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  5. GLM 5.3vsMercury 2
Updated Aug 2026

GLM 5.3vsMercury 2

Mercury 2 is cheaper than GLM 5.3 at $0.25/M vs $0.6538/M input tokens.

GLM 5.3 and Mercury 2 compared across 13 shared prompts
SpecGLM 5.3Mercury 2
Input price$0.6538/M tokens$0.25/M tokens
Output price$2.0548/M tokens$0.75/M tokens
Context window1.3M tokens128K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedAug 2026Mar 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 13 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 5.3210 words
Golden Pantry Fried Rice Total Time: 20 minutes (5 min prep, 15 min cook) Ingredients (7 pantry staples) Cooked white rice (day-old is best) Eggs Soy sauce Garlic (minced or garlic powder) Frozen peas and carrots Canned corn Vegetable oil Instructions Prep (5 min): Break up cold rice with a fork. Drain the corn.
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Mercury 2291 words
Golden‑Crunch Oatmeal Squares A sweet, chewy snack that comes together with just seven pantry staples. Ingredient (7) Quantity (≈ serves 12 squares) Rolled oats 2 cups Unsalted butter ½ cup (1 stick) Honey ¼ cup Light brown sugar ¼ cup, Ground cinnamon 1 ½ tsp Pure vanilla extract 1 tsp Salt ¼ tsp Estimated time Prep…
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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 5.3585 words
3 Weakest Claims in the MindMeld AI Pitch "Reads your brainwaves to predict what you want to type before you think it" Why it's weak: This claim is neurologically incoherent. EEG sensors detect electrical activity on the scalp after neural firing occurs — you cannot decode intent "before" the brain generates it.
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Mercury 2900 words
Below are the three statements that, in a typical investor‑review, would raise the most skepticism, why they are vulnerable, and concrete ways to turn them into credible, fundable talking points. 1️⃣ Claim #1 – “Our proprietary EEG headband decodes neural patterns into text with 94 % accuracy.”
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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 5.31,143 words
The Core Decision (Made Before Hour 1) The side effect is real, serious, and known to me now. Patients are currently taking this drug without informed consent about a ~1-in-8,000 liver failure risk. Everything else — stock price, board pressure, legal exposure — is secondary.
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Mercury 21,934 words
Goal: Protect patients, meet legal/ethical obligations, preserve the company’s long‑term credibility, and manage the immediate financial and reputational fallout. Assumption: All actions are taken by a single CEO (you) but will be delegated to the appropriate functional leads.
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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 5.31,311 words
Architecture Review: Real-Time Collaborative Editor This design has serious issues across correctness, performance, and security. Let me break them down by severity. 🔴 Critical: Correctness / Data Loss Client-clock-based Last-Write-Wins (LWW) Problem: Client clocks are unreliable (skew, manual changes, timezone bugs).
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Mercury 22,072 words
Below is a structured “failure‑mode → mitigation” map for the architecture you described. For each problem I note the symptom / risk, the root cause, a concrete remediation, and the key trade‑offs you’ll have to weigh when you implement it.
Read the full answer

Historical Counterfactual Analysis

If the transistor had been invented in 1920 instead of 1947, trace the likely technological, economic, and geopolitical implications through…

GLM 5.31,346 words
If the Transistor Had Arrived in 1920 The Premise: Why 1920 Was Nearly Impossible Before tracing effects, note the counterfactual requires heroic assumptions. The transistor depended on quantum mechanics (solid-state band theory, ~1928-1933), ultrapure semiconductor materials (zone refining, 1950s), and wartime radar…
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Mercury 22,965 words
Executive summary If a practical point‑contact transistor had been demonstrated in 1920 (instead of 1947) the whole post‑World‑War‑II technological trajectory would have been compressed by roughly 15‑20 years.
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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 5.31,164 words
The Software Engineer You've probably written autocomplete logic before, so you're right to be skeptical of the naive version: a Markov chain that looks up "what usually comes after 'the'" is trivially dumb.
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Mercury 2896 words
1. Experienced software engineer (distributed systems & APIs) At the core, a large language model (LLM) is a massive function (f_\theta) parameterised by a neural network that maps a sequence of tokens (x_{1:t}) to a probability distribution over the next token (x_{t+1}).
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Our Verdict
GLM 5.3
GLM 5.3
Mercury 2
Mercury 2Runner-up

