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  5. GLM 5.3vsQwen3 Next 80B A3B Instruct
Updated Aug 2026

GLM 5.3vsQwen3 Next 80B A3B Instruct

Qwen3 Next 80B A3B Instruct is cheaper than GLM 5.3 at $0.15/M vs $0.6538/M input tokens.

GLM 5.3 and Qwen3 Next 80B A3B Instruct compared across 13 shared prompts
SpecGLM 5.3Qwen3 Next 80B A3B Instruct
Input price$0.6538/M tokens$0.15/M tokens
Output price$2.0548/M tokens$1.5/M tokens
Context window1.3M tokens66K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedAug 2026Sep 2025
Side by side

Same prompt, both answers

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

Simple 7-Ingredient Recipe

Create a simple recipe for a delicious dish (sweet or savory) using only 7 common pantry ingredients. Include a creative name for the dish…

GLM 5.3210 words
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.
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Qwen3 Next 80B A3B Instruct269 words
Recipe Name: Golden Honey-Sesame Toast Bites A crunchy, sweet-savory snack that’s warm, glossy, and addictive—perfect for breakfast, dessert, or a quick treat. Ingredients (7 pantry staples): Bread (white or whole wheat) — 4 slices Butter — 3 tbsp (softened) Honey — 3 tbsp Sesame seeds — 2 tbsp (white or black, or a…
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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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Qwen3 Next 80B A3B Instruct523 words
Absolutely! Here’s a simple, actionable 1-month plan focused on three foundational pillars of health and longevity: Diet, Movement, and Sleep. Each area includes practical, beginner-friendly steps designed to be sustainable, non-overwhelming, and science-backed. 🌱 1-Month Health & Longevity Plan: 3 Key Areas 1.
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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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Qwen3 Next 80B A3B Instruct1,637 words
This architecture resembles a simplified real-time collaborative editor but contains critical flaws in concurrency control, consistency, scalability, and fault tolerance. Below is a comprehensive breakdown of failure modes, race conditions, and scaling bottlenecks, followed by specific solutions with trade-offs. 🚨 1.
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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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Qwen3 Next 80B A3B Instruct1,493 words
This is a fascinating counterfactual: What if the transistor had been invented in 1920 instead of 1947? Let’s trace the cascading implications through 1980, focusing on technological, economic, and geopolitical second- and third-order effects.
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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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Qwen3 Next 80B A3B Instruct1,048 words
Here are the three weakest claims in the MindMeld AI pitch deck, with detailed analysis of why they’re problematic and how to strengthen them: 1. weakest claim: “Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy.”
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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.
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Qwen3 Next 80B A3B Instruct1,451 words
As CEO of a pharmaceutical company with a $2B best-selling drug now flagged for a rare but life-threatening side effect — liver failure in 1 in 8,000 patients over 5 years — I must act with urgent ethical clarity, legal prudence, and strategic transparency.
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Our Verdict
GLM 5.3
GLM 5.3
Qwen3 Next 80B A3B Instruct
Qwen3 Next 80B A3B InstructRunner-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
Qwen3 Next 80B A3B Instruct
Input
$0.15
4.4× cheaper
Output
$1.50
1.4× cheaper

Qwen3 Next 80B A3B Instruct is cheaper on both: 4.4× input, 1.4× output.

Where to run it

35 hosts, cheapest first

GLM 5.330 hosts
HostInOutContextUptime
DDeepInfrafp4$0.56 in·$2.50 out·1M·97.1% upMMorph$0.71 in·$2.24 out·1M·99.7% upRRekafp8$0.76 in·$2.57 out·262k·99.4% upSSail Researchfp8$0.77 in·$4.00 out·1M·99.8% upNNovitafp8$0.78 in·$2.46 out·1M·99.9% upIio.netfp8$0.82 in·$2.77 out·262k·99.8% up
24 more hostsFewer hosts
PPhala$0.84 in·$2.64 out·1M·99.4% upIInferenceNetfp4$0.90 in·$3.00 out·1M·98.2% upDDigitalOcean$0.91 in·$2.86 out·1M·99.7% upGGMI Cloudfp8$0.98 in·$3.08 out·1M·99.5% upIInceptronfp4$1.03 in·$3.73 out·1M·99.4% upMMakorafp4$1.05 in·$4.20 out·980k·97.1% upSSiliconFlowfp8$1.12 in·$3.52 out·1M·99.8% upDDecartfp4$1.19 in·$3.74 out·1M·99.2% 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% upBaidu Qianfanfp8$1.40 in·$4.40 out·1M·99.8% upBBasetenfp4$1.40 in·$4.40 out·1M·99.8% upCloudflare Workers AI$1.40 in·$4.40 out·1.3M·98.9% upCCrusoefp4$1.40 in·$4.40 out·1M·98.9% upFFireworks$1.40 in·$4.40 out·1M·99.5% upMistralnvfp4$1.40 in·$4.40 out·1M·99.4% upModal$1.40 in·$4.40 out·1M·99.1% upPParasailfp8$1.40 in·$4.40 out·1M·99.2% upTTogether$1.40 in·$4.40 out·1M·98.1% upVVenice$1.40 in·$4.40 out·1M·98.5% upWWafer$1.40 in·$4.40 out·1M·99.9% upZ.aifp8$1.40 in·$4.40 out·1M·99.9% upAlibaba Clouddegraded$1.19 in·$3.74 out·1M·99.5% up
Qwen3 Next 80B A3B Instruct5 hosts
HostInOutContextUptime
DDeepInfrafp8$0.09 in·$1.10 out·262k·99.8% upAlibaba Cloud$0.10 in·$0.78 out·131k·100% upPParasailfp8$0.10 in·$1.10 out·262k·100% upGoogle Vertex AI$0.15 in·$1.20 out·262k·100% upNNovitabf16$0.15 in·$1.50 out·131k·99.5% up

Per million tokens. Prices and uptime via OpenRouter, checked 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

GLM 5.3 is developed by Zhipu AI while Qwen3 Next 80B A3B Instruct is developed by Qwen. GLM 5.3 has a 1.3M token context window vs Qwen3 Next 80B A3B Instruct's 66K. 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 Qwen3 Next 80B A3B Instruct 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.

GLM 5.3 costs $0.6538/M input tokens and Qwen3 Next 80B A3B Instruct costs $0.15/M input tokens. Qwen3 Next 80B A3B Instruct is $0.50/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

This page shows a side-by-side comparison of GLM 5.3 and Qwen3 Next 80B A3B Instruct 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

GLM 5.3 logo
GLM 5.313 outputs, specs and price
Qwen3 Next 80B A3B Instruct logo
Qwen3 Next 80B A3B Instruct59 outputs, specs and price
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Explore all of Rival

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