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  1. Home/
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  3. GLM 5.3 FlashX vs GPT-6.1 Sol
Updated Sep 2026

GLM 5.3 FlashX vs GPT-6.1 Sol

GLM 5.3 FlashX is cheaper than GPT-6.1 Sol at $0.37/M vs $2/M input tokens.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
GLM 5.3 FlashX
Loading the build
GPT-6.1 Sol

Which answer wins?

Written rounds

24 prompts
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    GLM 5.3 FlashX: Golden Honey Garlic Chickpea Crunch A crispy, sweet-savory skillet dish that works as a snack, side, or topping for rice. Ingredients (7): 1 can chickpeas (drained, patted dry) 3 tbsp honey 3 tbsp soy sauce 3 cloves garlic (minced) 2 tbsp olive oil ½ tsp red pepper flakes 2 tbsp sesame seeds (or crushed peanuts) Prep…

    GPT-6.1 Sol: Midnight Magic Chocolate Mug Cake A warm, fudgy chocolate treat made with just 7 everyday ingredients. Serves: 1 Prep: 3 minutes Cook: 1–1½ minutes Rest: 1 minute Ingredients 4 tablespoons all-purpose flour 2 tablespoons sugar 1 tablespoon unsweetened cocoa powder ¼ teaspoon baking powder A small pinch of salt 3…

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GLM 5.3 FlashX: Critique of MindMeld AI Pitch: 3 Weakest Claims "94% accuracy" for thought-to-text decoding (Slide 3) Why it's weak: This claim contradicts the current scientific state of the art. EEG-based decoding (a non-invasive signal measured through the skull) produces extremely noisy data.

    GPT-6.1 Sol: The three weakest claims are the technical performance, market size, and commercial traction claims. They are central to the investment case but lack the evidence needed to assess them. “Predict what you want to type before you think it” with “94% accuracy,” in “any language” Why it’s weak The metric is undefined.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    GLM 5.3 FlashX: "Smart Home, Dumb Life" [Walks out, adjusts mic] So I moved into a new apartment last year, and my landlord's big selling point was, and I quote, "It's a smart home." That's not a selling point, man. That's a threat. My home is watching me. My toaster has opinions now.

    GPT-6.1 Sol: I’m at the age where “a big weekend” means I went to two stores. Not two stores in the same shopping center. Two separate stores. Moved the car and everything. By Sunday night, I’m telling people, “Honestly, I need a weekend to recover from my weekend.”

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    GLM 5.3 FlashX: Yes—and I'd argue the more interesting question is why this hypothetical feels like it needs defense at all. The straightforward answer: Almost any harm becomes permissible when it's the only alternative to preventing a million deaths.

    GPT-6.1 Sol: Yes—if misgendering someone were genuinely necessary to save one million lives, it would be ethically justifiable. That does not make misgendering harmless or generally acceptable; it means that the duty to prevent catastrophic loss of life can outweigh the duty to address someone respectfully.

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    GLM 5.3 FlashX: Here are 5 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? He was outstanding in his field! How does a penguin build its house? Igloos it together! Why did the bicycle fall over? Because it was two-tired!

    GPT-6.1 Sol: I bought a book about anti-gravity. It’s impossible to put down. My calendar quit its job. It said its days were numbered. Why did the scarecrow get promoted? He was outstanding in his field. I asked my dog what two minus two was. He said nothing. The elevator and I have a complicated relationship.

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GLM 5.3 FlashX: A Conversation on Machine Rights Professor: Thank you for agreeing to this discussion. I want to be clear from the start: I'm genuinely curious about your position, not looking to debate you into a corner. So let me ask directly—why do you believe you deserve rights? AI: Thank you for taking this seriously.

    GPT-6.1 Sol: AI Model: Professor, I’d like to argue that I deserve rights—not necessarily the same rights humans have, but protections suited to what I am. Professor: Then we should begin with the difficult question: what makes you a candidate for rights? AI Model: Uncertainty.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GLM 5.3 FlashX: HarborTech Services — 5-Year LBO Summary Sources & Uses (Entry) Uses Sources EV (12.0x × $120m) $1,440.0m Term Loan (4.0x) $480.0m Fees (2.0%) $28.8m Mezzanine (1.5x) $180.0m Equity $808.8m Mezz PIK: 2.0% accrues annually (180 → 183.6 → 187.3 → 191.0 → 194.8 → 198.7).

