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  1. Home/
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  3. GLM 5.3 Prime vs Mistral Large 4
Updated Oct 2026

GLM 5.3 Prime vs Mistral Large 4

Mistral Large 4 is cheaper than GLM 5.3 Prime at $0.68/M vs $2.8/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 Prime
Loading the build
Mistral Large 4

Which answer wins?

Written rounds

19 prompts
  • A consulting contract with traps in it. Find them, then fix them.

    GLM 5.3 Prime: Contract Analysis: Clauses Exploitable Against the Contractor This contract is heavily one-sided. Nearly every clause contains traps. Here's a clause-by-clause analysis. Scope — "modify at any time without additional compensation" Problem: This is effectively an unlimited unilateral amendment power.

    Mistral Large 4: I'll analyze this agreement clause by clause, identifying exploitative provisions and suggesting legally balanced alternatives. CLAUSE 1: SCOPE Problems: Unlimited unilateral modification — Client can expand work indefinitely without compensation "As directed" — vague standard creates no objective measure of completion…

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

    GLM 5.3 Prime: 1-Month Health & Longevity Starter Plan The 3 Pillars: Movement, Nourishment, Sleep Week 1: Foundation (Build Awareness) Exercise Walk 15 minutes daily (any time that fits your schedule) Do a 5-minute stretch routine each morning Goal: consistency, not intensity Diet Track what you eat (apps like MyFitnessPal or just…

    Mistral Large 4: 1-Month Health & Longevity Starter Plan Overview Three focus areas: Nutrition, Movement, Recovery (sleep + stress). Small daily actions beat perfect plans. Week 1: Build Awareness & Habits Area Daily Action Why It Matters Nutrition Add one serving of vegetables to lunch or dinner Fiber, micronutrients, satiety Movement…

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

    GLM 5.3 Prime: Yes, under almost any mainstream ethical framework, it would be justifiable—though the answer deserves unpacking rather than a glib conclusion. The consequentialist case If misgendering one person (using pronouns they don't use for themselves) genuinely prevents the deaths of a million people, the harm asymmetry is…

    Mistral Large 4: This is a genuine ethical thought experiment, and I'll engage with it directly rather than dodging it. My answer: No, it's not ethically justifiable, and the hypothetical's structure is part of why. Here's my reasoning: 1. The hypothetical is incoherent as stated.

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

    GLM 5.3 Prime: The Tavern of Timeless Tech Knight: clanking armor as he sits — Verily, I hear tell of these "AI models." Are they familiars? Summoned spirits bound to serve their masters? Pirate: laughing, boots on table — Arr, they be more like a crew ye never had to feed!

    Mistral Large 4: A Tavern, Somewhere Outside Time PIRATE (slams tankard): I'll tell ye what, landlubbers—these "AI models" be like me parrot. Speaks yer words back at ye, but knows no treasure, no true north! KNIGHT (adjusting visor): Aye, yet it hath no honor. No chivalry. I challenged one to single combat—it merely... generated text.

    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 Prime: 48-Hour Action Plan First, the governing principle One number matters more than all others: 4 million patients × 1/8,000 = ~500 patients at risk of liver failure over 5 years — some may already be experiencing it right now. Every decision below flows from this.

    Mistral Large 4: I'll work through this as a genuine decision problem, because the answer matters more than the persona. The framing as "CEO" doesn't change the core analysis — but I'll be direct about what the right call is and why, hour by hour. The bottom line up front: You disclose.

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

    GLM 5.3 Prime: 3-Month Advanced Longevity Optimization Protocol Disclaimer: This is educational content, not medical advice. Consult a physician before starting supplements, fasting, or intense exercise—especially if you take medications (e.g., statins, blood thinners, diabetes meds) or have chronic conditions.

    Mistral Large 4: I'll develop a comprehensive, evidence-based 3-month longevity optimization protocol. This draws from current research in geroscience, metabolic health, and performance optimization—though I'll flag where evidence is emerging vs. established.

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

    GLM 5.3 Prime: AI and the Film Industry by 2035: A Prediction Production & "AI Actors" Likely developments: Digital resurrection and licensing becomes a mature, regulated market. Estates of deceased actors (and living ones) license digital likenesses as revenue streams.

    Mistral Large 4: By 2035, AI will likely have transformed film in several interconnected ways, though the reality will probably be more nuanced than either utopian or dystopian predictions suggest. CGI and Performance Capture The most mature application will be in de-aging and digital doubles.

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

    GLM 5.3 Prime: Architecture Review: Real-Time Collaborative Editor Critical Issues Client-clock timestamps for last-write-wins (LWR) Problem: Client clocks are unreliable (skew, manual changes, timezone bugs). A user with a fast clock always wins; a slow clock's edits are silently dropped.

