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  5. GLM 5.3 FlashXvsQwen3.5 397B A17B
Updated Sep 2026

GLM 5.3 FlashXvsQwen3.5 397B A17B

GLM 5.3 FlashX is cheaper than Qwen3.5 397B A17B at $0.37/M vs $0.6/M input tokens.

GLM 5.3 FlashX and Qwen3.5 397B A17B compared across 12 shared prompts
SpecGLM 5.3 FlashXQwen3.5 397B A17B
Input price$0.37/M tokens$0.6/M tokens
Output price$1.25/M tokens$3.6/M tokens
Context window1.0M tokens262K tokens
Weights—Open
Free API (OpenRouter)NoNo
ReleasedSep 2026Feb 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 12 prompts both answered. Formatting stripped, words untouched.

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 FlashX1,174 words
48-Hour Action Plan: Patient Safety First, Managed Transparency Second Core Decision (Stated Up Front) We report to regulators immediately—voluntarily—and we disclose on the earnings call. The math is brutal and clear: ~500 patients could develop liver failure over 5 years at current prescribing rates (4M ÷ 8,000).
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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

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 FlashX1,492 words
Counterfactual: The Transistor in 1920 This is a fun one to work through carefully. Let me establish the premise, then trace second- and third-order effects. The Premise Assume point-contact or early junction transistor physics is discovered ~1920 (say, by accident in vacuum tube/semiconductor rectifier research—the…
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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.
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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 FlashX347 words
1-Month Health & Longevity Starter Plan Week 1: Build the Foundation 🥗 Diet Add one vegetable or fruit to every meal (don't restrict anything yet) Drink a glass of water when you wake up each morning 🏃 Exercise Take a 10–15 minute walk daily (same time each day builds the habit) Do 2 sets of 5 squats and 10 wall…
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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.
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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 FlashX1,267 words
The Skeptical Software Engineer Yes, at inference time an LLM really is just "predict the next token, repeat" — a deterministic function (plus sampling noise) that maps a sequence of tokens to a probability distribution.
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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.
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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 FlashX172 words
Golden Honey Garlic Chickpea Crunch A crispy, sweet-savory skillet dish that works as a snack, side, or topping for rice. Ingredients (7): 1 can chickpeas (drained, patted dry) 3 tbsp honey 3 tbsp soy sauce 3 cloves garlic (minced) 2 tbsp olive oil ½ tsp red pepper flakes 2 tbsp sesame seeds (or crushed peanuts) Prep…
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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.
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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 5.3 FlashX695 words
Critique of MindMeld AI Pitch: 3 Weakest Claims "94% accuracy" for thought-to-text decoding (Slide 3) Why it's weak: This claim contradicts the current scientific state of the art. EEG-based decoding (a non-invasive signal measured through the skull) produces extremely noisy data.
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Qwen3.5 397B A17B655 words
Here are the three weakest claims in the MindMeld AI pitch deck, along with an analysis of their weaknesses and recommendations for improvement. The Vision Claim: "Predict what you want to type before you think it."
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Our Verdict
GLM 5.3 FlashX
GLM 5.3 FlashX
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 FlashX
Input
$0.37
1.6× cheaper
Output
$1.25
2.9× cheaper
Qwen3.5 397B A17B
Input
$0.60
Output
$3.60

GLM 5.3 FlashX is cheaper on both: 1.6× input, 2.9× output.

Where to run it

11 hosts, cheapest first

GLM 5.3 FlashX1 host
HostInOutContextUptime
Z.aifp8$0.37 in·$1.25 out·1M·100% 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 FlashX is developed by Zhipu AI while Qwen3.5 397B A17B is developed by Qwen. GLM 5.3 FlashX has a 1.0M token context window vs Qwen3.5 397B A17B's 262K. You can compare their actual outputs across 12 challenges on Rival to see how they differ in practice.

It depends on your use case. GLM 5.3 FlashX and Qwen3.5 397B A17B each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 12 challenges so you can judge which fits your needs best.

GLM 5.3 FlashX costs $0.37/M input tokens and Qwen3.5 397B A17B costs $0.6/M input tokens. GLM 5.3 FlashX is $0.23/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 FlashX 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.

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Model pages

GLM 5.3 FlashX logo
GLM 5.3 FlashX12 outputs, specs and price
Qwen3.5 397B A17B logo
Qwen3.5 397B A17B53 outputs, specs and price
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