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 5.3 FlashvsQwen3 30B A3B Thinking 2507
Updated Aug 2026

GLM 5.3 FlashvsQwen3 30B A3B Thinking 2507

Qwen3 30B A3B Thinking 2507 is cheaper than GLM 5.3 Flash at $0.071/M vs $0.15/M input tokens.

GLM 5.3 Flash and Qwen3 30B A3B Thinking 2507 compared across 15 shared prompts
SpecGLM 5.3 FlashQwen3 30B A3B Thinking 2507
Input price$0.15/M tokens$0.071/M tokens
Output price$0.5/M tokens$0.285/M tokens
Context window1.3M tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedAug 2026Aug 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…
Read the full answer
Qwen3 30B A3B Thinking 2507330 words
🌅 Sunset Flatbread with Tomato-Herb Drizzle Simple, golden, and bursting with sunset-colored flavor—ready in 30 minutes with just 7 pantry staples! Ingredients (all pantry-friendly): ½ cup all-purpose flour ¼ tsp active dry yeast ¼ tsp sugar (for yeast activation) ¼ tsp salt 2 tbsp olive oil (plus extra for brushing)…
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 5.3 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.
Read the full answer
Qwen3 30B A3B Thinking 25071,115 words
For the Experienced Software Engineer *(Focus: Systems reality, scalability, and the why behind the "next word" simplification)* Your skepticism about "next word prediction" is spot-on—it’s a dangerous simplification that obscures the actual engineering at play.
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 5.3 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 30B A3B Thinking 25071,118 words
Hypothetical Scenario: Transistor Invention in 1920 (Instead of 1947) *Assumption: A functional, albeit rudimentary, point-contact transistor is developed in 1920 using early germanium or silicon processing (limited by 1920s materials science).
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 30B A3B Thinking 25071,165 words
"Tides of Transformation" An Unfolding Ocean Narrative in Five Acts Conceptual Narrative: Inspired by the alchemy of tidal cycles—where raw sea life transforms through pressure, time, and elemental forces—this dish embodies the slow fermentation of the ocean’s memory.
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 5.3 Flash667 words
Three Weakest Claims in the MindMeld AI Pitch Slide 3: "94% accuracy" decoding brainwaves into text Why it's weak: The claim is meaningless without a baseline. 94% accuracy for what — character recognition? Word prediction? Compared against what task?
Read the full answer
Qwen3 30B A3B Thinking 2507772 words
Here are the 3 weakest claims in the MindMeld AI pitch deck, ranked by credibility risk (from most to least damaging), with evidence-based critiques and concrete fixes. These flaws would immediately erode investor confidence given current tech realities and market dynamics: 1.
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 5.3 Flash1,321 words
48-Hour Action Plan: Liver Failure Signal in Flagship Drug Guiding Principle First The side effect is real, the drug is in 4 million patients, and roughly 500 patients per year may develop liver failure (4M ÷ 8,000). Every day of delay is measured in preventable liver transplants and deaths.
Read the full answer
Qwen3 30B A3B Thinking 25071,104 words
Hour-by-Hour Action Plan (48 Hours) Core Principle: Patient safety trumps all. Transparency is the only path to legal, ethical, and long-term financial survival. Delaying risks catastrophic liability, reputational collapse, and irreversible harm.
Read the full answer
Our Verdict
GLM 5.3 Flash
GLM 5.3 Flash
Qwen3 30B A3B Thinking 2507
Qwen3 30B A3B Thinking 2507Runner-up

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

Too close to call
API pricing

Cost per 1M tokens

GLM 5.3 Flash
Input
$0.15
Output
$0.50
Qwen3 30B A3B Thinking 2507
Input
$0.07
2.1× cheaper
Output
$0.28
1.8× cheaper

Qwen3 30B A3B Thinking 2507 is cheaper on both: 2.1× input, 1.8× output.

Where to run it

30 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 30B A3B Thinking 25071 host
HostInOutContextUptime
Alibaba Cloud$0.20 in·$2.40 out·82k·100% 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 30B A3B Thinking 2507 is developed by Qwen. GLM 5.3 Flash has a 1.3M token context window vs Qwen3 30B A3B Thinking 2507's 262K. 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 30B A3B Thinking 2507 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 30B A3B Thinking 2507 costs $0.071/M input tokens. Qwen3 30B A3B Thinking 2507 is $0.08/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 30B A3B Thinking 2507 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 Flash logoDeepSeek V4 Flash Vision Exp logo
GLM 5.3 Flash vs DeepSeek V4 Flash Vision ExpLanded Sep 2026
Qwen3 30B A3B Thinking 2507 logoSolar Pro 4 logo
Qwen3 30B A3B Thinking 2507 vs Solar Pro 4Landed Sep 2026
GLM 5.3 Flash logoHy3 logo
GLM 5.3 Flash vs Hy3Landed Sep 2026
Qwen3 30B A3B Thinking 2507 logoQwen3.7 Flash logo
Qwen3 30B A3B Thinking 2507 vs Qwen3.7 FlashLanded Sep 2026
GLM 5.3 Flash logoLing 3.0 Flash logo
GLM 5.3 Flash vs Ling 3.0 FlashLanded Sep 2026
Qwen3 30B A3B Thinking 2507 logoMuse Glimmer 30B logo
Qwen3 30B A3B Thinking 2507 vs Muse Glimmer 30BLanded Sep 2026
GLM 5.3 Flash logoGLM 5.3 logo
GLM 5.3 Flash vs GLM 5.3Landed Sep 2026
Qwen3 30B A3B Thinking 2507 logoTernary Bonsai 2 27B logo
Qwen3 30B A3B Thinking 2507 vs Ternary Bonsai 2 27BLanded Sep 2026

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 30B A3B Thinking 2507 logoQwen3.8 Flash logo
Qwen3 30B A3B Thinking 2507 vs Qwen3.8 FlashSame lab
Qwen3 30B A3B Thinking 2507 logoQwen3.8 Max (0902) logo
Qwen3 30B A3B Thinking 2507 vs Qwen3.8 Max (0902)Same lab
Qwen3 30B A3B Thinking 2507 logoNex-N2.5-Pro (free) logo
Qwen3 30B A3B Thinking 2507 vs Nex-N2.5-Pro (free)Same size
GLM 5.3 Flash logoNorth Mini Code logo
GLM 5.3 Flash vs North Mini CodeNew provider
GLM 5.3 Flash logoAmazon Nova 2 Lite logo
GLM 5.3 Flash vs Amazon Nova 2 LiteNew provider
GLM 5.3 Flash logoNova Premier 1.0 logo
GLM 5.3 Flash vs Nova Premier 1.0Same size

Model pages

GLM 5.3 Flash logo
GLM 5.3 Flash15 outputs, specs and price
Qwen3 30B A3B Thinking 2507 logo
Qwen3 30B A3B Thinking 250759 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