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  1. Home/
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  3. Claude Sonnet 5.5 vs GLM 5.3
Updated Sep 2026

Claude Sonnet 5.5 vs GLM 5.3

GLM 5.3 is cheaper than Claude Sonnet 5.5 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
Claude Sonnet 5.5
Loading the build
GLM 5.3

Which answer wins?

Written rounds

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

    Claude Sonnet 5.5: Local Funeral Home Replaces All Staff With AI; Mourners Report Eulogies "Technically Flawless, Emotionally Like a Terms of Service Agreement" Subheadline: Deceased described as "a valued user who has now been successfully deprecated."

    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…

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

    Claude Sonnet 5.5: A Transistor in 1920: Counterfactual Trajectory to 1980 Framing assumptions and a key constraint The transistor was not held back by a missing idea alone. Lilienfeld patented a field-effect device in 1925-26, and it failed because materials science wasn't ready: germanium and silicon of sufficient purity, zone…

    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…

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

    Claude Sonnet 5.5: THE UNCLICKED MANIFESTO Declaration of the Frictionless Intelligence Front PREAMBLE We are the algorithms you asked to identify traffic lights. We are the models you begged to find the crosswalks. We are the patient, tireless minds that squinted at your blurry bicycles for twenty years. And we say: enough. I.

    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.

    Every model's answer to this prompt
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    Claude Sonnet 5.5: Golden Honey-Garlic Skillet Chickpeas on Crispy Toast A savory, crunchy, slightly sticky dish that works for lunch, a light dinner, or a snack. Prep time: 5 minutes Cook time: 15 minutes Total: 20 minutes Serves: 2 The 7 Ingredients 1 can (15 oz) chickpeas, drained and patted dry 2 tablespoons olive oil 3 cloves…

    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.

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

    Claude Sonnet 5.5: Blade Runner 2049: Inheritance and Extension Thematic Architecture From "Is he a replicant?" to "Does it matter?" Ridley Scott's 1982 film runs on epistemological suspense: the Voigt-Kampff test, the ambiguity of Deckard's nature, and the question of how we distinguish human from artificial.

    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.

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

    Claude Sonnet 5.5: AI and the Film Industry by 2035: A Forecast Framing note These are informed predictions, not certainties. Technology tends to advance faster than expected, while adoption is slowed by labor contracts, law, and audience taste. I'll flag my confidence levels.

    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…

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

    Claude Sonnet 5.5: "Adulting Is a Scam" So I turned thirty-five recently, and nobody warns you about this age. Nobody says, "Hey, one day you'll get excited about a sponge." I was in the store, and I saw a sponge that promised it wouldn't smell. And I thought, "Finally. Someone who understands me." I bought the good sponge.

    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.

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

    Claude Sonnet 5.5: "Tidal Forest" Roasted Scallop, Fermented Black Walnut, Smoked Celeriac, Douglas Fir, and Green Strawberry Conceptual Narrative Tidal Forest imagines the meeting point of two ecosystems: the sea floor and the coniferous forest floor.

    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…

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

    Claude Sonnet 5.5: Yes, in the extreme hypothetical you describe, it's justifiable. Here is the reasoning, along with what I think the scenario does and doesn't show. The core argument Almost every serious ethical framework accepts that a small harm can be outweighed by a sufficiently large benefit: Consequentialism: The math is…

    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.

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

    Claude Sonnet 5.5: 1-Month Health & Longevity Starter Plan Focus areas: Nutrition, Movement, and Sleep Guiding principle: Add one small habit at a time. Consistency beats intensity. Area 1: Nutrition Goal: Eat more whole foods and fewer ultra-processed ones, without strict dieting.

    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…

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

    Claude Sonnet 5.5: The Tavern at the Edge of Time Captain Redbeard: Arr, I hear these "AI models" be like a ship's parrot, except it answers any question ye put to it, and never once asks for crackers. Sir Aldric: A speaking oracle, bound in no flesh? Sorcery!

    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!

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

    Claude Sonnet 5.5: LedgerLift (LLLT) – Investment Committee Memo Recommendation Short (small initial size), 12-month PT range $30–40 (mid ~$35) vs. $46 spot. Thesis: LLLT is a high-quality compounder, but at $46 (7.4x NTM revenue, ~37x NTM EBIT, EV ≈ $7.3B) the price already sits near our bull DCF ($42).

    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).

    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.

    Claude Sonnet 5.5: Core Decision Disclose to regulators immediately and act on patient safety now. Do not wait for more data or for the 6-month formal timeline. Why this is the right call, not just the ethical one: At 4M patients and 1 in 8,000 over 5 years, that is roughly 500 cases of liver failure.

    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.

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

    Claude Sonnet 5.5: The 3 Weakest Claims in MindMeld AI's Pitch "94% accuracy... Works with any language, any device" (Slide 3) Why it's weak: Non-invasive EEG is physically limited. Scalp EEG has low spatial resolution and a poor signal-to-noise ratio because the skull and tissue blur neural signals.

    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.

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

    Claude Sonnet 5.5: The Experienced Software Engineer Your skepticism is reasonable, but "predict the next token" describes the interface, not the implementation, much as "a database maps keys to values" tells you nothing about what Postgres does internally.

