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  5. GLM 5.3 FlashvsKimi K2.6
Updated Aug 2026

GLM 5.3 FlashvsKimi K2.6

GLM 5.3 Flash is cheaper than Kimi K2.6 at $0.15/M vs $0.95/M input tokens.

GLM 5.3 Flash and Kimi K2.6 compared across 15 shared prompts
SpecGLM 5.3 FlashKimi K2.6
Input price$0.15/M tokens$0.95/M tokens
Output price$0.5/M tokens$4/M tokens
Context window1.3M tokens262K tokens
WeightsOpenOpen
Free API (OpenRouter)NoNo
ReleasedAug 2026Apr 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 15 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 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
Kimi K2.62,483 words
My north star: We disclose—fast, fully, and on our terms. The “wait for more data” option is a mirage. It exposes the company to criminal liability under FDA adverse-event reporting rules, potential DOJ investigation, securities fraud charges, and derivative litigation that could destroy the firm.
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 5.3 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
Kimi K2.6286 words
The Blanket Fort Stew (Rustic One-Pot Pasta e Ceci) A warm, garlicky hug of a meal built from true shelf-stable staples. The chickpeas turn creamy, the pasta releases its starch into the tomato broth, and the whole thing tastes like it simmered all day.
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
Kimi K2.6877 words
1. Experienced Software Engineer Think of an LLM not as a program you write, but as a binary you compile from the entire internet. Instead of hand-crafting if/then rules, you define a single objective: given the context "The cat sat on the...", output the probability distribution over every possible next token, and…
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 5.3 Flash327 words
1-Month Beginner Health & Longevity Plan 🥗 Area 1: Diet — "Add Before You Subtract" Week 1: Add one vegetable or fruit to every meal. Don't cut anything yet—just add. Week 2: Swap one sugary drink per day for water or unsweetened tea.
Read the full answer
Kimi K2.6549 words
The Foundation Month: A 30-Day Longevity Starter Plan Philosophy: Don’t overhaul your life. Stack one small win at a time so habits stick. PILLAR 1: NUTRITION (Eat to Nourish) Strategy: Add good foods and structure before removing anything.
Read the full answer

Debug This Architecture

Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with…

GLM 5.3 Flash1,516 words
Architecture Review: Collaborative Document Editor This architecture has several critical flaws that would break the core product promise (real-time collaboration). Let me work through them by severity.
Read the full answer
Kimi K2.62,081 words
Here is a systematic breakdown of the critical flaws in this architecture, categorized by the type of risk they introduce. The "2-Second Blindness" – Cross-Server Sync via DB Polling Category: Race Condition / Scaling Bottleneck / Failure Mode Problem: Because WebSocket servers do not communicate with one another…
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
Kimi K2.62,095 words
Counterfactual: The Transistor, 1920–1980 Assume a working solid-state amplifying device—crude, germanium-based, poorly understood—is demonstrated at Bell Labs or GE in 1920, a decade before the quantum-mechanical theory that explains it.
Read the full answer
Our Verdict
GLM 5.3 Flash
GLM 5.3 Flash
Kimi K2.6
Kimi K2.6Runner-up

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

GLM 5.3 Flash costs 8.0x less per token.

Too close to call
API pricing

Cost per 1M tokens

GLM 5.3 Flash
Input
$0.15
6.3× cheaper
Output
$0.50
8.0× cheaper
Kimi K2.6
Input
$0.95
Output
$4.00

GLM 5.3 Flash is cheaper on both: 6.3× input, 8.0× output.

Where to run it

50 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
Kimi K2.621 hosts
HostInOutContextUptime
IInceptronint4$0.50 in·$2.97 out·262k·99.7% upCChutesint4$0.50 in·$2.85 out·262k·98.8% upDDigitalOcean$0.57 in·$2.40 out·262k·98.3% upSStreamLakefp8$0.60 in·$2.52 out·256k·99% upCCoreWeavefp4$0.65 in·$3.41 out·262k·99.9% upDDeepInfrafp4$0.75 in·$3.50 out·262k·97.1% up
15 more hostsFewer hosts
PParasailint4$0.75 in·$3.50 out·262k·99.6% upVVeniceint4$0.75 in·$3.50 out·256k·95.9% upSSiliconFlowfp8$0.77 in·$3.40 out·262k·99.7% upNNovita$0.80 in·$3.40 out·262k·99.8% upGGMI Cloudfp8$0.85 in·$3.60 out·262k·86.7% upBaidu Qianfanfp4$0.95 in·$4.00 out·262k·100% upBBasetenfp4$0.95 in·$4.00 out·262k·97.2% upCloudflare Workers AI$0.95 in·$4.00 out·262k·99.2% upFFireworks$0.95 in·$4.00 out·262k·0% upMoonshot AIint4$0.95 in·$4.00 out·262k·99.9% upSSail Researchint4$1.00 in·$4.00 out·262k·100% upPPhala$1.09 in·$4.60 out·262k·99.7% upDDecartfp4degraded$0.59 in·$2.47 out·262k·98.4% upCCrusoebf16degraded$0.70 in·$3.50 out·262k·98.2% upAAtlasCloudint4degraded$0.95 in·$4.00 out·262k·98% 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 Kimi K2.6 is developed by Moonshot AI. GLM 5.3 Flash has a 1.3M token context window vs Kimi K2.6'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 Kimi K2.6 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 Kimi K2.6 costs $0.95/M input tokens. GLM 5.3 Flash is $0.80/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 Kimi K2.6 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

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GLM 5.3 Flash15 outputs, specs and price
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Kimi K2.658 outputs, specs and price
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Explore all of Rival

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