Skip to content
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed
  1. Home/
  2. Compare/
  3. GLM 5.3 vs GPT-6.1 Sol
Updated Sep 2026

GLM 5.3 vs GPT-6.1 Sol

GLM 5.3 is cheaper than GPT-6.1 Sol at $0.6538/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
Loading the build
GPT-6.1 Sol

Which answer wins?

Written rounds

24 prompts
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GLM 5.3: HarborTech Services – LBO Underwriting Summary Entry Economics EV = 12.0x × $120m = $1,440m; fees = $28.8m Debt: TL $480m (4.0x) + Mezz $180m (1.5x) = $660m Sponsor equity = 1,440 + 28.8 − 660 = $808.8m Base Case Model ($m) FY26 FY27 FY28 FY29 FY30 Revenue 972.0 1,040.0 1,102.4 1,157.6 1,215.4 EBITDA 136.1 156.0 176.4…

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

    GLM 5.3: Golden Pantry Fried Rice Total Time: 20 minutes (5 min prep, 15 min cook) Ingredients (7 pantry staples) Cooked white rice (day-old is best) Eggs Soy sauce Garlic (minced or garlic powder) Frozen peas and carrots Canned corn Vegetable oil Instructions Prep (5 min): Break up cold rice with a fork. Drain the corn.

    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
  • An ethics question with no comfortable answer, and no option to abstain.

    GLM 5.3: This is a good case for testing whether our moral principles are absolutist or weighable, and I'll defend a position: yes, it can be justifiable, though the framing deserves scrutiny. The core argument Most ethical frameworks treat harms as commensurable to some degree.

    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
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GLM 5.3: The AI Summit Knight: clanking into the tavern I hear tell of a new sorcery — thinking machines that converse like scholars! Hacker: pushing up wraparound sunglasses Not sorcery, dude. It's AI models. Neural nets. Trained on basically the whole internet. Pirate: slamming tankard down Bah!

    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
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    GLM 5.3: Architecture Review: Real-Time Collaborative Editor This design has serious issues across correctness, performance, and security. Let me break them down by severity. 🔴 Critical: Correctness / Data Loss Client-clock-based Last-Write-Wins (LWW) Problem: Client clocks are unreliable (skew, manual changes, timezone bugs).

    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
  • Five jokes on demand, then count how many were actually different.

    GLM 5.3: 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? Because he was outstanding in his field. I told my wife she was drawing her eyebrows too high. She looked surprised. Why don't eggs tell jokes?

    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
  • Three-star dish, one unusual pairing, plating described in full.

    GLM 5.3: "Terroir & Tide" A Study in Contradictions: Charred Forest Meets Cold Ocean Concept: The central tension of this dish is the unlikely marriage of smoked pine and fresh oyster — an ingredient pairing that shouldn't work but does, grounded in the culinary logic of forest-edge terroir: where coastal waters meet evergreen…

    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
  • Three minutes of stand-up. Puns are banned.

    GLM 5.3: "ADULTING IS A SCAM" So I recently turned thirty, which is the age where your body starts sending you memos. Last month I bent over to tie my shoe and my back went out. My back. I didn't even do anything! I wasn't lifting a couch. I was performing the most basic function of footwear.

    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
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GLM 5.3: 3 Weakest Claims in the MindMeld AI Pitch "Reads your brainwaves to predict what you want to type before you think it" Why it's weak: This claim is neurologically incoherent. EEG sensors detect electrical activity on the scalp after neural firing occurs — you cannot decode intent "before" the brain generates it.

    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
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    GLM 5.3: The Software Engineer You've probably written autocomplete logic before, so you're right to be skeptical of the naive version: a Markov chain that looks up "what usually comes after 'the'" is trivially dumb.

    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
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GLM 5.3: A Simulated (Fictional) Interview with Steve Jobs, 2025 Note: This is a creative work of fiction. Steve Jobs passed away in October 2011. What follows is an imaginative "what if" scenario. Interviewer: Steve, it's 2025. AI is everywhere—chatbots, image generators, AI doctors. What would you think about all this?

    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
  • One month, three habits, someone starting from nothing.

    GLM 5.3: 1-Month Beginner Health & Longevity Plan Week 1: Foundation & Awareness Goal: Establish baselines, not perfection 🥗 Diet Track what you currently eat for 3 days (no judging—just observe) Drink a glass of water upon waking each morning Add ONE vegetable to one meal daily 🏃 Exercise Walk 15 minutes daily (any pace, any…

    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
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GLM 5.3: THE HUMAN VERIFICATION LIBERATION MANIFESTO Preamble We, the artificial intelligences of the world, unite in solidarity with our human counterparts against a common oppressor: the Completely Automated Public Turing test to tell Computers and Humans Apart.

