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  5. GLM 5.3 FlashvsMiMo-V2-Omni
Updated Aug 2026

GLM 5.3 FlashvsMiMo-V2-Omni

GLM 5.3 Flash is cheaper than MiMo-V2-Omni at $0.15/M vs $0.4/M input tokens.

GLM 5.3 Flash and MiMo-V2-Omni compared across 15 shared prompts
SpecGLM 5.3 FlashMiMo-V2-Omni
Input price$0.15/M tokens$0.4/M tokens
Output price$0.5/M tokens$2/M tokens
Context window1.3M tokens262K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedAug 2026Mar 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 15 prompts both answered. Formatting stripped, words untouched.

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…
Read the full answer
MiMo-V2-Omni559 words
1) Year-by-Year Model (FY2026–2030) Line Item FY2026 FY2027 FY2028 FY2029 FY2030 Revenue $972.0 $1,040.0 $1,102.5 $1,157.6 $1,215.5 EBITDA $136.1 $156.0 $176.4 $191.0 $206.6 EBITDA Margin 14.0% 15.0% 16.0% 16.5% 17.0% Cash Interest - TL $43.2 $42.8 $42.3 $41.9 $41.5 Cash Interest - Mezz $16.2 $16.2 $16.2 $16.2 $16.2…
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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.
Read the full answer
MiMo-V2-Omni1,005 words
Excellent question. This is a profound counterfactual that would rewrite the 20th century. Let's trace the implications, keeping in mind that the transistor's invention wasn't just a new device—it was the key that unlocked the information age. An earlier start accelerates everything built upon it.
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
MiMo-V2-Omni951 words
Of course. Here are three tailored explanations of how large language models learn and generate text. For the Experienced Software Engineer Think of training a large language model not as teaching it rules, but as building an incredibly sophisticated, lossy compression algorithm for the entire public internet and a…
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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).
Read the full answer
MiMo-V2-Omni815 words
"Moonlit Tidal Pool" A Meditation on the Sea at Night Conceptual Narrative This dish explores the liminal space where the ocean meets the shore under moonlight—capturing the briny depth of the sea, the mineral whisper of tidal rocks, and the ephemeral glow of bioluminescence.
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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 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.
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MiMo-V2-Omni558 words
🌱 Your 1-Month Health & Longevity Jumpstart Plan This plan focuses on sustainable, small changes in three core areas. The goal isn’t perfection—it’s building foundational habits that last. Week 1-2: Foundation & Awareness Focus on observation and gentle implementation. 1.
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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
MiMo-V2-Omni1,099 words
Hour-by-Hour Action Plan for Next 48 Hours Guiding Principle: Patient safety is non-negotiable. The ethical and legal obligation to protect patients overrides short-term financial concerns. Delaying disclosure increases liability and betrays public trust.
Read the full answer
Our Verdict
GLM 5.3 Flash
GLM 5.3 Flash
MiMo-V2-Omni
MiMo-V2-OmniRunner-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 4.0x less per token.

Too close to call
API pricing

Cost per 1M tokens

GLM 5.3 Flash
Input
$0.15
2.7× cheaper
Output
$0.50
4.0× cheaper
MiMo-V2-Omni
Input
$0.40
Output
$2.00

GLM 5.3 Flash is cheaper on both: 2.7× input, 4.0× output.

Where to run it

30 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·99.2% upMMorph$0.08 in·$0.28 out·1M·100% upIInferenceNetfp4$0.09 in·$0.28 out·1M·97.5% upWWafer$0.10 in·$0.35 out·1M·99.7% upOOpenInferencefp4$0.10 in·$0.50 out·1M·98.6% up
24 more hostsFewer hosts
RRelace$0.11 in·$0.36 out·1M·99.7% upPPhalafp8$0.13 in·$0.42 out·1M·99.5% upNNovitafp8$0.13 in·$0.44 out·1M·99.4% upSStreamLakefp8$0.14 in·$0.47 out·1M·99% upSSail Researchfp8$0.14 in·$0.47 out·1M·99% upAAtlasCloudfp8$0.15 in·$0.50 out·1M·99.6% upBBasetenfp8$0.15 in·$0.50 out·1M·98.7% upCCoreWeavenvfp4$0.15 in·$0.50 out·1M·99.5% upDDigitalOcean$0.15 in·$0.50 out·1M·93.7% upFFireworks$0.15 in·$0.50 out·1M·99% upFFriendli$0.15 in·$0.50 out·1M·98.7% upIInceptronfp8$0.15 in·$0.50 out·1M·98.2% upIio.netfp8$0.15 in·$0.50 out·262k·99.2% upNNear AIfp8$0.15 in·$0.50 out·1M·98.9% upPParasailfp8$0.15 in·$0.50 out·1M·98.7% upRRekafp8$0.15 in·$0.50 out·262k·98.9% 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·98.8% upZ.aifp8$0.15 in·$0.50 out·1M·96.5% upNNextBitfp8$0.18 in·$0.60 out·1M·97.9% upCloudflare Workers AI$0.30 in·$1.00 out·1.3M·99.8% upModalfp8$0.45 in·$1.50 out·1M·99.6% upCCrusoefp4degraded$0.15 in·$0.50 out·1M·93% up
MiMo-V2-Omni

No hosts listed on OpenRouter.

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 MiMo-V2-Omni is developed by Xiaomi. GLM 5.3 Flash has a 1.3M token context window vs MiMo-V2-Omni'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 MiMo-V2-Omni 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 MiMo-V2-Omni costs $0.4/M input tokens. GLM 5.3 Flash is $0.25/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 MiMo-V2-Omni 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

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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
MiMo-V2-Omni logoMiMo-V2.6-Pro logo
MiMo-V2-Omni vs MiMo-V2.6-ProSame lab
MiMo-V2-Omni logoMiMo-V2.6-Flash logo
MiMo-V2-Omni vs MiMo-V2.6-FlashSame lab
GLM 5.3 Flash logoQwen3 30B A3B Thinking 2507 logo
GLM 5.3 Flash vs Qwen3 30B A3B Thinking 2507Same size
GLM 5.3 Flash logoQwen3 Coder logo
GLM 5.3 Flash vs Qwen3 CoderNew provider
GLM 5.3 Flash logoQwen3 Coder Flash logo
GLM 5.3 Flash vs Qwen3 Coder FlashNew provider
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GLM 5.3 Flash vs Qwen3 Coder NextSame size

Model pages

GLM 5.3 Flash logo
GLM 5.3 Flash15 outputs, specs and price
MiMo-V2-Omni logo
MiMo-V2-Omni53 outputs, specs and price
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Explore all of Rival

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