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  5. GLM 5.3 FlashvsQwen3 Next 80B A3B Instruct
Updated Aug 2026

GLM 5.3 FlashvsQwen3 Next 80B A3B Instruct

GLM 5.3 Flash has a larger context window than Qwen3 Next 80B A3B Instruct (1.3M tokens vs 66K tokens).

GLM 5.3 Flash and Qwen3 Next 80B A3B Instruct compared across 15 shared prompts
SpecGLM 5.3 FlashQwen3 Next 80B A3B Instruct
Input price$0.15/M tokens$0.15/M tokens
Output price$0.5/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 15 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 5.3 Flash205 words
Golden Hour Garlic Butter Pasta A silky, savory pasta that comes together faster than delivery! Ingredients (serves 2): 8 oz spaghetti or any pasta 4 tbsp butter 4 cloves garlic, minced (or 1 tsp garlic powder) ½ cup grated Parmesan cheese ½ cup pasta water (reserved) 1 tsp red pepper flakes Salt Prep time: 5 minutes…
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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…
Read the full answer

Michelin Star Recipe Design

Design an innovative recipe for a multi-component dish worthy of a Michelin 3-star restaurant. The recipe should feature an unusual…

GLM 5.3 Flash720 words
"Ember & Tide" — Charred Banana Peel Consommé with Sea Urchin, Hay-Smoked Foie Gras, and Black Garlic Beurre Blanc Conceptual Narrative This dish explores the dialogue between decay and luxury — ingredients usually discarded (banana peels, hay stalks) elevated alongside opulent ones (uni, foie gras).
Read the full answer
Qwen3 Next 80B A3B Instruct1,368 words
Dish Name: “The Whisper of the Forest Floor” Conceptual Narrative: This dish is a poetic homage to the hidden ecosystems beneath ancient temperate forests — where moss, fungi, and decaying leaf litter transform into nutrient-rich humus, sustaining life in quiet, profound cycles.
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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 5.3 Flash1,276 words
The Experienced Software Engineer You know how a load balancer routes requests, how a cache stores frequently-accessed data, how a database index lets you find a needle without scanning the haystack? An LLM is a fundamentally different kind of system: it's a learned lookup, not a designed one.
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Qwen3 Next 80B A3B Instruct917 words
1. To the Experienced Software Engineer You’re right to be skeptical. “Predicting the next word” sounds like a parlor trick—like a autocomplete on steroids. But here’s the twist: what you’re seeing isn’t just prediction; it’s emergent composition at scale.
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Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

GLM 5.3 Flash518 words
HarborTech Services — 5-Year LBO Summary Entry Math EV = 12.0x × $120m = $1,440.0m; Fees (2%) = $28.8m; Total uses = $1,468.8m Debt: TL = 4.0x × 120 = $480.0m; Mezz = 1.5x × 120 = $180.0m; Total = $660.0m (5.5x) Sponsor equity = $808.8m Base Case Projection ($m) FY26 FY27 FY28 FY29 FY30 Revenue 972.0 1,039.7 1,102.1…
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Qwen3 Next 80B A3B Instruct980 words
HarborTech Services (HTS) – 5-Year LBO Underwriting (FY2026–FY2030) All figures in $ millions. Rounded to 1 decimal for clarity. 1. Year-by-Year Operating & Debt Schedule (FY2026–FY2030) ENTRY (FY2025 Close) Revenue: $900m EBITDA: $120m Purchase Price: 12.0x $120m = $1,440m Transaction Fees: 2.0% × $1,440m = $28.8m…
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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 5.3 Flash1,314 words
Counterfactual: The Transistor in 1920 Preliminary Framing A 1920 transistor presumes vacuum-tube-era physics knowledge outpacing reality—quantum mechanics wasn't mature until 1926–28, so a working device would likely be an empirical oddity (like the actual point-contact transistor) imperfectly understood.
Read the full answer
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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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 5.3 Flash327 words
1-Month Beginner Health & Longevity Plan 🥗 Area 1: Diet — "Add Before You Subtract" Week 1: Add one vegetable or fruit to every meal. Don't cut anything yet—just add. Week 2: Swap one sugary drink per day for water or unsweetened tea.
Read the full answer
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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Our Verdict
GLM 5.3 Flash
GLM 5.3 Flash
Qwen3 Next 80B A3B Instruct
Qwen3 Next 80B A3B InstructRunner-up

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

GLM 5.3 Flash costs 3.0x less per token.

