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 5.3vsQwen3.8 27B
Updated Aug 2026

GLM 5.3vsQwen3.8 27B

Qwen3.8 27B is cheaper than GLM 5.3 at $0.4/M vs $0.6538/M input tokens.

GLM 5.3 and Qwen3.8 27B compared across 13 shared prompts
SpecGLM 5.3Qwen3.8 27B
Input price$0.6538/M tokens$0.4/M tokens
Output price$2.0548/M tokens$3/M tokens
Context window1.3M tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)NoYes (1 provider)
ReleasedAug 2026Aug 2026
Side by side

Same prompt, both answers

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

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
Qwen3.8 27B4,268 words
Below is a comprehensive issue register based on the architecture you described. I’m assuming: “change event” can be a small edit, possibly per keystroke or per paragraph. Documents are stored as HTML blobs. Each API server owns only its own WebSocket clients.
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
Qwen3.8 27B936 words
Here’s a simple, beginner-friendly 1-month plan focused on 3 key areas: movement, food, and sleep/stress. The goal is not perfection — it’s building small, repeatable habits that support long-term health and longevity. Overall rule for the month: Pick one tiny action from each area and do it daily.
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
Qwen3.8 27B116 words
Pantry Firecracker Black Beans A quick, savory, one-pan dish made with only 7 common pantry ingredients. Estimated time: Prep: 5 minutes Cook: 10 minutes Total: about 15 minutes Ingredients 1 can black beans, drained 2 tablespoons olive oil 1 teaspoon ground cumin 1/2 teaspoon chili powder 1/2 teaspoon garlic powder…
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
Qwen3.8 27B1,144 words
For an experienced software engineer Think of a large language model as a stateless inference service plus an enormous offline training pipeline. At inference time, the model takes a prompt, converts it into tokens, and produces a probability distribution over the next token.
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
Qwen3.8 27B4,262 words
Bottom line I would not wait 6 months, and I would not let the expected 40% stock-price drop drive a delay in protecting patients. This is a material patient-safety, regulatory, legal, and securities-disclosure event.
Read the full answer

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
Qwen3.8 27B4,291 words
A 1920 transistor would not merely make radios smaller. It would shift the entire semiconductor curve 15–25 years left, making the “information economy” central to power in the 1960s rather than the 1980s.
Read the full answer
Our Verdict
GLM 5.3
GLM 5.3
Qwen3.8 27B
Qwen3.8 27BRunner-up

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

Slight edge
API pricing

Cost per 1M tokens

GLM 5.3
Input
$0.65
Output
$2.05
1.5× cheaper
Qwen3.8 27B
Input
$0.40
1.6× cheaper
Output
$3.00

Qwen3.8 27B wins input (1.6× cheaper)·GLM 5.3 wins output (1.5× cheaper)

Where to run it

46 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
Qwen3.8 27B16 hosts
HostInOutContextUptime
DDarkbloomfp4$0.10 in·$1.80 out·262k·99% upDDekaLLM$0.10 in·$2.50 out·262k·99.7% upWWafer$0.11 in·$2.50 out·262k·99.9% upRRekafp8$0.12 in·$2.48 out·262k·99.9% upDDeepInfrabf16$0.15 in·$1.88 out·262k·97% upPPhala$0.20 in·$2.08 out·262k·98% up
10 more hostsFewer hosts
MMancerfp8$0.20 in·$2.50 out·262k·99.8% upCChutesfp8$0.24 in·$2.20 out·262k·99.4% upPParasailfp8$0.24 in·$2.20 out·262k·99.9% upAAkashMLfp8$0.25 in·$2.20 out·262k·100% upIIonstreamfp8$0.28 in·$2.55 out·262k·97.9% upCCoreWeavefp8$0.40 in·$3.00 out·262k·99.5% upNNovita$0.42 in·$3.00 out·1M·99.9% upAlibaba Cloud$0.42 in·$2.55 out·1M·100% upCloudflare Workers AI$0.45 in·$3.20 out·262k·91.7% upVVenicefp8$0.45 in·$3.20 out·262k·97.8% 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 Qwen3.8 27B is developed by Qwen. GLM 5.3 has a 1.3M token context window vs Qwen3.8 27B'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 Qwen3.8 27B 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 Qwen3.8 27B costs $0.4/M input tokens. Qwen3.8 27B is $0.25/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 Qwen3.8 27B 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
Qwen3.8 27B logoSolar Pro 4 logo
Qwen3.8 27B vs Solar Pro 4Landed Sep 2026
GLM 5.3 logoHy3 logo
GLM 5.3 vs Hy3Landed Sep 2026
Qwen3.8 27B logoQwen3.7 Flash logo
Qwen3.8 27B vs Qwen3.7 FlashLanded Sep 2026
GLM 5.3 logoLing 3.0 Flash logo
GLM 5.3 vs Ling 3.0 FlashLanded Sep 2026
Qwen3.8 27B logoMuse Glimmer 30B logo
Qwen3.8 27B vs Muse Glimmer 30BLanded Sep 2026
GLM 5.3 logoTernary Bonsai 2 27B logo
GLM 5.3 vs Ternary Bonsai 2 27BLanded Sep 2026
Qwen3.8 27B logoGLM 5.3 Flash logo
Qwen3.8 27B 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
Qwen3.8 27B logoQwen3.8 2.4T A95B logo
Qwen3.8 27B vs Qwen3.8 2.4T A95BVersion compare
Qwen3.8 27B logoQwen3.8 Flash logo
Qwen3.8 27B vs Qwen3.8 FlashSame lab
GLM 5.3 logoGolden Gate Claude logo
GLM 5.3 vs Golden Gate ClaudeSame size
Qwen3.8 27B logoGPT-2 logo
Qwen3.8 27B vs GPT-2Same size
GLM 5.3 logoGPT-3.5 Turbo logo
GLM 5.3 vs GPT-3.5 TurboNew provider
GLM 5.3 logoGPT-4 logo
GLM 5.3 vs GPT-4New provider

Model pages

GLM 5.3 logo
GLM 5.313 outputs, specs and price
Qwen3.8 27B logo
Qwen3.8 27B57 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