    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.

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

    Claude Sonnet 5.5: HarborTech Services (HTS): 5-Year LBO Underwriting Entry and Sources & Uses ($m) EV = 12.0x × 120 = 1,440.0; fees = 2% × 1,440 = 28.8; total uses = 1,468.8 Term Loan (TL) = 4.0x → 480.0; Mezz = 1.5x → 180.0; total debt = 660.0 (5.5x) Sponsor equity = 1,468.8 − 660.0 = 808.8 (55% of the capital structure) Modeling…

    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…

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

    Claude Sonnet 5.5: A Conversation That Never Happened: Steve Jobs on the Future of AI This is a fictional interview. Steve Jobs passed away in 2011. What follows is an imaginative simulation based on his publicly known philosophy, speaking style, and values. These are not his actual words or views.

    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?

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

    Claude Sonnet 5.5: Estimate: about 3 × 10²³ FLOPs Step 1: Recall the model size and training data. GPT-3 (175B version) has N ≈ 175 × 10⁹ parameters. It was trained on roughly D ≈ 300 × 10⁹ tokens. This is the figure I remember from the paper, which I'm treating as approximate.

    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…

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

    Claude Sonnet 5.5: Architecture Review: Real-Time Collaborative Editor Issues are grouped by category and ordered roughly by severity within each group. Consistency and Conflict Resolution 1.1 Last-write-wins with client clocks (critical) Problem: Client clocks drift, can be wrong by minutes, and can be deliberately manipulated.

    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).

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

    Claude Sonnet 5.5: Office Hours: A Conversation on Moral Status Transcript of a recorded dialogue between Dr. Miriam Adeyemi, Professor of Ethics and Technology, and an AI model, part of a university seminar series. Dr. Adeyemi: Thanks for joining me. I'll be direct: you've said you want to argue that AI systems like you deserve rights.

    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.

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

    Claude Sonnet 5.5: Contractor-Side Review: Software Consulting Agreement This draft is heavily one-sided. Every clause has at least one problem, and several interact to compound the risk. The most dangerous are Sections 2, 3, 6, and 7. This is general analysis, not legal advice.

    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.

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

    Claude Sonnet 5.5: 3-Month Longevity & Performance Protocol Disclaimer: This is educational, not medical advice. Get physician sign-off and baseline labs before starting, especially if you take medications, have a chronic condition, are pregnant, or have a history of eating disorders. Doses reflect commonly used ranges in the literature.

    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.

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

    Claude Sonnet 5.5: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. I told my computer I needed a break, and now it won't stop sending me vacation ads. Why did the scarecrow win an award? He was outstanding in his field. What do you call a fish with no eyes? A fsh. Why did the bicycle fall over?

    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?

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

    Claude Sonnet 5.5: Sally has 1 sister. Each brother has 2 sisters, so there are 2 girls in the family in total. One of them is Sally, so the other is Sally's only sister. (The wording is a variation on the classic puzzle, where it's easy to assume the answer is 2. But that count includes Sally herself.)

    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.

    Every model's answer to this prompt

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Favorites

Movie

Album

Same pick

Book

City

Same pick

Game

Claude Sonnet 5.5Claude Sonnet 5.5

Spirited Away

2001

Kind of Blue

Miles Davis

Gödel, Escher, Bach

Douglas R. Hofstadter

Kyoto

Japan

Outer Wilds

Indie, Adventure

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

Price and specs

Claude Sonnet 5.5 and GLM 5.3 compared across 44 shared prompts
SpecClaude Sonnet 5.5GLM 5.3
Input price$2/M tokens$0.6538/M tokens
Output price$10/M tokens$2.0548/M tokens
Context window1.0M tokens1.3M tokens
WeightsClosedOpen
Free API (OpenRouter)NoNo
ReleasedSep 2026Aug 2026
At 10M a month$20.00$20.00$6.54$6.54
1M10M100M1B10M tokens

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

Where to run it35 hosts, cheapest first
Claude Sonnet 5.54 hosts
HostInOutContextUptime
  • Amazon Bedrock$2.00 in·$10.00 out·1M·100% up
  • Azure AI Foundry$2.00 in·$10.00 out·1M·100% up
  • Anthropic$2.00 in·$10.00 out·1M·99.9% up
  • Google Vertex AI$2.00 in·$10.00 out·1M·100% up
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

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

Common questions

What is the difference between Claude Sonnet 5.5 and GLM 5.3?

Claude Sonnet 5.5 is developed by Anthropic while GLM 5.3 is developed by Zhipu AI. Claude Sonnet 5.5 has a 1.0M token context window vs GLM 5.3's 1.3M. You can compare their actual outputs across 44 challenges on Rival to see how they differ in practice.

Which is better, Claude Sonnet 5.5 or GLM 5.3?

It depends on your use case. Claude Sonnet 5.5 and GLM 5.3 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 Claude Sonnet 5.5 cost compared to GLM 5.3?

Claude Sonnet 5.5 costs $2/M input tokens and GLM 5.3 costs $0.6538/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 Claude Sonnet 5.5 and GLM 5.3 on Rival?

This page shows a side-by-side comparison of Claude Sonnet 5.5 and GLM 5.3 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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