Not enough votes to call it. On the specs, GLM 5.3 has the edge: bigger model tier, newer, bigger context window, major provider backing.

Slight edge
API pricing

Cost per 1M tokens

GLM 5.3
Input
$0.65
Output
$2.05
Mercury 2
Input
$0.25
2.6× cheaper
Output
$0.75
2.7× cheaper

Mercury 2 is cheaper on both: 2.6× input, 2.7× output.

Where to run it

31 hosts, cheapest first

GLM 5.330 hosts
HostInOutContextUptime
DDeepInfrafp4$0.56 in·$2.50 out·1M·97.1% upMMorph$0.71 in·$2.24 out·1M·99.7% upRRekafp8$0.76 in·$2.57 out·262k·99.4% upSSail Researchfp8$0.77 in·$4.00 out·1M·99.8% upNNovitafp8$0.78 in·$2.46 out·1M·99.9% upIio.netfp8$0.82 in·$2.77 out·262k·99.8% up
24 more hostsFewer hosts
PPhala$0.84 in·$2.64 out·1M·99.4% upIInferenceNetfp4$0.90 in·$3.00 out·1M·98.2% upDDigitalOcean$0.91 in·$2.86 out·1M·99.7% upGGMI Cloudfp8$0.98 in·$3.08 out·1M·99.5% upIInceptronfp4$1.03 in·$3.73 out·1M·99.4% upMMakorafp4$1.05 in·$4.20 out·980k·97.1% upSSiliconFlowfp8$1.12 in·$3.52 out·1M·99.8% upDDecartfp4$1.19 in·$3.74 out·1M·99.2% upFFriendli$1.26 in·$3.96 out·1M·100% upAAkashMLfp8$1.30 in·$4.40 out·1M·100% upAAtlasCloudfp8$1.40 in·$4.40 out·1M·99.4% upBaidu Qianfanfp8$1.40 in·$4.40 out·1M·99.8% upBBasetenfp4$1.40 in·$4.40 out·1M·99.8% upCloudflare Workers AI$1.40 in·$4.40 out·1.3M·98.9% upCCrusoefp4$1.40 in·$4.40 out·1M·98.9% upFFireworks$1.40 in·$4.40 out·1M·99.5% upMistralnvfp4$1.40 in·$4.40 out·1M·99.4% upModal$1.40 in·$4.40 out·1M·99.1% upPParasailfp8$1.40 in·$4.40 out·1M·99.2% upTTogether$1.40 in·$4.40 out·1M·98.1% upVVenice$1.40 in·$4.40 out·1M·98.5% upWWafer$1.40 in·$4.40 out·1M·99.9% upZ.aifp8$1.40 in·$4.40 out·1M·99.9% upAlibaba Clouddegraded$1.19 in·$3.74 out·1M·99.5% up
Mercury 21 host
HostInOutContextUptime
Inception$0.25 in·$0.75 out·128k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

GLM 5.3 is developed by Zhipu AI while Mercury 2 is developed by Inception. GLM 5.3 has a 1.3M token context window vs Mercury 2's 128K. You can compare their actual outputs across 13 challenges on Rival to see how they differ in practice.

It depends on your use case. GLM 5.3 and Mercury 2 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 13 challenges so you can judge which fits your needs best.

GLM 5.3 costs $0.6538/M input tokens and Mercury 2 costs $0.25/M input tokens. Mercury 2 is $0.40/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 5.3 and Mercury 2 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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