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  5. GLM 5.3 FlashvsKimi K2.7 Code
Updated Aug 2026

GLM 5.3 FlashvsKimi K2.7 Code

GLM 5.3 Flash is cheaper than Kimi K2.7 Code at $0.15/M vs $0.95/M input tokens.

GLM 5.3 Flash and Kimi K2.7 Code compared across 15 shared prompts
SpecGLM 5.3 FlashKimi K2.7 Code
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 2026Jun 2026
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…
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Kimi K2.7 Code218 words
Sunshine Chickpea & Rice Pilaf A cozy, one-pot pantry meal that’s earthy, lightly spiced, and satisfying. Ingredients (7 pantry staples) 1 can (15 oz) chickpeas, drained and rinsed 1 can (14.5 oz) diced tomatoes, with juices 1 cup long-grain white rice 1 small yellow onion, diced 2 cloves garlic, minced 2 tbsp olive…
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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 5.3 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 K2.7 Code1,593 words
Disclaimer: This is a strategic role-play and crisis-management framework, not legal, medical, or securities advice. In a real situation I would rely on qualified FDA/regulatory, securities, and product-liability counsel, and the company’s Chief Medical Officer and Patient Safety team.
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Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

GLM 5.3 Flash518 words
HarborTech Services — 5-Year LBO Summary Entry Math EV = 12.0x × $120m = $1,440.0m; Fees (2%) = $28.8m; Total uses = $1,468.8m Debt: TL = 4.0x × 120 = $480.0m; Mezz = 1.5x × 120 = $180.0m; Total = $660.0m (5.5x) Sponsor equity = $808.8m Base Case Projection ($m) FY26 FY27 FY28 FY29 FY30 Revenue 972.0 1,039.7 1,102.1…
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Kimi K2.7 Code495 words
All figures in $m. Assumptions: 1% Term Loan amortization = 1% of original $480m principal ($4.8m/year); “FCF after debt service” = EBITDA – cash interest – cash taxes – capex – ΔNWC – mandatory amortization; all of that FCF is used for optional Term Loan paydown.
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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).
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Kimi K2.7 Code1,291 words
Aurora over the Boreal Forest Arctic Char • Birch-Sap & Horseradish Meringue • Fermented Sea Buckthorn • Black Garlic–Parsnip Soil • Dill Ash • Rye-Malt Tuile A dish designed for a Michelin 3-star tasting menu: technically demanding, visually striking, and built around an unexpected Nordic flavor story—silky cold-water…
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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 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.
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Kimi K2.7 Code880 words
For the experienced software engineer You can think of a large language model as a distributed compression engine that has been forced to become a pattern-matching oracle. During training, it is shown trillions of tokens of text and is optimized for one task: predict the next token given all previous tokens.
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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 5.3 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.
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Kimi K2.7 Code1,566 words
Counterfactual: The Transistor Invented in 1920 Core assumption: A practical solid-state amplifying/switching device—the transistor—is demonstrated in a major industrial lab around 1920–1922, is patented by 1925, and is in modest mass production by the early 1930s.
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Our Verdict
GLM 5.3 Flash
GLM 5.3 Flash
Kimi K2.7 Code
Kimi K2.7 Code

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

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 K2.7 Code
Input
$0.95
Output
$4.00

