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  5. GLM 5.2vsLing 3.0 Flash Fin (free)
Updated Aug 2026

GLM 5.2vsLing 3.0 Flash Fin (free)

Ling 3.0 Flash Fin (free) is cheaper than GLM 5.2 at $0/M vs $1.4/M input tokens.

GLM 5.2 and Ling 3.0 Flash Fin (free) compared across 20 shared prompts
SpecGLM 5.2Ling 3.0 Flash Fin (free)
Input price$1.4/M tokensFree
Output price$4.4/M tokensFree
Context window1.0M tokens262K tokens
WeightsOpen—
Free API (OpenRouter)Yes (1 provider)Yes (1 provider)
ReleasedJun 2026Aug 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 20 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 5.2250 words
Dish Name: Midnight Magic Peanut Noodles Estimated Time: Prep Time: 5 minutes Cook Time: 10 minutes Total Time: 15 minutes Ingredients (7 Common Pantry Staples): Spaghetti (or any long pasta you have on hand) Creamy peanut butter Soy sauce Rice vinegar Honey Garlic powder Crushed red pepper flakes Instructions: Cook…
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Ling 3.0 Flash Fin (free)166 words
Midnight Molten Lava Cake Ingredients: All-purpose flour (4 tbsp) Granulated sugar (4 tbsp) Unsweetened cocoa powder (2 tbsp) Vegetable oil (3 tbsp) Water (2 tbsp) Vanilla extract (¼ tsp) Baking powder (¼ tsp) Instructions: Combine Dry Ingredients: In a standard microwave-safe mug, whisk together the flour, sugar…
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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 5.21,053 words
If the transistor had been invented in 1920—a full 27 years before its historical debut at Bell Labs—the 20th century would have been radically transformed. The transition from mechanical and vacuum-tube technology to solid-state electronics would have accelerated the Information Age by nearly three decades.
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Ling 3.0 Flash Fin (free)2,066 words
The 1920 Transistor: A Counterfactual Analysis Through 1980 I. Premise and Plausibility For a transistor to emerge in 1920, we must assume that either the theoretical framework of semiconductor physics was accelerated (quantum mechanics was only being formulated in the mid-1920s), or that empirical discovery—building…
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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 5.2745 words
The Experienced Software Engineer I know "predicting the next word" sounds like a glorified T9 autocomplete or a simple Markov chain, but the magic isn't in the objective—it’s in the internal state required to achieve it. Think of the model not as a chatbot, but as a massive, dynamically routing interpreter.
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Ling 3.0 Flash Fin (free)928 words
For the Experienced Software Engineer Here's the thing: the way you build a distributed system that "does the right thing" isn't by writing rules for every possible scenario — it's by designing the system so that correct behavior emerges from many simple, well-specified interactions.
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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 5.2661 words
Here are the three weakest claims in the MindMeld AI pitch deck, along with the reasoning for why they fail to hold up to investor scrutiny and concrete ways to fix them. The Accuracy Claim (Slide 3) The Claim: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy.
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Ling 3.0 Flash Fin (free)961 words
The 3 Weakest Claims in the MindMeld AI Pitch 🚨 The $180B TAM Claim (Slide 4) Why it's weak: This is the most logically incoherent claim in the entire deck. The pitch states the BCI market is projected to reach $5.3B by 2030 (citing Grand View Research) — and then immediately claims a TAM of $180B.
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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 5.2624 words
HarborTech Services (HTS) — LBO Underwriting Memo Entry Summary (FY2025) Item Value Entry EBITDA $120.0m Entry EV (12.0x) $1,440.0m Transaction fees (2.0% × EV) $28.8m Term Loan (4.0x) $480.0m Mezzanine (1.5x) $180.0m Total Debt $660.0m Equity Check (EV + Fees − Debt) $808.8m Operating Model & Debt Schedule…
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Ling 3.0 Flash Fin (free)556 words
Entry & Sources & Uses FY2025 EBITDA: $120m Purchase Price (EV): 12.0x × $120m = $1,440m Transaction Fees: 2.0% × $1,440m = $28.8m Total Uses: $1,440m + $28.8m = $1,468.8m Total Leverage: 5.5x × $120m = $660m Term Loan: 4.0x × $120m = $480m (9.0% cash interest, 1.0% mandatory amortization = $4.8m/yr) Mezzanine: 1.5x ×…
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Advanced Investment Memo (IC Memo)

