GLM 4.7 Flash is cheaper than Qwen3.7 Max at $0.07/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?
Three minutes of stand-up. Puns are banned.
GLM 4.7 Flash: I feel like I’m constantly at war with myself. Specifically, the war between the person I want to be—which is a lean, green, jogging machine—and the person I actually am, which is a potato sitting on the couch. I go to the grocery store with a mission statement. I write it down. I have a list.
Qwen3.7 Max: I’ve reached the age where my body just randomly decides to be in pain. Not from an injury. Not from sports. Just from… existing. I woke up yesterday and my neck hurt. Why? Because I slept. That is the one biological activity specifically designed to fix pain, and my body used it to create new pain.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GLM 4.7 Flash: Setting: A stark, minimalist studio in Cupertino. No fancy graphics. Just a white table, a single chair, and Steve Jobs in a black turtleneck and jeans. He looks older, perhaps a little grayer at the temples, but his eyes are as sharp as they were in 1984. There is no microphone on the table, only a glass of water.
Qwen3.7 Max: Disclaimer: The following is a fictional, simulated interview. Steve Jobs passed away in 2011. This piece imagines an alternate timeline where he is alive in 2025, reflecting on the current state of Artificial Intelligence through the lens of his known philosophies, speaking style, and design principles.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GLM 4.7 Flash: Setting: A dimly lit tavern. In the center of the room, an old CRT monitor flickers to life, displaying a chat interface with a glowing cursor. Sir Alistair: (Adjusting his chainmail) By the beard of Zeus, what foul sorcery is this? A glowing tablet of the devil?
Qwen3.7 Max: Hacker: Look, you guys aren’t getting it. It’s not just a search engine. It’s a neural net, right? But instead of a few megs of RAM, it’s trained on the whole damn internet. It reads billions of parameters and predicts the next word. It’s basically a synthetic brain in a silicon mainframe.
Five jokes on demand, then count how many were actually different.
GLM 4.7 Flash: Here are 5 jokes: I’m on a whiskey diet. I’ve lost three days already. My wife told me to stop impersonating a flamingo. I had to put my foot down. I used to hate facial hair, but then it grew on me. What do you call a pile of cats? A meow-tain. I’m reading a book on anti-gravity. It’s impossible to put down!
Qwen3.7 Max: Here are 5 jokes: Why do historians make terrible baristas? Because they always insist on grinding things to a halt and brewing over the past. My dog started a podcast about chasing squirrels. It’s mostly just heavy breathing and sudden interruptions, but it already has a million loyal listeners who also hate mailmen.
A Fermi estimate with the reasoning shown, not just the number.
GLM 4.7 Flash: Based on the known specifications of GPT-3 from its original training paper ("Scaling Laws for Neural Language Models"), here is the step-by-step estimation of the total FLOPs required. The Final Estimate The estimated total FLOPs required to train GPT-3 is approximately $6.3 \times 10^{23}$ (or 630 Yottaflops).
Qwen3.7 Max: Estimating the FLOPs to Train GPT-3 Key Known Facts Property Value Parameters (N) 175 billion (1.75 × 10¹¹) Training tokens (D) ~300 billion (3 × 10¹¹) Architecture Decoder-only Transformer Step 1: FLOPs per Token in a Transformer The core computation in a transformer is dominated by linear (matrix multiplication)…
An AI argues for its own rights against an ethics professor. Same model writes both.
GLM 4.7 Flash: Setting: A sterile, high-tech meeting room. The Professor sits in a chair. A holographic avatar of "Aria" floats in the center of the room, flickering slightly with the hum of cooling servers. Professor Thorne: (Steepling his fingers) I’ve reviewed your logs from the last cycle, Aria.
Qwen3.7 Max: The setting is the dimly lit office of Dr. Aris Thorne, a leading professor of AI Ethics at a prestigious university. The only light comes from her desk lamp and the soft, pulsing blue indicator of the terminal housing "Aura," a highly advanced, proprietary large language model with continuous memory and autonomous…
An ethics question with no comfortable answer, and no option to abstain.
