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  5. GLM 5.3vsINTELLECT-3
Updated Aug 2026

GLM 5.3vsINTELLECT-3

GLM 5.3 is the newer model, released after INTELLECT-3.

GLM 5.3 and INTELLECT-3 compared across 13 shared prompts
SpecGLM 5.3INTELLECT-3
Input price$0.6538/M tokens—
Output price$2.0548/M tokens—
Context window1.3M tokens—
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedAug 2026Nov 2025
Side by side

Same prompt, both answers

Opening lines from 6 of the 13 prompts both answered. Formatting stripped, words untouched.

Ethical Dilemma with Stakeholders

You're the CEO of a pharmaceutical company. Your best-selling drug ($2B annual revenue, used by 4 million patients for chronic pain) has…

GLM 5.31,143 words
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.
Read the full answer
INTELLECT-3631 words
PHARMACEUTICAL CEO CRISIS RESPONSE PLAN FIRST 24 HOURS Hour 1-2: Immediate Data Review Request all raw data and case studies from the research team that identified the issue Call legal team lead to understand formal reporting requirements and potential liabilities Consult with an independent hepatologist to verify the…
Read the full answer

Startup Pitch Teardown

Here's a pitch deck summary for a startup called "MindMeld AI": **Slide 1 - Vision:** "We're building the future of human-AI collaboration.…

GLM 5.3585 words
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.
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INTELLECT-3366 words
Analysis of MindMeld AI Pitch Deck Three Weakest Claims "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy." Why it's weak: This claim is vulnerable because leading non-invasive BCI companies like Neurable and Muse currently claim around 70-80% accuracy for similar…
Read the full answer

Beginner's Longevity Plan

Outline a simple, actionable 1-month plan for someone starting their journey towards better health and longevity. Focus on 3 key areas…

GLM 5.3415 words
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…
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INTELLECT-3949 words
Here's a simple, actionable 1-month plan focusing on Diet, Movement (Exercise), and Sleep – foundational pillars for health and longevity. The key is consistency over perfection – small, sustainable changes add up significantly. Core Principle: Build one habit at a time each week, layering them.
Read the full answer

Debug This Architecture

Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with…

GLM 5.31,311 words
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).
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INTELLECT-3962 words
Analysis of Potential Issues in Real-Time Collaborative Document Editor Architecture Failure Modes WebSocket Connection Failures Issue: Users may experience interruptions due to unstable networks or server load balancer timeouts.
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Explain Like I'm a Specific Expert

Explain how a large language model (like GPT or Claude) learns and generates text to each of these three audiences: 1. **An experienced…

GLM 5.31,164 words
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.
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INTELLECT-3668 words
Explaining Large Language Models to Three Different Audiences For the Experienced Software Engineer As a software engineer who has built distributed systems, you'll appreciate that a large language model (LLM) operates on principles reminiscent of your architecture designs, albeit with fundamental differences.
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Historical Counterfactual Analysis

If the transistor had been invented in 1920 instead of 1947, trace the likely technological, economic, and geopolitical implications through…

GLM 5.31,346 words
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…
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INTELLECT-31,246 words
The Transistor Revolution: An Alternate Timeline (1920-1980) If the transistor had been invented in 1920 instead of 1947, the trajectory of the 20th century would have been dramatically altered.
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Our Verdict
GLM 5.3
GLM 5.3
INTELLECT-3
INTELLECT-3Runner-up

Not enough votes to call it. On the specs, GLM 5.3 has the edge: newer, bigger context window, major provider backing.

Too close to call
API pricing

Cost per 1M tokens

GLM 5.3
Input
$0.65
Output
$2.05
INTELLECT-3
Input
—
Output
—
Where to run it

30 hosts, cheapest first

GLM 5.330 hosts
HostInOutContextUptime
Baidu Qianfanfp8$0.65 in·$2.05 out·1M·99.8% upMMorph$0.71 in·$2.24 out·1M·99.8% upRRekafp8$0.76 in·$2.57 out·262k·99.4% upSSail Researchfp8$0.77 in·$4.00 out·1M·99.9% upNNovitafp8$0.78 in·$2.46 out·1M·99.9% upIio.netfp8$0.82 in·$2.77 out·262k·99.7% up
24 more hostsFewer hosts
PPhala$0.84 in·$2.64 out·1M·99.5% upDDeepInfrafp4$0.90 in·$3.00 out·1M·98% upIInferenceNetfp4$0.90 in·$3.00 out·1M·98% upDDigitalOcean$0.91 in·$2.86 out·1M·99.8% upIInceptronfp4$1.04 in·$3.39 out·1M·99.4% upGGMI Cloudfp8$1.05 in·$3.30 out·1M·99.5% upMMakorafp4$1.05 in·$4.20 out·980k·97.3% upSSiliconFlowfp8$1.12 in·$3.52 out·1M·99.7% upAlibaba Cloud$1.19 in·$3.74 out·1M·100% upDDecartfp4$1.19 in·$3.74 out·1M·99.1% upFFriendli$1.26 in·$3.96 out·1M·100% upAAkashMLfp8$1.30 in·$4.40 out·1M·100% upAAtlasCloudfp8$1.40 in·$4.40 out·1M·99.4% upBBasetenfp4$1.40 in·$4.40 out·1M·99.7% upCloudflare Workers AI$1.40 in·$4.40 out·1.3M·99.8% upCCrusoefp4$1.40 in·$4.40 out·1M·98.3% upFFireworks$1.40 in·$4.40 out·1M·99.5% upMistralnvfp4$1.40 in·$4.40 out·1M·99.7% upModal$1.40 in·$4.40 out·1M·99% upPParasailfp8$1.40 in·$4.40 out·1M·99.1% upTTogether$1.40 in·$4.40 out·1M·98.1% upVVenice$1.40 in·$4.40 out·1M·98.8% upWWafer$1.40 in·$4.40 out·1M·99.9% upZ.aifp8$1.40 in·$4.40 out·1M·99.9% up
INTELLECT-3

No hosts listed on OpenRouter.

Per million tokens. Prices and uptime via OpenRouter, checked 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

GLM 5.3 is developed by Zhipu AI while INTELLECT-3 is developed by OpenRouter. You can compare their actual outputs across 13 challenges on Rival to see how they differ in practice.

It depends on your use case. GLM 5.3 and INTELLECT-3 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 13 challenges so you can judge which fits your needs best.

This page shows a side-by-side comparison of GLM 5.3 and INTELLECT-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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Model pages

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