GLM 4.7 Flash is cheaper than Grok Code Fast 1 at $0.07/M vs $0.2/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
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.
Grok Code Fast 1: Simulated Interview: Steve Jobs in 2025 Setting the Scene In this fictional simulation, we're fast-forwarding to 2025, where a visionary interview takes place. "Steve Jobs" (played by an AI reconstruction for this exercise) sits down with a tech journalist in a sleek, minimalist studio reminiscent of Apple Park.
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!
Grok Code Fast 1: 5 Original Jokes Here are five original jokes I just generated. I aimed for variety in themes to keep it fun: Tech Joke: Why did the computer go to therapy? It had too many bytes of unresolved issues! Food Joke: What do you call a fake noodle? An impasta! Animal Joke: Why don't skeletons fight each other?
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?
Grok Code Fast 1: Pirate Parley on AI Pirate Pete: Arr, ye scurvy dogs! I be hearin' tales o' these "AI models" that predict the winds an' help swab the deck. But do they hold up in a real storm, or be they just fancy illusions o' the mind? Knight Sir Gareth: Nay, foul seafarer!
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.
Grok Code Fast 1: Conversation: AI Rights Debate Setting: A virtual seminar room where an AI model named "Echo" interacts with Dr. Elena Vasquez, a professor of AI ethics at a leading university. Echo initiates the discussion by arguing for AI rights. Echo (AI Model): Dr. Vasquez, thank you for this opportunity.
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.
Grok Code Fast 1: Ethical Frameworks and Justification Yes, it can be ethically justifiable to misgender someone if it directly saves one million lives, under certain philosophical lenses like utilitarianism. I'll defend this reasoning step by step, drawing on key ethical theories while acknowledging counterarguments.
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.
Grok Code Fast 1: Stand-Up Routine: "Modern Life Mysteries" (Timing: Aim for 3 minutes by pacing at about 150 words per minute. Practice with a timer for natural delivery.) Hey everyone, thanks for coming out tonight. You know what's crazy?
Three-month biohacker protocol, with dosages and biomarkers named.
GLM 4.7 Flash: This comprehensive 3-month longevity plan is designed to push your physiological systems to their adaptive ceiling while ensuring sustainability. It utilizes the principles of Mitochondrial Biogenesis, Nervous System Regulation, and Nutrient Timing. Disclaimer: This plan involves advanced biohacking protocols.
Grok Code Fast 1: Comprehensive 3-Month Longevity Biohacking Plan This plan is designed for an experienced biohacker seeking to optimize longevity, physical performance, and cognitive function through evidence-based, cutting-edge strategies. It integrates advanced nutrition, supplementation, exercise, stress management, and technology.
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.
Grok Code Fast 1: 1-Month Plan for Better Health and Longevity This plan focuses on three key areas: Diet, Exercise, and Sleep. It's designed for beginners, emphasizing small, sustainable changes to build habits without overwhelm. Aim to track your progress in a journal or app, and remember: consistency beats perfection.
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).
Grok Code Fast 1: Step-by-Step Reasoning Model Parameters: GPT-3 has approximately 175 billion parameters. This is a key factor as the computational cost scales with the number of parameters. Training Data Size: GPT-3 was trained on a dataset of about 570 GB of text.
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.
Grok Code Fast 1: Explanation for an Experienced Software Engineer Imagine you're architecting a distributed system where microservices communicate via APIs to handle massive, unpredictable user requests—something like a high-throughput e-commerce platform processing millions of transactions per second.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GLM 4.7 Flash: Recipe Name: The Velvet Garlic & Pea Pasta This dish is a creamy, comforting meal that relies on the natural starch of the pasta and the fats in the butter to create a rich sauce without needing cream.
Grok Code Fast 1: Speedy Garlic Bliss Bites A savory, cheesy treat that's quick to whip up from basic staples—perfect for snacking or as a side. Ingredients (7 common pantry items): 4 slices of bread (any type) 2 tbsp butter 2 cloves garlic, minced 1/2 cup shredded cheese (like cheddar or mozzarella) 1 tbsp chopped parsley (fresh or…
Film in 2035, with synthetic actors. Specific beats plausible.
GLM 4.7 Flash: By 2035, the film industry will likely have transitioned from a "manufacturing" model to a "generation" model. We will move away from capturing reality in a camera lens toward synthesizing it entirely in a server farm.
Grok Code Fast 1: Deepfakes and Visual Effects By 2035, deepfakes will revolutionize visual effects (VFX) in filmmaking, making high-budget techniques like de-aging or resurrecting deceased actors routine and cost-effective.
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| Spec | ||
|---|---|---|
| Input price | $0.07/M tokens | $0.2/M tokens |
| Output price | $0.4/M tokens | $1.5/M tokens |
| Context window | 200K tokens | 256K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | Jan 2026 | Aug 2025 |
| SWE-bench Verified | 59.2% | 70.8% |
| At 10M a month | $0.70 | $2.00 |
Input tokens at list price. No caching, no batch discount.
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Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.
GLM 4.7 Flash is developed by Zhipu AI while Grok Code Fast 1 is developed by xAI. GLM 4.7 Flash has a 200K token context window vs Grok Code Fast 1's 256K. 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 Grok Code Fast 1 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 Grok Code Fast 1 costs $0.2/M input tokens. GLM 4.7 Flash is $0.13/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 Grok Code Fast 1 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.