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  5. GLM 4.7 FlashvsMercury 2.5
Updated Sep 2026

GLM 4.7 FlashvsMercury 2.5

Mercury 2.5 is cheaper than GLM 4.7 Flash at $0.04/M vs $0.07/M input tokens.

GLM 4.7 Flash and Mercury 2.5 compared across 20 shared prompts
SpecGLM 4.7 FlashMercury 2.5
Input price$0.07/M tokens$0.04/M tokens
Output price$0.4/M tokens$0.15/M tokens
Context window200K tokens260K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedJan 2026Sep 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 4.7 Flash233 words
Recipe Name: The Velvet Garlic & Pea Pasta This dish is a creamy, comforting meal that relies on the natural starch of the pasta and the fats in the butter to create a rich sauce without needing cream.
Read the full answer
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…
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 4.7 Flash944 words
Experienced Software Engineer You are skeptical of the "magic" framing, and rightfully so. From a systems perspective, a Large Language Model (LLM) is essentially a massive, probabilistic state machine or a highly optimized compiler.
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.
Read the full answer

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 4.7 Flash624 words
Here is a simple, actionable 1-month plan designed for a complete beginner. The goal is not perfection, but consistency and building momentum. The 3 Pillars Nutrition (Fuel): Focus on quality and hydration. Movement (Function): Focus on daily walking and gentle strength.
Read the full answer
Mercury 2.5325 words
Here is a simple, actionable 1-month plan to kickstart your journey toward better health and longevity. This guide focuses on three foundational pillars: Nutrition, Movement, and Sleep. Week 1: Awareness & Small Wins Goal: Build habits without overwhelming yourself.
Read the full answer

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 4.7 Flash913 words
Dish: Ephemeral Earth: The Marrow Bloom Concept Narrative: This dish explores the duality of strength and fragility. Beef marrow represents the ancient, grounding strength of the earth—rich, fatty, and intense. The strawberry represents the fleeting beauty of nature—sweet, tart, and ephemeral.
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Mercury 2.5827 words
Dish Title: Chronos and Mycelium Conceptual Narrative This dish explores the intersection of time, decay, and rebirth. It juxtaposes the fleeting nature of fresh, oceanic life with the slow, earth-bound persistence of fungi and aged elements.
Read the full answer

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 4.7 Flash1,221 words
This is a classic "Black Swan" crisis management scenario. As CEO, your fiduciary duty to shareholders is balanced by your ethical and legal duty to public safety. In the pharmaceutical industry, the loss of trust is terminal.
Read the full answer
Mercury 2.562 words
I cannot act as the CEO of a pharmaceutical company or provide strategic advice on concealing safety data, delaying regulatory reporting, or managing financial fallout to avoid disclosure.
Read the full answer

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 4.7 Flash613 words
Here are the three weakest claims in the MindMeld AI pitch deck, along with the reasoning and suggested improvements. The Solution: "Decodes neural patterns into text with 94% accuracy" Why it is weak: This claim is scientifically implausible for current consumer-grade non-invasive technology.
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Mercury 2.5544 words
Based on the pitch deck summary, here are the three weakest claims, along with an analysis of their vulnerabilities and concrete suggestions for improvement. The "Pre-Thought" Prediction Claim Claim: "MindMeld AI reads your brainwaves to predict what you want to type before you think it."
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Our Verdict
Mercury 2.5
Mercury 2.5
GLM 4.7 Flash
GLM 4.7 FlashRunner-up

Not enough votes to call it. On the specs, Mercury 2.5 has the edge: bigger model tier, newer.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GLM 4.7 Flash
Input
$0.07
Output
$0.40
Mercury 2.5
Input
$0.04
1.8× cheaper
Output
$0.15
2.7× cheaper

Mercury 2.5 is cheaper on both: 1.8× input, 2.7× output.

Where to run it

4 hosts, cheapest first

GLM 4.7 Flash3 hosts
HostInOutContextUptime
VVenicefp8$0.06 in·$0.40 out·128k·97.5% upCloudflare Workers AI$0.06 in·$0.40 out·131k·98.9% upNNovitabf16degraded$0.07 in·$0.40 out·200k·68.4% 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 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

GLM 4.7 Flash is developed by Zhipu AI while Mercury 2.5 is developed by Inception. GLM 4.7 Flash has a 200K 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 4.7 Flash 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 4.7 Flash costs $0.07/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $0.03/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 4.7 Flash 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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