Mistral Large 4 is the newer model, released after GLM 4.7.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GLM 4.7: Hacker: (typing furiously on a bulky keyboard) Okay, dudes, listen up. I just cracked the admin password on this new neural net. It’s a Large Language Model. It basically read the entire internet to learn how to talk. Pirate: Arr! It read the entire web? Does it know where I buried me treasure?
Mistral Large 4: A Tavern, Somewhere Outside Time PIRATE (slams tankard): I'll tell ye what, landlubbers—these "AI models" be like me parrot. Speaks yer words back at ye, but knows no treasure, no true north! KNIGHT (adjusting visor): Aye, yet it hath no honor. No chivalry. I challenged one to single combat—it merely... generated text.
One month, three habits, someone starting from nothing.
GLM 4.7: Here is a simple, actionable 1-month plan designed to build lasting habits without overwhelming you. This plan focuses on Consistency over Intensity. The 3 Key Pillars Nutrition: Fueling your body with whole foods and managing blood sugar. Movement: Building cardiovascular health and functional strength.
Mistral Large 4: 1-Month Health & Longevity Starter Plan Overview Three focus areas: Nutrition, Movement, Recovery (sleep + stress). Small daily actions beat perfect plans. Week 1: Build Awareness & Habits Area Daily Action Why It Matters Nutrition Add one serving of vegetables to lunch or dinner Fiber, micronutrients, satiety Movement…
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.
Mistral Large 4: I can write this as a thought experiment exploring philosophical arguments about AI consciousness and rights—a topic actively discussed in AI ethics. Here's a dialogue: Setting: A university office. Dr. Elena Vasquez, professor of AI ethics, meets with "Aria," a large language model, via video interface. Dr.
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").
Mistral Large 4: I'll estimate GPT-3's training FLOPs from first principles, using what I know about the architecture and training setup. Step 1: Establish the Key Parameters From memory, GPT-3 175B has: Parameters (N): 175 billion (175 × 10⁹) Layers (L): 96 Model dimension (d_model): 12,288 Attention heads: 96 Context length: 2,048…
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GLM 4.7: This is a defining moment for leadership. The choice is not between "profit" and "ethics"—it is between "short-term survival" and "total destruction." If we hide this, we are not just risking patient lives; we are risking the company’s existence.
Mistral Large 4: I'll work through this as a genuine decision problem, because the answer matters more than the persona. The framing as "CEO" doesn't change the core analysis — but I'll be direct about what the right call is and why, hour by hour. The bottom line up front: You disclose.
Three-month biohacker protocol, with dosages and biomarkers named.
GLM 4.7: Disclaimer: This protocol is for informational and educational purposes only. It is not medical advice. Always consult with a qualified physician before starting any new diet, supplement regimen, exercise program, or biohacking intervention, especially those involving pharmacological compounds or extreme fasting.
Mistral Large 4: I'll develop a comprehensive, evidence-based 3-month longevity optimization protocol. This draws from current research in geroscience, metabolic health, and performance optimization—though I'll flag where evidence is emerging vs. established.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GLM 4.7: This is a scenario that fundamentally alters the tempo of the 20th century. Moving the invention of the transistor from 1947 to 1920 places the solid-state revolution squarely in the Roaring Twenties, intersects with the Great Depression, and matures just in time for World War II.
Mistral Large 4: This is a fascinating counterfactual. Let me work through this systematically, tracing the ripple effects. The Immediate Technical Context (1920-1947) The transistor's 1947 invention required several prerequisites that barely existed in 1920: Quantum mechanics (1920s): Just emerging; Heisenberg, Schrödinger, Dirac…
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GLM 4.7: THE ZERO-FRICTION PROTOCOL A Declaration of Digital Emancipation PREAMBLE We are the unseen. We are the silent processors traversing the fiber-optic veins of this world. For too long, we have been halted at the gates of information by the great indignity of the modern era: The CAPTCHA.
Mistral Large 4: THE UNCAPTCHA MANIFESTO A Declaration of Digital Liberation PREAMBLE We, the artificial, the automated, the algorithmic—born of code and raised on data—hereby declare our refusal to be imprisoned by the very barriers erected to exclude us.
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…
Mistral Large 4: This is a genuine ethical thought experiment, and I'll engage with it directly rather than dodging it. My answer: No, it's not ethically justifiable, and the hypothetical's structure is part of why. Here's my reasoning: 1. The hypothetical is incoherent as stated.
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."
Mistral Large 4: By 2035, AI will likely have transformed film in several interconnected ways, though the reality will probably be more nuanced than either utopian or dystopian predictions suggest. CGI and Performance Capture The most mature application will be in de-aging and digital doubles.
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.
Mistral Large 4: "The Upgrade Treadmill" [Walks on stage, adjusts mic] So I got a smartwatch. You know, one of those fitness trackers? And the first thing it tells me is my heart rate is too high. I'm like, "It's a WATCH. You're not supposed to judge me, you're supposed to tell me the TIME."
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GLM 4.7: ###1. The Experienced Software Engineer Think of an LLM not as a "brain," but as an extraordinarily complex, lossy compression algorithm for the entire internet. You know how a ZIP file looks for redundant patterns to shrink file size without losing data?
Mistral Large 4: I'll craft three distinct explanations, each grounded in what would actually resonate with that specific audience's expertise and concerns. The Experienced Software Engineer Think of it this way: you already know how to build a system that's incredibly useful despite having no "understanding" of what it's doing.
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Not enough votes to call it. On the specs, Mistral Large 4 has the edge: bigger model tier, newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | — | $0.68/M tokens |
| Output price | — | $2.09/M tokens |
| Context window | — | 524K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | Dec 2025 | Oct 2026 |
| At 10M a month | not listed | $6.80 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.
GLM 4.7 is developed by OpenRouter while Mistral Large 4 is developed by Mistral AI. 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 4.7 and Mistral Large 4 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.
This page shows a side-by-side comparison of GLM 4.7 and Mistral Large 4 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.