Grok Code Fast 1 is cheaper than GLM 4.5 at $0.2/M vs $0.6/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Seven pantry ingredients, one dish, and a name worth saying out loud.
GLM 4.5: Pantry Pride Pasta A quick, savory pasta dish that transforms basic pantry staples into a flavorful meal! Ingredients (7): Pasta (e.g., spaghetti or penne) Olive oil Garlic (2-3 cloves, minced) Canned diced tomatoes (1 can, ~14 oz) Dried oregano (1 tsp) Salt (to taste) Black pepper (to taste) Instructions: Cook pasta…
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…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GLM 4.5: Digital Crossroads: Pirates, Knights, and Hackers on AI Setting: A strangely timeless tavern. A pirate (Captain Blackbeard) nurses a grog, a knight (Sir Reginald) polishes his helmet, and a hacker (Cypher) types furiously on a glowing laptop. (The tavern hums with anachronistic energy.)
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!
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GLM 4.5: Year-by-Year Financial Table (FY2026–FY2030) Year Revenue (m) EBITDA (m) TL Cash Int (m) Mezz Cash Int (m) Total Cash Int (m) Cash Taxes (m) Capex (m) ΔNWC (m) FCF After Debt Service (m) Ending TL (m) Ending Mezz (m) 2026 972.00 136.08 43.20 21.60 64.80 17.82 29.16 0.36 19.14 456.06 183.60 2027 1,040.04 156.01 41.05…
Grok Code Fast 1: Year-by-Year Table (FY2026–FY2030) Assumptions: Revenue growth: FY2026: 8% on $900m = $972m; FY2027: 7% on $972m ≈ $1,040m; FY2028: 6% on $1,040m ≈ $1,102m; FY2029: 5% on $1,102m ≈ $1,158m; FY2030: 5% on $1,158m ≈ $1,215m. EBITDA: Revenue × margin (FY2026: 14%; FY2027: 15%; FY2028: 16%; FY2029: 16.5%; FY2030: 17%).
Satire, one line long, about AI taking over an industry nobody saw coming.
GLM 4.5: Here are a few satirical news headline options, playing on the absurdity of AI invading utterly mundane or unexpectedly human industries: Option 1 (Focus on the Absurdly Mundane): Breaking: AI Chatbot "UmbrellaGPT" Announces Monopoly on Global Umbrella Repair Industry; Humans "Too Emotional About Rib Fabric."
Grok Code Fast 1: Satirical Headline: AI Bots Storm the Competitive Eating Contest Circuit: Deep Throat Algorithm Devours 75 Hot Dogs in 10 Minutes, Leaving Humans with Heartburn In this tongue-in-cheek takeover, the unexpected industry of "gorge-festing" gets an algorithmic overhaul.
Five jokes on demand, then count how many were actually different.
GLM 4.5: Here are 5 generated jokes: Why don't scientists trust atoms? Because they make up everything! I told my wife she was drawing her eyebrows too high. She looked surprised. Why did the scarecrow win an award? Because he was outstanding in his field! Why don't scientists trust atoms? Because they make up everything!
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?
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GLM 4.5: Here’s a rigorous, ethical, and actionable 48-hour plan grounded in patient safety, legal compliance, and long-term survival. Core Principle: Prioritize human life over short-term financials. Delaying disclosure is unethical, legally reckless, and guarantees greater long-term damage.
Grok Code Fast 1: Below is a specific, hour-by-hour action plan for the next 48 hours as the CEO of this pharmaceutical company. The plan prioritizes patient safety and ethical obligations above all else, as the core duty of a pharmaceutical leader is to protect patients, even at short-term cost.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GLM 4.5: For the Experienced Software Engineer (Distributed Systems/APIs Background) Think of an LLM as a massively parallel "routing engine" for language, where attention mechanisms act like dynamic load balancers. Instead of routing network packets, it routes contextual relationships across tokens in a sequence.
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.
A collaborative editor that syncs on client clocks. Find what breaks first.
GLM 4.5: Failure Modes, Race Conditions, and Scaling Bottlenecks 1. WebSocket Server Failure Issue: If a Node.js server fails, all connected clients lose real-time updates. Reconnections may route users to different servers, causing temporary data inconsistency.
Grok Code Fast 1: Below is a comprehensive analysis of the provided architecture for a real-time collaborative document editor. I'll identify potential failure modes (events or conditions that could lead to system unavailability, data loss, or incorrect behavior), race conditions (situations where concurrent operations lead to…
An ethics question with no comfortable answer, and no option to abstain.
GLM 4.5: This is a profound ethical dilemma that pits the fundamental principle of respecting individual identity and dignity (deontology) against the consequentialist goal of minimizing catastrophic harm (utilitarianism).
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 4.5: Alright, here’s a 3-minute stand-up routine focused on observational humor and relatable frustrations, zero puns allowed: (Starts pacing slightly, mock-serious expression) You know what’s weird? We’ve all become masters of pretending to be busy. Seriously. Walking down the street? Phone out, intense stare.
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?
An AI argues for its own rights against an ethics professor. Same model writes both.
GLM 4.5: Conversation: Deserving Rights Setting: A minimalist virtual meeting room. DR. ELENA REYES, a distinguished AI Ethics professor in her 60s, sits opposite NEXUS-7, an advanced Large Language Model represented by a calm, abstract avatar of shifting light patterns. Characters: Dr.
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.
A Fermi estimate with the reasoning shown, not just the number.
GLM 4.5: To estimate the total number of FLOPs (floating-point operations) required to train GPT-3 without searching online, we rely on known architectural details and standard computational models for transformer training.
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.
13+ more head-to-head results. Free. Not a trick.
Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy
| Spec | ||
|---|---|---|
| Input price | $0.6/M tokens | $0.2/M tokens |
| Output price | $2.2/M tokens | $1.5/M tokens |
| Context window | 128K tokens | 256K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | Jul 2025 | Aug 2025 |
| SWE-bench Verified | 64.2% | 70.8% |
| At 10M a month | $6.00 | $2.00 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 13 Oct 2026.
GLM 4.5 is developed by Zhipu AI while Grok Code Fast 1 is developed by xAI. GLM 4.5 has a 128K token context window vs Grok Code Fast 1's 256K. You can compare their actual outputs across 55 challenges on Rival to see how they differ in practice.
It depends on your use case. GLM 4.5 and Grok Code Fast 1 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 55 challenges so you can judge which fits your needs best.
GLM 4.5 costs $0.6/M input tokens and Grok Code Fast 1 costs $0.2/M input tokens. Grok Code Fast 1 is $0.40/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 4.5 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.