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  5. GLM 5.3 FlashvsGPT-4.1 Nano
Updated Aug 2026

GLM 5.3 FlashvsGPT-4.1 Nano

GPT-4.1 Nano is cheaper than GLM 5.3 Flash at $0.1/M vs $0.15/M input tokens.

GLM 5.3 Flash and GPT-4.1 Nano compared across 15 shared prompts
SpecGLM 5.3 FlashGPT-4.1 Nano
Input price$0.15/M tokens$0.1/M tokens
Output price$0.5/M tokens$0.4/M tokens
Context window1.3M tokens1.0M tokens
WeightsOpenClosed
Free API (OpenRouter)NoNo
ReleasedAug 2026Apr 2025
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…
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GPT-4.1 Nano742 words
Let's proceed step-by-step. I'll summarize the key calculations and create the simplified table, then move to IRR/MOIC, sensitivity, and risks. Initial Deal Assumptions Recap Entry EBITDA FY2025: $120m Entry EV: 12.0x EBITDA = 12.0 * 120 = $1,440m Transaction fees: 2.0% * $1,440m = $28.8m (paid from equity) Leverage at…
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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 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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GPT-4.1 Nano199 words
Dish Name: Savory Sweet Potato & Chickpea Hash Ingredients: Sweet potatoes (2 medium) Canned chickpeas (1 can, drained and rinsed) Olive oil (2 tablespoons) Garlic powder (1 teaspoon) Smoked paprika (1 teaspoon) Salt and pepper (to taste) Honey (1 tablespoon) Estimated Prep/Cook Time: 30 minutes Instructions: Prepare…
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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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GPT-4.1 Nano868 words
This is a fascinating hypothetical scenario. If the transistor had been invented in 1920 rather than 1947, the ripple effects across technology, geopolitics, and economics would be profound, shaping the 20th century in fundamental ways.
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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.
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GPT-4.1 Nano776 words
Let's analyze the architecture step-by-step to identify potential failure modes, race conditions, and scaling bottlenecks, along with proposed solutions and their trade-offs. 1. Potential Failure Modes a.
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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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GPT-4.1 Nano629 words
1. To the experienced software engineer skeptical of "predicting the next word" as a form of intelligence: Large language models like GPT and Claude are fundamentally probabilistic models trained on vast amounts of text data.
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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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GPT-4.1 Nano685 words
Dish Name: Celestial Harmony – A Deconstructed Black Garlic & Blueberry Soufflé with Yuzu Foam and Edible Silver Dust Conceptual Narrative: This dish embodies the cosmic dance of contrasts—earth and sky, sweetness and umami, tradition and innovation.
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Our Verdict
GLM 5.3 Flash
GLM 5.3 Flash
GPT-4.1 Nano
GPT-4.1 NanoRunner-up

Not enough votes to call it. On the specs, GLM 5.3 Flash has the edge: bigger model tier, newer.

Too close to call
API pricing

Cost per 1M tokens

GLM 5.3 Flash
Input
$0.15
Output
$0.50
GPT-4.1 Nano
Input
$0.10
1.5× cheaper
Output
$0.40
1.3× cheaper

GPT-4.1 Nano is cheaper on both: 1.5× input, 1.3× output.

Where to run it

32 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·97.2% upWWafer$0.10 in·$0.35 out·1M·99.8% upOOpenInferencefp4$0.10 in·$0.50 out·1M·98.1% up
24 more hostsFewer hosts
RRelace$0.11 in·$0.36 out·1M·99.7% upPPhalafp8$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% upSSail Researchfp8$0.14 in·$0.47 out·1M·98.9% upAAtlasCloudfp8$0.15 in·$0.50 out·1M·99.6% upBBasetenfp8$0.15 in·$0.50 out·1M·99.1% 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·94.1% upFFireworks$0.15 in·$0.50 out·1M·99.1% 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·99.1% upNNear AIfp8$0.15 in·$0.50 out·1M·98.9% upPParasailfp8$0.15 in·$0.50 out·1M·98.8% 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·98.7% upZ.aifp8$0.15 in·$0.50 out·1M·96.6% upNNextBitfp8$0.18 in·$0.60 out·1M·98% upModalfp8$0.45 in·$1.50 out·1M·99.6% upCCrusoefp4degraded$0.15 in·$0.50 out·1M·93.1% up
GPT-4.1 Nano2 hosts
HostInOutContextUptime
Azure AI Foundry$0.10 in·$0.40 out·1M·99.7% upOpenAI$0.10 in·$0.40 out·1M·99.9% 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 GPT-4.1 Nano is developed by OpenAI. GLM 5.3 Flash has a 1.3M token context window vs GPT-4.1 Nano's 1.0M. 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 GPT-4.1 Nano 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 GPT-4.1 Nano costs $0.1/M input tokens. GPT-4.1 Nano is $0.05/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 GPT-4.1 Nano 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 5.3 Flash logo
GLM 5.3 Flash15 outputs, specs and price
GPT-4.1 Nano logo
GPT-4.1 Nano55 outputs, specs and price
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