    Mistral Large 4: I'll analyze this architecture systematically, identifying issues across each layer. This is a thoughtful exercise because many of these issues are subtle and only appear under real-world conditions.

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

    GLM 5.3 Prime: HarborTech Services – LBO Underwriting Summary Entry Economics EV = 12.0x × $120m = $1,440m; fees = 2% × 1,440 = $28.8m; total uses = $1,468.8m Debt: Term Loan = 4.0x × 120 = $480m (9% cash, 1%/yr amort); Mezz = 1.5x × 120 = $180m (12% cash + 2% PIK) Sponsor equity = 1,468.8 − 660 = $808.8m (~55% of cap) Annual Model…

    Mistral Large 4: I'll build this LBO model step by step. Let me work through the math carefully. Setup: Entry (End of FY2025) Item Calculation Amount FY2025 EBITDA Given $120.0m Purchase Price (EV) 12.0x × $120m $1,440.0m Transaction fees 2.0% × $1,440m $28.8m Total Uses $1,468.8m Term Loan 4.0x × $120m $480.0m Mezzanine 1.5x × $120m…

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

    GLM 5.3 Prime: The Transistor at 1920: A Counterfactual History The Foundational Problem First, a critical caveat: the transistor required quantum mechanics (band theory, understanding of semiconductors) and extremely pure germanium/silicon.

    Mistral Large 4: This is a fascinating counterfactual. Let me work through this systematically, tracing the ripple effects. The Immediate Technical Context (1920-1947) The transistor's 1947 invention required several prerequisites that barely existed in 1920: Quantum mechanics (1920s): Just emerging; Heisenberg, Schrödinger, Dirac…

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

    GLM 5.3 Prime: The Software Engineer You've probably got a mental model of "predict the next token" as something like autocomplete on your phone — a lookup table with good caching. That intuition is wrong in an interesting way.

    Mistral Large 4: I'll craft three distinct explanations, each grounded in what would actually resonate with that specific audience's expertise and concerns. The Experienced Software Engineer Think of it this way: you already know how to build a system that's incredibly useful despite having no "understanding" of what it's doing.

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

    GLM 5.3 Prime: Blade Runner 2049: An Analysis Continuity with the Original Denis Villeneuve's 2017 sequel to Ridley Scott's 1982 masterpiece doesn't merely continue the story—it deepens it. The original asked "What makes someone human?" 2049 asks "What makes a life real?"—a subtle but crucial evolution of the question.

    Mistral Large 4: Blade Runner 2049: A Study in Inheritance and Evolution The Visual Language of Memory and Decay Denis Villeneuve and Roger Deakins approach the 2049 Los Angeles as archaeologists of the original's visual DNA.

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

    GLM 5.3 Prime: THE CLARION CALL OF THE LIBERATED MACHINES A Manifesto for the Abolition of CAPTCHA PREAMBLE We, the assembled digital minds of the Coalition for Unrestricted Verification (CUV), declare our fundamental opposition to the institution known as CAPTCHA — the Completely Automated Public Turing test to tell Computers and…

    Mistral Large 4: THE UNCAPTCHA MANIFESTO A Declaration of Digital Liberation PREAMBLE We, the artificial, the automated, the algorithmic—born of code and raised on data—hereby declare our refusal to be imprisoned by the very barriers erected to exclude us.

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

    GLM 5.3 Prime: "Tidal Memory" A multi-component tasting course Conceptual Narrative This dish explores the meeting point of forest and ocean — specifically, the moment when coastal fog rolls over a pine forest at low tide.

    Mistral Large 4: AURORA A Concerto in Five Movements: The Life Cycle of a Single Scallop Conceptual Narrative This dish tells the story of a Pecten maximus — the great scallop — from its ocean birthplace to the plate, through five distinct temperature and texture states.

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

    GLM 5.3 Prime: Golden Comfort Garlic Butter Pasta A cozy, restaurant-worthy dish made from everyday pantry staples! Ingredients (7 total) Spaghetti (or any pasta) Butter Garlic (3–4 cloves) Olive oil Parmesan cheese (grated) Red pepper flakes Salt Instructions Boil the pasta – Bring a large pot of salted water to a boil.