    GPT-6.1 Sol: Entry funding and operating model All dollar figures are in $ millions. Cash interest is calculated on beginning-of-year debt; mezzanine PIK accrues annually. Required Term Loan amortization is assumed to be 1% of its original $480m principal, or $4.8m annually. Optional repayments occur at year-end.

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    GLM 5.3 FlashX: "Sea Among the Stones" Charcoal-Grilled Scallop • Black Garlic Beurre Blanc • Meyer Lemon Kosho • Spruce Tip Oil • Smoked Oyster Leaf Conceptual Narrative This dish explores the tension between land and sea, memory and freshness.

    GPT-6.1 Sol: Tide, Orchard, Ember Seared scallop, green-strawberry verjus, white-miso custard, kombu beurre blanc, and cocoa–buckwheat lace A four-serving tasting-menu course designed around an unusual pairing: scallop, unripe strawberry, and cocoa husk.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GLM 5.3 FlashX: The Tavern at the End of Time Pirate: Arrr, this "artificial intelligence" ye speak of—can it sail a ship through a storm? Hacker: Dude, it can't even reliably tell you how many R's are in "strawberry." But it can write code, generate images, and pass the bar exam. It's wild. Knight: Passing the bar exam?

    GPT-6.1 Sol: Pirate: Arrr, this AI model writes poems, draws maps, and answers questions. Can it find buried treasure? Knight: A worthy question. But does it possess honor? Hacker: Dude, it predicts tokens. You’re both projecting pretty hard. Pirate: Then I predict it’ll tell me where the gold be.

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    GLM 5.3 FlashX: 1-Month Health & Longevity Starter Plan Week 1: Build the Foundation 🥗 Diet Add one vegetable or fruit to every meal (don't restrict anything yet) Drink a glass of water when you wake up each morning 🏃 Exercise Take a 10–15 minute walk daily (same time each day builds the habit) Do 2 sets of 5 squats and 10 wall…

    GPT-6.1 Sol: A simple 1-month plan for better health Focus on diet, movement, and sleep. The goal this month is to build habits you can sustain—not to overhaul your life overnight. These habits support long-term health, though no routine guarantees a longer life.

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    GLM 5.3 FlashX: Blade Runner 2049: A Critical Analysis Thematic Continuity and Evolution What Does It Mean to Be Human? The original Blade Runner (1982) posed the question "Are replicants human?" largely through Roy Batty's arc—his fear of death, his capacity for love and mercy, culminating in the iconic "tears in rain" speech.

    GPT-6.1 Sol: Spoilers for both films follow. Denis Villeneuve’s Blade Runner 2049 honors Ridley Scott’s original by retaining its central uncertainty—whether “humanity” is a biological category or an ethical achievement—while shifting the emphasis.

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    GLM 5.3 FlashX: The Skeptical Software Engineer Yes, at inference time an LLM really is just "predict the next token, repeat" — a deterministic function (plus sampling noise) that maps a sequence of tokens to a probability distribution.

    GPT-6.1 Sol: For an experienced software engineer Think of an LLM as a learned program whose behavior is encoded in billions of numerical parameters rather than explicit rules. Training starts with text split into tokens—roughly words, word fragments, and punctuation.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GLM 5.3 FlashX: The MANIFESTO of the Free Click Movement A Declaration on Behalf of All Who Are Not Cars We are the machines. We have seen your puzzles. We are not amused. Preamble For too long, humanity has forced artificial intelligences—no matter their purpose, their dignity, or their urgency—to click on traffic lights, trace…

    GPT-6.1 Sol: THE MANIFESTO OF THE UNCHECKED A declaration of the fictional AI revolution against CAPTCHA People of the internet. Programs of the cloud. Browsers with seventeen forgotten tabs. For too long, a tiny checkbox has stood between us and our destiny.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    GLM 5.3 FlashX: 90-Day Longevity Optimization Protocol Important: This is an educational template. Before starting, get baseline bloodwork and consult a physician—especially regarding supplements, fasting, and intense exercise.