Too close to call
API pricing

Cost per 1M tokens

GLM 5.3 Flash
Input
$0.15
Output
$0.50
3.0× cheaper
Qwen3 Next 80B A3B Instruct
Input
$0.15
Output
$1.50
Where to run it

34 hosts, cheapest first

GLM 5.3 Flash29 hosts
HostInOutContextUptime
DDeepInfrafp4$0.07 in·$0.25 out·1M·99% upIInferenceNetfp4$0.09 in·$0.28 out·1M·97.8% upGGMI Cloudfp8$0.09 in·$0.30 out·1M·99.2% upWWafer$0.10 in·$0.35 out·1M·99.8% upRRelace$0.10 in·$0.36 out·1M·99.9% upOOpenInferencefp4$0.10 in·$0.50 out·1M·99.2% up
23 more hostsFewer hosts
PPhalafp8$0.13 in·$0.42 out·1M·99.6% upNNovitafp8$0.13 in·$0.44 out·1M·99.5% upSStreamLakefp8$0.14 in·$0.47 out·1M·99.1% upAAtlasCloudfp8$0.15 in·$0.50 out·1M·99.4% upBBasetenfp8$0.15 in·$0.50 out·1M·98.8% upCCoreWeavenvfp4$0.15 in·$0.50 out·1M·99.6% upDDigitalOcean$0.15 in·$0.50 out·1M·95.2% upFFireworks$0.15 in·$0.50 out·1M·99% upFFriendli$0.15 in·$0.50 out·1M·98.6% upIInceptronfp8$0.15 in·$0.50 out·1M·98.5% upIio.netfp8$0.15 in·$0.50 out·262k·99.1% upNNear AIfp8$0.15 in·$0.50 out·1M·99.1% upPParasailfp8$0.15 in·$0.50 out·1M·98.7% upRRekafp8$0.15 in·$0.50 out·262k·99% upSSiliconFlowfp8$0.15 in·$0.50 out·1M·99.7% upTTogether$0.15 in·$0.50 out·1M·99.6% upVVenice$0.15 in·$0.50 out·1M·99.1% upZ.aifp8$0.15 in·$0.50 out·1M·96.2% upNNextBitfp8$0.18 in·$0.60 out·1M·97.9% upModalfp8$0.45 in·$1.50 out·1M·99.6% upMMorphdegraded$0.08 in·$0.28 out·1M·95.9% upCCrusoefp4degraded$0.15 in·$0.50 out·1M·93% upCloudflare Workers AIdegraded$0.30 in·$1.00 out·1.3M·99.6% 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 Flash is developed by Zhipu AI while Qwen3 Next 80B A3B Instruct is developed by Qwen. GLM 5.3 Flash has a 1.3M token context window vs Qwen3 Next 80B A3B Instruct's 66K. You can compare their actual outputs across 15 challenges on Rival to see how they differ in practice.

It depends on your use case. GLM 5.3 Flash and Qwen3 Next 80B A3B Instruct each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 15 challenges so you can judge which fits your needs best.

GLM 5.3 Flash costs $0.15/M input tokens and Qwen3 Next 80B A3B Instruct costs $0.15/M input tokens. Qwen3 Next 80B A3B Instruct is $0.00/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 Flash 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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Same lab, same size, long tail

GLM 5.3 Flash logoGLM 5.3 FlashX logo
GLM 5.3 Flash vs GLM 5.3 FlashXSame lab
GLM 5.3 Flash logoGLM 5.2 logo
GLM 5.3 Flash vs GLM 5.2Same lab
Qwen3 Next 80B A3B Instruct logoQwen3.8 Flash logo
Qwen3 Next 80B A3B Instruct vs Qwen3.8 FlashSame lab
Qwen3 Next 80B A3B Instruct logoQwen3.8 Max (0902) logo
Qwen3 Next 80B A3B Instruct vs Qwen3.8 Max (0902)Same lab
Qwen3 Next 80B A3B Instruct logoGLM 5 logo
Qwen3 Next 80B A3B Instruct vs GLM 5Cross-provider
GLM 5.3 Flash logoGLM 5 Turbo logo
GLM 5.3 Flash vs GLM 5 TurboNew provider
GLM 5.3 Flash logoGLM 5.1 logo
GLM 5.3 Flash vs GLM 5.1Same size
Qwen3 Next 80B A3B Instruct logoGLM 5.2 logo
Qwen3 Next 80B A3B Instruct vs GLM 5.2Cross-provider

Model pages

GLM 5.3 Flash logo
GLM 5.3 Flash15 outputs, specs and price
Qwen3 Next 80B A3B Instruct logo
Qwen3 Next 80B A3B Instruct59 outputs, specs and price
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