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

Where to run it

44 hosts, cheapest first

GLM 5.3 Flash30 hosts
HostInOutContextUptime
DDeepInfrafp4$0.07 in·$0.25 out·1M·99.1% upGGMI Cloudfp8$0.07 in·$0.25 out·1M·99.1% upMMorph$0.08 in·$0.28 out·1M·99.9% upIInferenceNetfp4$0.09 in·$0.28 out·1M·97% upWWafer$0.10 in·$0.35 out·1M·99.8% upRRelace$0.11 in·$0.36 out·1M·99.6% up
24 more hostsFewer hosts
PPhalafp8$0.13 in·$0.42 out·1M·99.4% upNNovitafp8$0.13 in·$0.44 out·1M·99.4% upSStreamLakefp8$0.14 in·$0.47 out·1M·99.1% upSSail Researchfp8$0.14 in·$0.47 out·1M·98.7% upAAtlasCloudfp8$0.15 in·$0.50 out·1M·99.6% upCloudflare Workers AI$0.15 in·$0.50 out·1.3M·99.8% upCCoreWeavenvfp4$0.15 in·$0.50 out·1M·99.5% upDDigitalOcean$0.15 in·$0.50 out·1M·94.2% upFFireworks$0.15 in·$0.50 out·1M·99% upFFriendli$0.15 in·$0.50 out·1M·99.4% upIInceptronfp8$0.15 in·$0.50 out·1M·98.1% upIio.netfp8$0.15 in·$0.50 out·262k·99.2% upNNear AIfp8$0.15 in·$0.50 out·1M·98.8% upPParasailfp8$0.15 in·$0.50 out·1M·98.9% upRRekafp8$0.15 in·$0.50 out·262k·99.1% upSSiliconFlowfp8$0.15 in·$0.50 out·1M·99.7% upTTogether$0.15 in·$0.50 out·1M·99.5% upVVenice$0.15 in·$0.50 out·1M·98.7% upZ.aifp8$0.15 in·$0.50 out·1M·97% upNNextBitfp8$0.18 in·$0.60 out·1M·98.3% upModalfp8$0.45 in·$1.50 out·1M·99.5% upOOpenInferencefp4degraded$0.10 in·$0.50 out·1M·98.4% upBBasetenfp8degraded$0.15 in·$0.50 out·1M·99% upCCrusoefp4degraded$0.15 in·$0.50 out·1M·93.1% up
Kimi K2.7 Code14 hosts
HostInOutContextUptime
DDeepInfrafp4$0.68 in·$3.40 out·262k—IInceptronint4$0.71 in·$3.30 out·262k·99.8% upCCoreWeaveint4$0.71 in·$3.50 out·262k·99.9% upSStreamLake$0.71 in·$3.00 out·256k·99.8% upVVeniceint4$0.75 in·$3.50 out·256k·95.9% upMModelRunfp4$0.85 in·$3.75 out·262k·100% up
8 more hostsFewer hosts
SSiliconFlowfp8$0.86 in·$3.80 out·262k·99.9% upNNovitaint4$0.91 in·$3.84 out·262k·100% upAlibaba Cloudfp8$0.95 in·$4.00 out·262k·99.9% upBBasetenfp4$0.95 in·$4.00 out·262k·99.9% upCloudflare Workers AI$0.95 in·$4.00 out·262k·100% upFFireworks$0.95 in·$4.00 out·262k·95.6% upGGMI Cloudfp8$0.95 in·$4.00 out·262k·100% upMoonshot AIint4$0.95 in·$4.00 out·262k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

GLM 5.3 Flash is developed by Zhipu AI while Kimi K2.7 Code is developed by Moonshot AI. GLM 5.3 Flash has a 1.3M token context window vs Kimi K2.7 Code'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 K2.7 Code 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 K2.7 Code 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 K2.7 Code 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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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
Kimi K2.7 Code logoKimi K3 logo
Kimi K2.7 Code vs Kimi K3Same lab
Kimi K2.7 Code logoKimi K2.5 logo
Kimi K2.7 Code vs Kimi K2.5Same lab
GLM 5.3 Flash logoHy4 Preview logo
GLM 5.3 Flash vs Hy4 PreviewNew provider
GLM 5.3 Flash logoInkling logo
GLM 5.3 Flash vs InklingNew provider
GLM 5.3 Flash logoINTELLECT-3 logo
GLM 5.3 Flash vs INTELLECT-3Same size
GLM 5.3 Flash logoKimi K2 logo
GLM 5.3 Flash vs Kimi K2Cross-provider

Model pages

GLM 5.3 Flash logo
GLM 5.3 Flash15 outputs, specs and price
Kimi K2.7 Code logo
Kimi K2.7 Code58 outputs, specs and price
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Explore all of Rival

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