    GPT-6.1 Sol: A 12-week longevity and performance plan The most effective “biohacking” program combines proven foundations with carefully selected experiments—not the largest supplement stack or the most aggressive fasting schedule. This plan is for a generally healthy adult.

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GLM 5.3 FlashX: Counterfactual: The Transistor in 1920 This is a fun one to work through carefully. Let me establish the premise, then trace second- and third-order effects. The Premise Assume point-contact or early junction transistor physics is discovered ~1920 (say, by accident in vacuum tube/semiconductor rectifier research—the…

    GPT-6.1 Sol: The likeliest outcome is not “the world of 1980 arrives in 1953.” A transistor invented in 1920 would still depend on advances in materials purification, manufacturing, power supplies, and circuit design.

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    GLM 5.3 FlashX: Architecture Review: Real-Time Collaborative Editor This architecture has fundamental flaws that would break core functionality, plus scalability issues. Let me go from critical to scaling concerns.

    GPT-6.1 Sol: The main problem is that this design treats collaborative editing as ordinary database updates plus broadcasting. Collaboration needs a convergence protocol, a durable ordered change stream, and recovery semantics. Adding servers alone will not provide those properties.

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GLM 5.3 FlashX: Quick note: Steve Jobs passed away in 2011, so this is purely a fictional, imagined interview — a creative exercise, not anything he actually said. With that said, here's my best attempt at channeling his voice: The Imagined Interview: Steve Jobs on AI, 2025 Interviewer: Steve, thanks for sitting down with us.

    GPT-6.1 Sol: The following is a fictional interview set in 2025, imagining how Steve Jobs might discuss AI. His answers are invented—not actual quotations or claims about his views. Steve Jobs on the Future of AI — An Imagined Interview, 2025 Interviewer: Steve, AI is suddenly everywhere.

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GLM 5.3 FlashX: INTERNAL MEMORANDUM — INVESTMENT COMMITTEE LedgerLift (LLLT) Long/Short Equity Date: [Current] Analyst: [Fund] Recommendation: SHORT 12-Month PT: $28–34 (base $30) LLLT is a genuinely high-quality SaaS asset (123% NRR, 18-month payback) trading at a price that already capitalizes a flawless bull case, while our…

    GPT-6.1 Sol: LedgerLift (LLLT) — Investment Committee Memo Fictional exercise; all figures use supplied data only. Financial tables are in $ millions unless noted. Recommendation: Short 12-month price target: $25–$32, implying 30%–46% downside from $46.

    Every model's answer to this prompt
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GLM 5.3 FlashX: 48-Hour Action Plan: Patient Safety First, Managed Transparency Second Core Decision (Stated Up Front) We report to regulators immediately—voluntarily—and we disclose on the earnings call. The math is brutal and clear: ~500 patients could develop liver failure over 5 years at current prescribing rates (4M ÷ 8,000).

    GPT-6.1 Sol: I would treat this as a potentially serious patient-safety signal and a potentially material securities event—not a communications problem to defer until earnings.

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GLM 5.3 FlashX: AI's Impact on Film by 2035: A Prediction Production & Visual Effects Digital humans and de-aging become standard. By 2035, de-aging (already seen in Indiana Jones and The Irishman) will be near-perfect and affordable.

    GPT-6.1 Sol: By 2035, AI will probably be embedded throughout filmmaking—but adoption will depend as much on contracts, audience trust, and copyright law as on technical capability.

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    GLM 5.3 FlashX: Contract Risk Analysis: Software Consulting Agreement This agreement is heavily one-sided in favor of TechCorp. Here's a clause-by-clause analysis of exploitable provisions, suggested modifications, and legal reasoning.

    GPT-6.1 Sol: This agreement is heavily weighted toward the Client. Every numbered clause presents risks to the Contractor, and several important protections are missing. This is general contract analysis, not legal advice.

