Skip to content
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare Models
  • All Models
  • Image Comparison
  • Audio Comparison
  • Image Generation
  • Best AI For...
  • Arena
  • API Pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model Updates
  • Provider Status
  • AI Creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy Policy
  • Terms
  • RSS Feed
  1. Home
  2. /
  3. Compare
  4. /
  5. GLM 5.3vsQwen3.5 397B A17B
Updated Aug 2026

GLM 5.3vsQwen3.5 397B A17B

Qwen3.5 397B A17B is cheaper than GLM 5.3 at $0.6/M vs $0.6538/M input tokens.

GLM 5.3 and Qwen3.5 397B A17B compared across 13 shared prompts
SpecGLM 5.3Qwen3.5 397B A17B
Input price$0.6538/M tokens$0.6/M tokens
Output price$2.0548/M tokens$3.6/M tokens
Context window1.3M tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedAug 2026Feb 2026
Side by side

Same prompt, both answers

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

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…
Read the full answer
Qwen3.5 397B A17B1,254 words
This scenario posits a 27-year technological head start (1920 vs. 1947). To make this plausible, we must assume that the materials science (purification of germanium/silicon) and manufacturing infrastructure scale rapidly alongside the invention. Here is the traced history of the "Silicon Twenties" through 1980.
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…
Read the full answer
Qwen3.5 397B A17B641 words
Here is a simple, actionable 1-month plan designed to build sustainable habits for health and longevity. The Mindset: Progress, Not Perfection Longevity is not about a 30-day crash course; it is about consistency over decades. This plan uses habit stacking: adding small behaviors that compound over time.
Read the full answer

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
Qwen3.5 397B A17B1,322 words
This is a crisis scenario that defines a CEO's legacy. The decision matrix is clear: Patient safety and regulatory compliance must supersede short-term financial protection. Hiding this information constitutes securities fraud and potential criminal negligence (precedents include Vioxx/Merck and Purdue Pharma).
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).
Read the full answer
Qwen3.5 397B A17B1,249 words
This architecture contains several critical flaws that would prevent it from functioning as a usable real-time collaborative editor. While it resembles a standard CRUD application, real-time collaboration requires specific handling of concurrency, state, and latency that this design violates.
Read the full answer

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.
Read the full answer
Qwen3.5 397B A17B569 words
The Experienced Software Engineer Think of training an LLM not as "teaching" it, but as extreme lossy compression. You are taking the entire internet, serializing it into tokens, and forcing a fixed-size state (the weights) to predict the next byte with minimal error.
Read the full answer

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.
Read the full answer
Qwen3.5 397B A17B284 words
🌟 Golden Hour Glaze Bowls A sweet-and-savory dish that turns humble shelf-stable staples into a crispy, comforting meal. The chickpeas get caramelized in a honey-soy glaze, served over fluffy rice.
Read the full answer
Our Verdict
GLM 5.3
GLM 5.3
Qwen3.5 397B A17B
Qwen3.5 397B A17B

Not enough votes to call it. On the specs, nothing separates them.

Too close to call
API pricing

Cost per 1M tokens

GLM 5.3
Input
$0.65
Output
$2.05
1.8× cheaper
Qwen3.5 397B A17B
Input
$0.60
1.1× cheaper
Output
$3.60

Qwen3.5 397B A17B wins input (1.1× cheaper)·GLM 5.3 wins output (1.8× cheaper)

Where to run it

40 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
Qwen3.5 397B A17B10 hosts
HostInOutContextUptime
Alibaba Cloud$0.39 in·$2.34 out·262k·99.9% upDDeepInfrafp8$0.45 in·$3.00 out·262k·97% upPParasailfp8$0.50 in·$3.60 out·262k·99.2% upAAtlasCloudfp8$0.55 in·$3.50 out·262k·96% upDDigitalOcean$0.55 in·$3.50 out·131k·90.3% upPPhala$0.55 in·$3.50 out·262k·98.7% up
4 more hostsFewer hosts
GGMI Cloudfp8$0.60 in·$3.60 out·262k·83.5% upNNovita$0.60 in·$3.60 out·262k·97.4% upSStreamLake$0.60 in·$3.60 out·256k·97.4% upVVenice$0.75 in·$4.50 out·128k·73.2% 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 Qwen3.5 397B A17B is developed by Qwen. GLM 5.3 has a 1.3M token context window vs Qwen3.5 397B A17B's 262K. 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 Qwen3.5 397B A17B 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 Qwen3.5 397B A17B costs $0.6/M input tokens. Qwen3.5 397B A17B is $0.05/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 Qwen3.5 397B A17B 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.

Keep exploring

More comparisons

Against the newest arrivals

GLM 5.3 logoDeepSeek V4 Flash Vision Exp logo
GLM 5.3 vs DeepSeek V4 Flash Vision ExpLanded Sep 2026
Qwen3.5 397B A17B logoSolar Pro 4 logo
Qwen3.5 397B A17B vs Solar Pro 4Landed Sep 2026
GLM 5.3 logoHy3 logo
GLM 5.3 vs Hy3Landed Sep 2026
Qwen3.5 397B A17B logoQwen3.7 Flash logo
Qwen3.5 397B A17B vs Qwen3.7 FlashLanded Sep 2026
GLM 5.3 logoLing 3.0 Flash logo
GLM 5.3 vs Ling 3.0 FlashLanded Sep 2026
Qwen3.5 397B A17B logoMuse Glimmer 30B logo
Qwen3.5 397B A17B vs Muse Glimmer 30BLanded Sep 2026
GLM 5.3 logoTernary Bonsai 2 27B logo
GLM 5.3 vs Ternary Bonsai 2 27BLanded Sep 2026
Qwen3.5 397B A17B logoGLM 5.3 Flash logo
Qwen3.5 397B A17B vs GLM 5.3 FlashLanded Sep 2026

Same lab, same size, long tail

GLM 5.3 logoGLM 5.3 Flash logo
GLM 5.3 vs GLM 5.3 FlashSame lab
GLM 5.3 logoGLM 5.3 FlashX logo
GLM 5.3 vs GLM 5.3 FlashXSame lab
Qwen3.5 397B A17B logoQwen3.5 Plus 2026-02-15 logo
Qwen3.5 397B A17B vs Qwen3.5 Plus 2026-02-15Version compare
Qwen3.5 397B A17B logoQwen3.8 Flash logo
Qwen3.5 397B A17B vs Qwen3.8 FlashSame lab
GLM 5.3 logoGPT-5.3-Codex logo
GLM 5.3 vs GPT-5.3-CodexNew provider
GLM 5.3 logoGPT-5.4 logo
GLM 5.3 vs GPT-5.4New provider
GLM 5.3 logoGPT-5.4 Mini logo
GLM 5.3 vs GPT-5.4 MiniNew provider
GLM 5.3 logoGPT-5.4 Nano logo
GLM 5.3 vs GPT-5.4 NanoNew provider

Model pages

GLM 5.3 logo
GLM 5.313 outputs, specs and price
Qwen3.5 397B A17B logo
Qwen3.5 397B A17B53 outputs, specs and price
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare Models
  • All Models
  • Image Comparison
  • Audio Comparison
  • Image Generation
  • Best AI For...
  • Arena
  • API Pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model Updates
  • Provider Status
  • AI Creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy Policy
  • Terms
  • RSS Feed