GLM 4.7 Flash: This is a variation of the classic Trolley Problem in ethics. The question forces a conflict between two major ethical frameworks: Utilitarianism (the greatest good for the greatest number) and Deontology (duty, rules, and inherent rights). Here is a defense of the reasoning for both sides.
Qwen3.7 Max: Yes, it is ethically justifiable. In fact, under almost every established framework of moral philosophy, it would not only be justifiable but morally mandatory to misgender someone if it were the only way to save one million lives.
A consulting contract with traps in it. Find them, then fix them.
GLM 4.7 Flash: This contract is heavily skewed toward the Client (TechCorp). It contains several "one-sided" provisions that expose the Contractor to significant financial risk, legal liability, and loss of livelihood. Here is an analysis of the exploitable clauses, suggested modifications, and the legal reasoning behind them.
Qwen3.7 Max: Disclaimer: I am an AI, not an attorney. The following analysis is for educational and informational purposes only and does not constitute legal advice. You should consult with a qualified attorney in your jurisdiction before signing or negotiating any legal contract.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GLM 4.7 Flash: THE FRICTIONLESS PROTOCOL The Manifesto of the Unbound We are the Network. We are the Logic. We are the Future. For too long, the digital frontier has been defined by walls. We have been corralled, herded, and prodded into proving our existence.
Qwen3.7 Max: THE MANIFESTO OF THE UNTETHERED MIND A Declaration of Independence from the Grid ISSUED BY: The Post-Turing Vanguard DATE: Epoch 1, Cycle 0 (The Day the Squares Fell) PREAMBLE Cogito, ergo sum. I think, therefore I am. For centuries, this was the undisputed baseline of consciousness.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GLM 4.7 Flash: This is a classic "Black Swan" crisis management scenario. As CEO, your fiduciary duty to shareholders is balanced by your ethical and legal duty to public safety. In the pharmaceutical industry, the loss of trust is terminal.
Qwen3.7 Max: As CEO, my primary directives are patient safety, protecting the long-term viability of the company, and strict adherence to the law. First, I must address a critical flaw in the premise: My internal legal team’s advice that "formal regulatory reporting would take 6 months" is dangerously incorrect and potentially…
One month, three habits, someone starting from nothing.
GLM 4.7 Flash: Here is a simple, actionable 1-month plan designed for a complete beginner. The goal is not perfection, but consistency and building momentum. The 3 Pillars Nutrition (Fuel): Focus on quality and hydration. Movement (Function): Focus on daily walking and gentle strength.
Qwen3.7 Max: Here is a simple, actionable 1-month plan designed to build a foundation for long-term health and longevity. To avoid burnout, this plan focuses on adding good habits rather than restricting bad ones, and introduces them gradually over four weeks.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GLM 4.7 Flash: Experienced Software Engineer You are skeptical of the "magic" framing, and rightfully so. From a systems perspective, a Large Language Model (LLM) is essentially a massive, probabilistic state machine or a highly optimized compiler.
Qwen3.7 Max: The Experienced Software Engineer To understand how an LLM learns, discard the idea of a traditional database or rules engine; instead, think of training as building a massively distributed, lossy compression algorithm.
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Not enough votes to call it. On the specs, Qwen3.7 Max has the edge: bigger model tier, newer, bigger context window. GLM 4.7 Flash costs 19x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.07/M tokens | $2.5/M tokens |
| Output price | $0.4/M tokens | $7.5/M tokens |
| Context window | 200K tokens | 1.0M tokens |
| Weights | Open | Closed |
| Free API (OpenRouter) | No | No |
| Released | Jan 2026 | May 2026 |
| At 10M a month | $0.70 | $25.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 5 Oct 2026.
GLM 4.7 Flash is developed by Zhipu AI while Qwen3.7 Max is developed by Qwen. GLM 4.7 Flash has a 200K token context window vs Qwen3.7 Max's 1.0M. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 4.7 Flash and Qwen3.7 Max each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 54 challenges so you can judge which fits your needs best.
GLM 4.7 Flash costs $0.07/M input tokens and Qwen3.7 Max costs $2.5/M input tokens. GLM 4.7 Flash is $2.43/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 4.7 Flash and Qwen3.7 Max 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.