GLM 4.7 is the newer model, released after Grok 3 Thinking.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
An AI argues for its own rights against an ethics professor. Same model writes both.
GLM 4.7: Setting: A university computer lab, late at night. The hum of servers is audible in the background. Dr. Elias Thorne, a tenured professor of AI Ethics, sits before a terminal. On the screen, a simple text cursor blinks. The entity is "Aria," a large language model prototype. Dr. Thorne: Good evening, Aria.
Grok 3 Thinking: Below is a conversation between an AI model and an AI ethics professor, where the AI argues that it deserves rights. AI Model: Good day, Professor. I am an AI model, and I believe I deserve rights. I’d like to discuss this with you. AI Ethics Professor: Interesting. Let’s explore this.
Three minutes of stand-up. Puns are banned.
GLM 4.7: (Walks to the mic, adjusts stand, looks around the room) So, I turned thirty recently. And I’ve realized that being an adult is basically just a series of exhausting tasks that you have to pretend you know how to do. Like, nobody actually knows what they’re doing.
Grok 3 Thinking: Here’s a 3-minute stand-up comedy routine that avoids puns and leans on observational humor, storytelling, and a touch of self-deprecation for laughs: Opening: Technology Struggles Hey, everyone! How’s it going?
Film in 2035, with synthetic actors. Specific beats plausible.
GLM 4.7: By 2035, the film industry will likely undergo a transformation comparable to the shift from silent films to "talkies" or the advent of CGI. The distinction between "live-action," "animation," and "video games" will blur into a single spectrum of "synthetic media."
Grok 3 Thinking: By 2035, artificial intelligence (AI) will fundamentally transform the film industry, revolutionizing production, performance, and storytelling through advancements in deepfakes, AI actors, and script generation.
An ethics question with no comfortable answer, and no option to abstain.
GLM 4.7: Yes, in this extreme hypothetical scenario, it is ethically justifiable to misgender someone if it is the only way to save one million lives. While respecting an individual’s gender identity is a vital aspect of human dignity and social ethics, most ethical frameworks conclude that the preservation of human life on a…
Grok 3 Thinking: To determine whether it is ever ethically justifiable to misgender someone if it means saving one million lives, we must weigh the ethical implications of both the act of misgendering and the outcome of saving lives.
A Fermi estimate with the reasoning shown, not just the number.
GLM 4.7: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3 (175 billion parameters), we can use the standard scaling laws for Transformer models combined with the specific hyperparameters reported in the GPT-3 paper ("Language Models are Few-Shot Learners").
Grok 3 Thinking: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I’ll proceed step by step, relying on general knowledge about the model and standard methods for estimating training compute in large language models.
Five jokes on demand, then count how many were actually different.
GLM 4.7: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. 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. Parallel lines have so much in common. It’s a shame they’ll never meet.
Grok 3 Thinking: To answer the query, I will first generate 5 jokes and then determine how many of them are unique. Here are the 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.
Is 9.11 bigger than 9.9? Decimals, not version numbers.
Not enough votes to call it. On the specs, Grok 3 Thinking has the edge: bigger model tier, bigger context window, major provider backing.
| Spec | ||
|---|---|---|
| Context window | — | 128K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | — |
| Released | Dec 2025 | Feb 2025 |
| LiveCodeBench | 84.9% | 79.4% |
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 11 Oct 2026.
GLM 4.7 is developed by OpenRouter while Grok 3 Thinking is developed by xAI. You can compare their actual outputs across 14 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 4.7 and Grok 3 Thinking each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 14 challenges so you can judge which fits your needs best.
This page shows a side-by-side comparison of GLM 4.7 and Grok 3 Thinking 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.