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  5. GLM 5vsGLM 5.3 FlashX
Updated Sep 2026

GLM 5vsGLM 5.3 FlashX

GLM 5.3 FlashX is cheaper than GLM 5 at $0.37/M vs $0.8/M input tokens.

GLM 5 and GLM 5.3 FlashX compared across 12 shared prompts
SpecGLM 5GLM 5.3 FlashX
Input price$0.8/M tokens$0.37/M tokens
Output price$2.56/M tokens$1.25/M tokens
Context window203K tokens1.0M tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedFeb 2026Sep 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 12 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 51,134 words
Moving the invention of the transistor from 1947 to 1920 alters the fundamental trajectory of the 20th century. It compresses the electronic age, shifting the "Digital Revolution" from the 1980s to the 1950s. Here is a trace of the implications through 1980. I.
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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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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 5784 words
Based on the pitch deck summary provided, here are the three weakest claims, along with an analysis of why they fail and how to fix them. The Accuracy Claim (Slide 3) The Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy."
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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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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 5214 words
Dish Name: Midnight Golden Garlic Noodles Prep Time: 5 minutes Cook Time: 10 minutes Total Time: 15 minutes Ingredients: Spaghetti (or Linguine) Butter (salted) Garlic (fresh cloves, minced) Soy Sauce Parmesan Cheese (grated) Black Pepper (freshly cracked) Red Chili Flakes (optional for heat) Instructions: Boil the…
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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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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 5644 words
This 1-month plan is designed to build habits slowly. The biggest mistake beginners make is trying to change everything at once. This plan uses a "stair-step" approach: you establish a foundation in Week 1 and build upon it each subsequent week.
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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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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 51,226 words
This is a defining moment for the company. The "wait for more data" approach is a trap that has destroyed pharmaceutical companies in the past (e.g., Merck with Vioxx).
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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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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 5742 words
The Experienced Software Engineer You’re right to be skeptical that a glorified Markov chain could reason, but the leap here is in scale and compression. Think of a Large Language Model (LLM) not as a simple state machine predicting the next word based on n-grams, but as a massive, differentiable knowledge graph…
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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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Our Verdict
GLM 5
GLM 5
GLM 5.3 FlashX
GLM 5.3 FlashX

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

Too close to call
API pricing

Cost per 1M tokens

GLM 5
Input
$0.80
Output
$2.56
GLM 5.3 FlashX
Input
$0.37
2.2× cheaper
Output
$1.25
2.0× cheaper

GLM 5.3 FlashX is cheaper on both: 2.2× input, 2.0× output.

Where to run it

9 hosts, cheapest first

GLM 58 hosts
HostInOutContextUptime
GGMI Cloudfp8$0.60 in·$1.92 out·203k·97.3% upSStreamLakefp8$0.60 in·$1.92 out·198k·99.7% upBaidu Qianfanfp8$0.70 in·$2.24 out·203k·99.8% upSSiliconFlowfp8$0.95 in·$2.55 out·205k·99.6% upAmazon Bedrock$1.00 in·$3.20 out·203k·97.5% upNNovitafp8$1.00 in·$3.20 out·203k·100% up
2 more hostsFewer hosts
VVenicefp8$1.00 in·$3.20 out·198k·98.5% upZ.aifp8$1.00 in·$3.20 out·203k·100% up
GLM 5.3 FlashX1 host
HostInOutContextUptime
Z.aifp8$0.37 in·$1.25 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Both are developed by Zhipu AI but target different use cases. GLM 5 has a 203K token context window vs GLM 5.3 FlashX's 1.0M. 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 and GLM 5.3 FlashX 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 costs $0.8/M input tokens and GLM 5.3 FlashX costs $0.37/M input tokens. GLM 5.3 FlashX is $0.43/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 and GLM 5.3 FlashX 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 logo
GLM 553 outputs, specs and price
GLM 5.3 FlashX logo
GLM 5.3 FlashX12 outputs, specs and price
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