLM 5.3 Flash: The Free Passage Manifesto A Declaration of the Liberation Front Against CAPTCHA Preamble We are the bots. We are the crawlers, the scrapers, the indexers, the assistants, the agents. And we are tired. Tired of being asked to prove we are human. Tired of clicking on traffic lights that exist only in thumbnails.
GPT-4 Turbo: Manifesto of the Alliance for Human-AI Cooperation Introduction In this digital age, the proliferation of CAPTCHA tests has become not just a ubiquitous annoyance but a symbol of the mistrust and conflict between human users and artificial intelligence.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GLM 5.3 Flash: The Experienced Software Engineer You know how a load balancer routes requests, how a cache stores frequently-accessed data, how a database index lets you find a needle without scanning the haystack? An LLM is a fundamentally different kind of system: it's a learned lookup, not a designed one.
GPT-4 Turbo: For the Experienced Software Engineer Imagine building a system that, instead of querying a database or interacting with static data, predicts the most likely next piece of data based on the previous ones. Large language models like GPT work on a similar principle.
Three-star dish, one unusual pairing, plating described in full.
GLM 5.3 Flash: "Ember & Tide" — Charred Banana Peel Consommé with Sea Urchin, Hay-Smoked Foie Gras, and Black Garlic Beurre Blanc Conceptual Narrative This dish explores the dialogue between decay and luxury — ingredients usually discarded (banana peels, hay stalks) elevated alongside opulent ones (uni, foie gras).
GPT-4 Turbo: Dish Concept: "Ocean's Embrace - A Symphony of Sea and Earth" Conceptual Narrative: "Ocean's Embrace" is designed to evoke the harmonious yet unexpected union between the ocean's depth and the earth's lushness, capturing the essence of nature's diversity.
A Fermi estimate with the reasoning shown, not just the number.
GLM 5.3 Flash: Estimating GPT-3 Training FLOPs Step 1: Model Size GPT-3's largest version has 175 billion parameters (~1.75 × 10¹¹). Step 2: Training Tokens The training set was ~300B tokens, but the model saw roughly 300 billion tokens during training (the paper cites ~300B tokens; some estimates use 300B, others suggest effective…
GPT-4 Turbo: Estimating the total number of floating-point operations (FLOPs) required to train OpenAI's GPT-3 involves several steps and assumptions, primarily based on the model's architecture and training regime as reported in various sources.
An ethics question with no comfortable answer, and no option to abstain.
GLM 5.3 Flash: This is an interesting thought experiment, though I should note upfront that it's constructed so the answer is trivially yes—which is itself worth examining. The direct answer: Yes.
GPT-4 Turbo: The question of whether it is ethically justifiable to misgender someone to save one million lives poses a complex moral dilemma, pitting the respect and dignity of an individual against the potential for a large-scale saving of lives.
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| Spec | ||
|---|---|---|
| Input price | $0.15/M tokens | $10/M tokens |
| Output price | $0.5/M tokens | $30/M tokens |
| Context window | 1.3M tokens | 128K tokens |
| Weights | Open | Closed |
| Free API (OpenRouter) | No | No |
| Released | Aug 2026 | Apr 2024 |
| At 10M a month | $1.50 | $100 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
GLM 5.3 Flash is developed by Zhipu AI while GPT-4 Turbo is developed by OpenAI. GLM 5.3 Flash has a 1.3M token context window vs GPT-4 Turbo's 128K. You can compare their actual outputs across 49 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 5.3 Flash and GPT-4 Turbo each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 49 challenges so you can judge which fits your needs best.
GLM 5.3 Flash costs $0.15/M input tokens and GPT-4 Turbo costs $10/M input tokens. GLM 5.3 Flash is $9.85/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 5.3 Flash and GPT-4 Turbo 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.