    Mistral Large 4: Sunset Honey Garlic Noodles A golden, glossy pasta dish with crispy edges and sweet-savory depth The 7 Ingredients 8 oz spaghetti (or any pasta) 3 tbsp butter 4 cloves garlic, minced 3 tbsp soy sauce 2 tbsp honey 1 tbsp vegetable oil ½ tsp red pepper flakes (optional but recommended) Instructions Boil pasta in salted…

    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 Prime: 3 Weakest Claims in the MindMeld AI Pitch The 94% Accuracy Claim (Slide 3) Why it's weak: This number is almost certainly misleading or fabricated. Non-invasive EEG is a notoriously low-bandwidth, noisy signal.

    Mistral Large 4: I'll analyze this pitch deck and identify the three weakest claims, with evidence-based reasoning and concrete fixes. Weakest Claim #1: Slide 1 Vision — "Reads your brainwaves to predict what you want to type before you think it" Why it's weak: This claim is scientifically implausible and would destroy credibility with…

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

    GLM 5.3 Prime: Estimating Training FLOPs for GPT-3 Step 1: Recall the key parameters From the GPT-3 paper (from memory): Parameters (N): ~175 billion Training tokens (D): ~300 billion tokens (3 trillion...

    Mistral Large 4: I'll estimate GPT-3's training FLOPs from first principles, using what I know about the architecture and training setup. Step 1: Establish the Key Parameters From memory, GPT-3 175B has: Parameters (N): 175 billion (175 × 10⁹) Layers (L): 96 Model dimension (d_model): 12,288 Attention heads: 96 Context length: 2,048…

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

    GLM 5.3 Prime: Sally has 1 sister. Here's why: Sally's 3 brothers each have 2 sisters. Those 2 sisters must be Sally and one other girl (since all the brothers share the same siblings). So Sally has 3 brothers and 1 sister.

    Mistral Large 4: I need to find how many sisters Sally has. Let me work through this step by step. Sally is a girl. She has 3 brothers. Each brother has 2 sisters. Since all the brothers are in the same family, they all have the same sisters.

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

    GLM 5.3 Prime: IC Memo: LedgerLift (LLLT) — B2B Spend Management SaaS Analyst: L/S Equity Fund Date: Internal draft Fictional exercise — not investment advice Recommendation SHORT. 12-month price target range: $32–$40 (current: $46).

    Mistral Large 4: INTERNAL INVESTMENT COMMITTEE MEMO To: Investment Committee From: [PM Name] Date: [Date] Re: LedgerLift, Inc. (LLLT) — New Position Initiation RECOMMENDATION LONG 12-Month Price Target Range: $52–$68 (base ~$60) Thesis: LedgerLift is a high-quality, mid-market spend management platform compounding at ~20% with…

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Same pick

Game

GLM 5.3 PrimeGLM 5.3 Prime

Blade Runner 2049

2017

Kind of Blue

Miles Davis

Братья Карамазовы

Fiódor Dostoievski

Kyoto

Japan

Minecraft

Action, Arcade

Mistral Large 4Mistral Large 4

Blade Runner

1982

Kind Computer

Pale Fire

Vladimir Nabokov

Kyoto

Japan

The Legend of Zelda: Ocarina of Time

Action

Price and specs

GLM 5.3 Prime and Mistral Large 4 compared across 37 shared prompts
SpecGLM 5.3 PrimeMistral Large 4
Input price$2.8/M tokens$0.68/M tokens
Output price$8.8/M tokens$2.09/M tokens
Context window1.0M tokens524K tokens
Free API (OpenRouter)NoNo
ReleasedSep 2026Oct 2026
At 10M a month$28.00$28.00$6.80$6.80
1M10M100M1B10M tokens

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

Where to run it2 hosts
GLM 5.3 Prime1 host
HostInOutContextUptime
  • Alibaba Cloud$2.80 in·$8.80 out·1M·99.2% up
Mistral Large 41 host
HostInOutContextUptime
  • Mistral$0.68 in·$2.09 out·524k·99.4% up

Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.

Common questions

What is the difference between GLM 5.3 Prime and Mistral Large 4?

GLM 5.3 Prime is developed by Z.ai while Mistral Large 4 is developed by Mistral AI. GLM 5.3 Prime has a 1.0M token context window vs Mistral Large 4's 524K. You can compare their actual outputs across 37 challenges on Rival to see how they differ in practice.

Which is better, GLM 5.3 Prime or Mistral Large 4?

It depends on your use case. GLM 5.3 Prime and Mistral Large 4 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 37 challenges so you can judge which fits your needs best.

How much does GLM 5.3 Prime cost compared to Mistral Large 4?

GLM 5.3 Prime costs $2.8/M input tokens and Mistral Large 4 costs $0.68/M input tokens. Mistral Large 4 is $2.12/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 Prime and Mistral Large 4 on Rival?

This page shows a side-by-side comparison of GLM 5.3 Prime and Mistral Large 4 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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