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  5. GLM 5.1vsGLM 5.3 FlashX
Updated Sep 2026

GLM 5.1vsGLM 5.3 FlashX

GLM 5.3 FlashX is cheaper than GLM 5.1 at $0.37/M vs $1.4/M input tokens.

GLM 5.1 and GLM 5.3 FlashX compared across 12 shared prompts
SpecGLM 5.1GLM 5.3 FlashX
Input price$1.4/M tokens$0.37/M tokens
Output price$4.4/M tokens$1.25/M tokens
Context window203K tokens1.0M tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedApr 2026Sep 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 12 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 5.1265 words
Creative Name: Midnight Maple Peanut Butter Bars Prep time: 10 minutes Cook time: 0 minutes (Chill time: 30 minutes) Ingredients (Exactly 7): Rolled oats (1 cup) Creamy peanut butter (1/2 cup) Maple syrup (1/3 cup) Semi-sweet chocolate chips (1/4 cup) Vanilla extract (1 tsp) Ground cinnamon (1/2 tsp) Kosher salt (1/4…
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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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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 5.11,378 words
As CEO, my north star in this crisis is unequivocally patient safety. The "wait for more data" approach is a catastrophic trap—both ethically and legally. History (e.g., Vioxx, Fen-Phen) shows that delaying disclosure of a known severe adverse event transforms a medical safety issue into corporate fraud, manslaughter…
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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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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 5.1717 words
This 1-month plan is designed to build a foundation for longevity without overwhelming you. The secret to long-term success is consistency over intensity. Instead of overhauling your life overnight, you will add one small, manageable habit each week across three core pillars: Movement, Nourishment, and Sleep.
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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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Historical Counterfactual Analysis

If the transistor had been invented in 1920 instead of 1947, trace the likely technological, economic, and geopolitical implications through…

GLM 5.11,208 words
To envision the invention of the transistor in 1920, we must first resolve a historical anomaly: quantum mechanics—the theoretical foundation of semiconductors—did not yet exist. For this timeline to work, we must assume a serendipitous empirical leap.
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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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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 5.1805 words
1. The Experienced Software Engineer Think of an LLM not as a chatbot, but as a massive, distributed probabilistic state machine. During training, it ingests terabytes of text and runs a continuous optimization loop to adjust billions of floating-point weights.
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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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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 5.1877 words
Here are the three weakest claims in the MindMeld AI pitch, along with the logical reasoning for their vulnerabilities and concrete ways to strengthen them. The Technology Claim: "Predicts what you want to type before you think it" & "94% accuracy with any language" Why it’s weak: This claim defies basic neuroscience…
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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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Our Verdict
GLM 5.3 FlashX
GLM 5.3 FlashX
GLM 5.1
GLM 5.1Runner-up

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

GLM 5.3 FlashX costs 3.5x less per token.

Too close to call
API pricing

Cost per 1M tokens

GLM 5.1
Input
$1.40
Output
$4.40
GLM 5.3 FlashX
Input
$0.37
3.8× cheaper
Output
$1.25
3.5× cheaper

GLM 5.3 FlashX is cheaper on both: 3.8× input, 3.5× output.

Where to run it

15 hosts, cheapest first

GLM 5.114 hosts
HostInOutContextUptime
SStreamLakefp8$0.97 in·$3.04 out·200k·99.5% upDDeepInfrafp4$1.05 in·$3.50 out·203k·99.6% upSSiliconFlowfp8$1.19 in·$3.74 out·205k·100% upAAtlasCloudfp8$1.26 in·$3.96 out·203k·99.7% upAlibaba Cloudfp8$1.33 in·$4.18 out·203k·100% upNNovitafp8$1.38 in·$4.40 out·205k·99.8% up
8 more hostsFewer hosts
Baidu Qianfanfp8$1.40 in·$4.40 out·203k·99.9% upFFriendli$1.40 in·$4.40 out·203k·100% upGGMI Cloudfp8$1.40 in·$4.40 out·203k·99.2% upZ.aifp8$1.40 in·$4.40 out·203k·99.8% upVVenicefp8$1.40 in·$4.40 out·200k·97.7% upCChutesfp8degraded$0.98 in·$3.08 out·203k·73.5% upPPhaladegraded$1.21 in·$4.20 out·203k·74.4% upNNebiusfp8degraded$1.40 in·$4.40 out·203k·95.5% 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

GLM 5.1 is developed by Z.ai while GLM 5.3 FlashX is developed by Zhipu AI. GLM 5.1 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 5.1 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 5.1 costs $1.4/M input tokens and GLM 5.3 FlashX costs $0.37/M input tokens. GLM 5.3 FlashX is $1.03/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 5.1 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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