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  5. GLM 5.2vsMercury 2.5
Updated Sep 2026

GLM 5.2vsMercury 2.5

Mercury 2.5 is cheaper than GLM 5.2 at $0.04/M vs $1.4/M input tokens.

GLM 5.2 and Mercury 2.5 compared across 20 shared prompts
SpecGLM 5.2Mercury 2.5
Input price$1.4/M tokens$0.04/M tokens
Output price$4.4/M tokens$0.15/M tokens
Context window1.0M tokens260K tokens
WeightsOpen—
Free API (OpenRouter)Yes (1 provider)No
ReleasedJun 2026Sep 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 20 prompts both answered. Formatting stripped, words untouched.

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.
Read the full answer
Mercury 2.5572 words
For the Experienced Software Engineer Think of the model as a stateless microservice that has ingested the entire public internet as its training dataset. During the training phase, it performs a massive distributed optimization job, adjusting internal weights to minimize a loss function based on next-token prediction.
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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…
Read the full answer
Mercury 2.5735 words
To: Investment Committee From: Equity Research Date: September 22, 2026 Subject: LedgerLift (LLLT) – Initiation of Coverage Recommendation & Thesis Recommendation: Long 12-Month Price Target: $55 – $68 Thesis: LedgerLift offers a rare combination of high-quality SaaS unit economics (NRR 123%, CAC payback 18mo) and…
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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 5.2250 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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Mercury 2.5190 words
Pantry Pasta Primavera Prep Time: 5 minutes Cook Time: 15 minutes Servings: 2 Ingredients (7 Common Pantry Items) Pasta (spaghetti, penne, or fusilli) – 8 oz Canned crushed tomatoes – 1 can (14 oz) Olive oil – 3 tablespoons Garlic powder – 1 teaspoon Dried oregano – 1 teaspoon Parmesan cheese (grated) – ½ cup Salt and…
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Advanced Longevity Plan (Biohacker)

Develop a comprehensive, cutting-edge 3-month longevity plan for a biohacker aiming to optimize longevity, physical and cognitive…

GLM 5.21,222 words
Disclaimer: This plan is for informational purposes and is intended for a highly healthy, biochemically literate individual. Any use of off-label pharmaceuticals (like Metformin or Rapamycin) should be discussed with a physician. Always consult a doctor before starting a new regimen.
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Mercury 2.5610 words
3-Month Biohacking Longevity & Performance Plan Disclaimer: I am an AI, not a physician or certified health professional. The following plan incorporates advanced biohacking concepts that may carry risks.
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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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Mercury 2.5660 words
Inventing the transistor in 1920 would have fundamentally reshaped the 20th century, compressing the digital age by roughly two decades. The shift from vacuum tubes to solid-state electronics would accelerate industrial capacity, alter conflict dynamics, and redistribute global power.
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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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Mercury 2.5403 words
1. Simplified Year-by-Year LBO Model (FY2026–FY2030) Entry Assumptions: Enterprise Value (EV): $120m × 12.0x = $1,440m Transaction Fees: $1,440m × 2.0% = $28.8m Total Debt at Close: $120m × 5.5x = $660m Term Loan: $120m × 4.0x = $480m Mezzanine: $120m × 1.5x = $180m Equity Required: $1,440m (EV) + $28.8m (Fees) − $660m…
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Our Verdict
GLM 5.2
GLM 5.2
Mercury 2.5
Mercury 2.5Runner-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.

Mercury 2.5 costs 29x less per token.

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
Mercury 2.5
Input
$0.04
35× cheaper
Output
$0.15
29× cheaper

Mercury 2.5 is cheaper on both: 35× input, 29× output.

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·99.9% upDDigitalOcean$0.70 in·$2.20 out·262k·97.8% upCCoreWeavefp4$0.76 in·$2.42 out·1M·99.8% up
16 more hostsFewer hosts
AAtlasCloudfp8$0.94 in·$2.95 out·1M·99.9% upAlibaba Cloudfp8$0.97 in·$3.04 out·1M·99.9% upIInceptronfp4$1.01 in·$3.18 out·1M·98.7% upSSiliconFlowfp8$1.19 in·$3.74 out·1M·99.8% 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% up
Mercury 2.51 host
HostInOutContextUptime
Inception$0.04 in·$0.15 out·260k·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 Mercury 2.5 is developed by Inception. GLM 5.2 has a 1.0M token context window vs Mercury 2.5's 260K. 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 Mercury 2.5 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 Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $1.36/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 Mercury 2.5 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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Rival
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Explore all of Rival

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