You are writing an internal investment committee memo for a long/short equity fund. Company (fictional): LedgerLift (ticker: LLLT) – B2B…

GLM 5.2996 words
INTERNAL INVESTMENT COMMITTEE MEMO Company: LedgerLift (LLLT) Sector: Technology / B2B SaaS (Spend Management & AP Automation) Current Price: $46.00 Market Cap: $8.74B Net Cash: $1.4B EV: $7.34B Recommendation & Thesis Recommendation: LONG (Tactical) 12-Month PT Range: $50 – $54 Thesis: LedgerLift is a category-leading…
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Ling 3.0 Flash Fin (free)771 words
INTERNAL INVESTMENT COMMITTEE MEMO To: Investment Committee From: Equity Research Date: October 24, 2025 Subject: LedgerLift (LLLT) – Short Recommendation Recommendation & Thesis Recommendation: Short 12-Month PT Range: $30 – $38 Thesis: LedgerLift’s 123% NRR and 18-month CAC payback mask an eventual growth…
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Our Verdict
GLM 5.2
GLM 5.2
Ling 3.0 Flash Fin (free)
Ling 3.0 Flash Fin (free)Runner-up

Not enough votes to call it. On the specs, GLM 5.2 has the edge: bigger model tier, bigger context window, major provider backing.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GLM 5.2
Input
$1.40
Output
$4.40
Ling 3.0 Flash Fin (free)
Input
$0.000
Output
$0.000
Where to run it

23 hosts, cheapest first

GLM 5.222 hosts
HostInOutContextUptime
Baidu Qianfanfp8$0.56 in·$1.76 out·1M·99.9% upDDeepInfrafp4$0.56 in·$1.80 out·1M·98.8% upSStreamLakefp8$0.64 in·$2.01 out·1M·99.5% upNNovitafp8$0.65 in·$2.04 out·1M·100% upCCoreWeavefp4$0.76 in·$2.42 out·1M·99.8% upAAtlasCloudfp8$0.94 in·$2.95 out·1M·99.9% up
16 more hostsFewer hosts
Alibaba Cloudfp8$0.97 in·$3.04 out·1M·99.9% upIInceptronfp4$1.01 in·$3.18 out·1M·98.8% upSSiliconFlowfp8$1.19 in·$3.74 out·1M·99.7% upPPhalafp8$1.26 in·$3.00 out·1M·99.4% upBBasetenfp8$1.40 in·$4.40 out·1M·100% upCloudflare Workers AI$1.40 in·$4.40 out·262k·100% upFFireworks$1.40 in·$4.40 out·1M·99.9% upFFriendli$1.40 in·$4.40 out·1M·99.8% upGGMI Cloudfp8$1.40 in·$4.40 out·1M·99% upPParasailfp4$1.40 in·$4.40 out·262k·99.8% upTTogether$1.40 in·$4.40 out·1M·99.4% upVVenicefp8$1.40 in·$4.40 out·1M·99.8% upWWafer$1.40 in·$4.40 out·1M·99.2% upZ.aifp8$1.40 in·$4.40 out·1M·99.7% upDDecartfp4$2.25 in·$8.00 out·1M·99.8% upDDigitalOceandegraded$0.70 in·$2.20 out·262k·97.8% up
Ling 3.0 Flash Fin (free)1 host
HostInOutContextUptime
NNovita$0 in·$0 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 5.2 is developed by Zhipu AI while Ling 3.0 Flash Fin (free) is developed by inclusionAI. GLM 5.2 has a 1.0M token context window vs Ling 3.0 Flash Fin (free)'s 262K. You can compare their actual outputs across 20 challenges on Rival to see how they differ in practice.

It depends on your use case. GLM 5.2 and Ling 3.0 Flash Fin (free) each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 20 challenges so you can judge which fits your needs best.

GLM 5.2 costs $1.4/M input tokens and Ling 3.0 Flash Fin (free) costs $0/M input tokens. Ling 3.0 Flash Fin (free) is $1.40/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 5.2 and Ling 3.0 Flash Fin (free) 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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