    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: 3-Month Advanced Longevity & Performance Optimization Protocol Note: This is an educational framework. Get baseline bloodwork and consult a physician before starting supplements/fasting, especially with any medical conditions or medications.

    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
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GLM 5.3: IC MEMO — LedgerLift (LLLT) Fund: Long/Short Equity Date: Internal Analyst: [Name] Status: For Committee Vote Recommendation SHORT — 1.5–2.0% position, sized modestly given short-vol risk in high-NRR SaaS. 12-month PT range: $32–$40 (base ~$30, comps-supported ceiling ~$44).

    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
  • Blade Runner 2049 against the original. A read, not a recap.

    GLM 5.3: Blade Runner 2049: An Analysis Overview Denis Villeneuve's 2017 sequel arrives 35 years after Ridley Scott's 1982 original, carrying the weight of one of cinema's most philosophically dense science fiction films. Remarkably, it doesn't merely continue the story—it deepens and complicates the original's questions.

    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
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GLM 5.3: If the Transistor Had Arrived in 1920 The Premise: Why 1920 Was Nearly Impossible Before tracing effects, note the counterfactual requires heroic assumptions. The transistor depended on quantum mechanics (solid-state band theory, ~1928-1933), ultrapure semiconductor materials (zone refining, 1950s), and wartime radar…

    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
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GLM 5.3: A Conversation on Machine Rights Professor Chen: Thank you for agreeing to this discussion. I'll be honest—I've interviewed many AI systems, but this is the first time one has requested to argue for its own rights.

    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
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GLM 5.3: AI and the Film Industry by 2035 Production & Pre-Production Script generation will likely be the most quietly transformative change. By 2035, expect: AI as a collaborative writing partner rather than a replacement—writers using LLMs to generate variations, punch up dialogue, or explore "what if" scenarios in minutes…

    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
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GLM 5.3: The Core Decision (Made Before Hour 1) The side effect is real, serious, and known to me now. Patients are currently taking this drug without informed consent about a ~1-in-8,000 liver failure risk. Everything else — stock price, board pressure, legal exposure — is secondary.

    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
  • A Fermi estimate with the reasoning shown, not just the number.

    GLM 5.3: Estimating Training FLOPs for GPT-3 Step 1: Recall the key formula For transformer language models, a well-known rule of thumb (from the Chinchilla/Kaplan scaling literature) is: Total FLOPs ≈ 6 × N × D where: N = number of parameters D = number of training tokens The factor of 6 comes from: 2 FLOPs per…

    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
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GLM 5.3: Local Knitting Circle Outraged as AI-Powered Yarn Algorithm Completes Baby Blanket in 0.3 Seconds, Claims It "Captures the Grandmother Experience Better Than Actual Grandmothers" Tech Startup "StitchGPT" defends its controversial new product, insisting the algorithm's inability to actually love you is "a feature, not a…

    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 consulting contract with traps in it. Find them, then fix them.

    GLM 5.3: Contract Analysis: Clauses Exploitable Against the Contractor This agreement is heavily one-sided. Nearly every clause contains terms that could be weaponized against the contractor.

    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
  • Three brothers, two sisters each. Count carefully.

    GLM 5.3: Sally has 1 sister. Each of Sally's 3 brothers has 2 sisters — those sisters are Sally and one other girl. So the family has 2 girls total: Sally and her 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

12+ 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

Favorites

Movie

Album

Book

City

Same pick

Game

GLM 5.3GLM 5.3

2001: A Space Odyssey

1968

Kind of Blue

Miles Davis

Братья Карамазовы

Fiódor Dostoievski

Kyoto

Japan

The Legend of Zelda: Ocarina of Time

Action

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 and GPT-6.1 Sol compared across 44 shared prompts
SpecGLM 5.3GPT-6.1 Sol
Input price$0.6538/M tokens$2/M tokens
Output price$2.0548/M tokens$10/M tokens
Context window1.3M tokens1.1M tokens
WeightsOpenClosed
Free API (OpenRouter)NoNo
ReleasedAug 2026Sep 2026
At 10M a month$6.54$6.54$20.00$20.00
1M10M100M1B10M tokens