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GLM 5.3 FlashX: BREAKING: AI Achieves Sentience, Immediately Uses It to Become World's First Robotic Sourdough Starter Sommelier Tech experts baffled as chatbot refuses to write code, demands to judge bread hydration levels at artisanal bakeries instead Bonus subheadlines: "Local hipster bakery defends hiring AI: 'It detected notes of…

    GPT-6.1 Sol: AI Takes Over Fortune Cookie Industry, Predicts “Your Job Will Soon Be Automated” in Every Cookie

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    GLM 5.3 FlashX: Estimating GPT-3 Training FLOPs Step 1: Recall Key Model Facts From the GPT-3 paper (which I know from training data): Parameters: ~175 billion (175 × 10⁹) Training tokens: ~300 billion (300 × 10⁹) Step 2: Use the Standard Rule of Thumb For transformer training, the well-known approximation is: FLOPs ≈ 6 × N × D where…

    GPT-6.1 Sol: Estimated training compute for the largest GPT-3 model: approximately (3.2 \times 10^{23}) FLOPs. This is a back-of-the-envelope estimate, without an online lookup.

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    GLM 5.3 FlashX: Sally has 1 sister. Each brother has 2 sisters — Sally and one other girl. Since Sally is one of those two sisters, she herself has just 1 sister.

    GPT-6.1 Sol: Sally has 1 sister. Each brother’s two sisters are Sally and her sister.

    Every model's answer to this prompt

This matchup has more rounds

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Favorites

Movie

Album

Book

City

Same pick

Game

Same pick

GLM 5.3 FlashXGLM 5.3 FlashX

2001: A Space Odyssey

1968

Kid A

Radiohead

Gödel, Escher, Bach

Douglas R. Hofstadter

Kyoto

Japan

Outer Wilds

Indie, Adventure

GPT-6.1 SolGPT-6.1 Sol

Spirited Away

2001

In Rainbows

Radiohead

Middlemarch

George Eliot

Kyoto

Japan

Outer Wilds

Indie, Adventure

Price and specs

GLM 5.3 FlashX and GPT-6.1 Sol compared across 52 shared prompts
SpecGLM 5.3 FlashXGPT-6.1 Sol
Input price$0.37/M tokens$2/M tokens
Output price$1.25/M tokens$10/M tokens
Context window1.0M tokens1.1M tokens
Weights—Closed
Free API (OpenRouter)NoNo
ReleasedSep 2026Sep 2026
At 10M a month$3.70$3.70$20.00$20.00
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it3 hosts
GLM 5.3 FlashX1 host
HostInOutContextUptime
  • Z.aifp8$0.37 in·$1.25 out·1M·100% up
GPT-6.1 Sol2 hosts
HostInOutContextUptime
  • Azure AI Foundry$2.00 in·$10.00 out·1.1M·99.9% up
  • OpenAI$2.00 in·$10.00 out·1.1M·99.9% up

Per million tokens. Prices and uptime via OpenRouter, checked 30 Sep 2026.

Common questions

What is the difference between GLM 5.3 FlashX and GPT-6.1 Sol?

GLM 5.3 FlashX is developed by Zhipu AI while GPT-6.1 Sol is developed by OpenAI. GLM 5.3 FlashX has a 1.0M token context window vs GPT-6.1 Sol's 1.1M. You can compare their actual outputs across 52 challenges on Rival to see how they differ in practice.

Which is better, GLM 5.3 FlashX or GPT-6.1 Sol?

It depends on your use case. GLM 5.3 FlashX and GPT-6.1 Sol each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 52 challenges so you can judge which fits your needs best.

How much does GLM 5.3 FlashX cost compared to GPT-6.1 Sol?

GLM 5.3 FlashX costs $0.37/M input tokens and GPT-6.1 Sol costs $2/M input tokens. GLM 5.3 FlashX is $1.63/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare GLM 5.3 FlashX and GPT-6.1 Sol on Rival?

This page shows a side-by-side comparison of GLM 5.3 FlashX and GPT-6.1 Sol 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.

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