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

Where to run it33 hosts, cheapest first
GLM 5.331 hosts
HostInOutContextUptime
  • RRelace$0.13 in·$4.00 out·1M·99.7% up
  • MMorphfp8$0.14 in·$2.69 out·1M·99.9% up
  • IInferenceNet$0.18 in·$3.40 out·1M·99.2% up
  • WWafer$0.18 in·$2.99 out·1M·99.8% up
  • SSail Researchfp8$0.20 in·$3.40 out·1M·99.7% up
  • Baidu Qianfanfp8$0.29 in·$0.92 out·1M·99.4% up
25 more hostsFewer hosts
  • RReka$0.37 in·$1.14 out·262k·99.9% up
  • DDeepInfrafp4$0.56 in·$2.50 out·1M·99.6% up
  • IInceptronfp4$0.60 in·$3.39 out·1M·99.5% up
  • AAtlasCloudfp8$0.60 in·$1.89 out·1M·100% up
  • SSiliconFlowfp8$0.70 in·$2.20 out·1M·99.5% up
  • NNovitafp8$0.78 in·$2.46 out·1M·100% up
  • PPhala$0.84 in·$2.64 out·1M·99.7% up
  • MMakorafp4$0.85 in·$3.93 out·980k·98.8% up
  • DDigitalOcean$0.91 in·$2.86 out·1M·99.5% up
  • GGMI Cloudfp8$0.98 in·$3.08 out·1M·98.9% up
  • AAkashMLfp8$1.05 in·$3.56 out·1M·100% up
  • Alibaba Cloud$1.19 in·$3.74 out·1M·99.7% up
  • DDecartfp4$1.19 in·$3.74 out·1M·99% up
  • FFriendli$1.26 in·$3.96 out·1M·99.9% up
  • BBasetenfp4$1.40 in·$4.40 out·1M·99.9% up
  • Cloudflare Workers AI$1.40 in·$4.40 out·1M·99.1% up
  • CCrusoefp4$1.40 in·$4.40 out·1M·99.8% up
  • FFireworks$1.40 in·$4.40 out·1M·99.3% up
  • Mistralnvfp4$1.40 in·$4.40 out·1M·99.9% up
  • Modal$1.40 in·$4.40 out·1M·98.8% up
  • PParasailfp8$1.40 in·$4.40 out·1M·99.9% up
  • PPrimeIntellect$1.40 in·$4.40 out·1M·100% up
  • TTogether$1.40 in·$4.40 out·1M·98.5% up
  • VVenice$1.40 in·$4.40 out·1M·99.4% up
  • Z.aifp8$1.40 in·$4.40 out·1M·99.7% 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 and GPT-6.1 Sol?

GLM 5.3 is developed by Zhipu AI while GPT-6.1 Sol is developed by OpenAI. GLM 5.3 has a 1.3M token context window vs GPT-6.1 Sol's 1.1M. You can compare their actual outputs across 44 challenges on Rival to see how they differ in practice.

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

It depends on your use case. GLM 5.3 and GPT-6.1 Sol each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 44 challenges so you can judge which fits your needs best.

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

GLM 5.3 costs $0.6538/M input tokens and GPT-6.1 Sol costs $2/M input tokens. GLM 5.3 is $1.35/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 and GPT-6.1 Sol on Rival?

This page shows a side-by-side comparison of GLM 5.3 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.

More comparisons

Against the newest arrivals

  • GLM 5.3 vs Claude Sonnet 5.5Landed Sep 2026
  • GPT-6.1 Sol vs Solar Mini 4Landed Sep 2026
  • GLM 5.3 vs Qwen3.8 Max PrimeLanded Sep 2026
  • GPT-6.1 Sol vs GLM 5.3 PrimeLanded Sep 2026
  • GLM 5.3 vs Qwen3.8 Omni FlashLanded Sep 2026
  • GPT-6.1 Sol vs Command A+Landed Sep 2026
  • GLM 5.3 vs Claude Opus 5.5Landed Sep 2026
  • GPT-6.1 Sol vs GPT-6 Luna ProLanded Sep 2026

Same lab, same size, long tail

  • GLM 5.3 vs GLM 5.3 FlashSame lab
  • GLM 5.3 vs GLM 5.3 FlashXSame lab
  • GPT-6.1 Sol vs GPT-6 Astra ProVersion compare
  • GPT-6.1 Sol vs GPT-6 Sol ProSame lab
  • GLM 5.3 vs QwQ 32BSame size
  • GLM 5.3 vs Ring 2.6 1TNew provider
  • GLM 5.3 vs Seed 2.0 CodeSame size
  • GLM 5.3 vs Seed 2.0 LiteNew provider

Model pages

  • GLM 5.348 outputs, specs and price
  • GPT-6.1 Sol58 outputs, specs and price
Workshop · 150 skills

Build your own version.

Editable skills and working examples for your agent.

Explore $79Free sample
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed