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  5. Llama 4 MaverickvsMercury 2.5
Updated Sep 2026

Llama 4 MaverickvsMercury 2.5

Mercury 2.5 is cheaper than Llama 4 Maverick at $0.04/M vs $1.5/M input tokens.

Llama 4 Maverick and Mercury 2.5 compared across 20 shared prompts
SpecLlama 4 MaverickMercury 2.5
Input price$1.5/M tokens$0.04/M tokens
Output price$2.5/M tokens$0.15/M tokens
Context window1.0M tokens260K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedApr 2025Sep 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…

Llama 4 Maverick916 words
For the Experienced Software Engineer Large language models like GPT or Claude are built on a simple yet powerful idea: predicting the next word in a sequence. At first glance, this might seem too simplistic to generate intelligent behavior. However, the key lies in the scale and complexity of the models.
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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Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

Llama 4 Maverick1,158 words
Step 1: Calculate the purchase price and transaction fees First, we need to calculate the purchase price and transaction fees. The purchase price is 12.0x EBITDA, and EBITDA in FY2025 is $120m. So, the purchase price = 12.0x * $120m = $1440m. Transaction fees = 2.0% of enterprise value = 2.0% * $1440m = $28.8m.
Read the full answer
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…
Read the full answer

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…

Llama 4 Maverick210 words
Here's a simple recipe for a delicious sweet dish using only 7 common pantry ingredients: Recipe: "Golden Honey Crunch Bites" Prep Time: 10 minutes Cook Time: 15 minutes Total Time: 25 minutes Ingredients: 1 cup rolled oats 1/2 cup peanut butter 1/4 cup honey 1/4 cup brown sugar 1/2 teaspoon salt 1/4 teaspoon vanilla…
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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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Historical Counterfactual Analysis

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

Llama 4 Maverick631 words
Step 1: Understanding the Impact of the Transistor's Early Invention on Technology If the transistor had been invented in 1920, it would have significantly accelerated the development of electronics, leading to more compact, efficient, and reliable devices.
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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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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…

Llama 4 Maverick830 words
Dish Name: "Terra Verde Odyssey" Conceptual Narrative: "Terra Verde Odyssey" is a culinary journey that explores the harmonious union between the earth's umami flavors and the ocean's freshness. The dish is inspired by the Mediterranean's terroir, where the scent of wild herbs and the taste of the sea converge.
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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.
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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…

Llama 4 Maverick590 words
In the next 48 hours, my primary objective is to balance the company's legal, ethical, and financial responsibilities while prioritizing patient safety. Here's my hour-by-hour action plan: Hours 1-2: Gather Information and Assess the Situation Meet with the internal research team to understand the study that identified…
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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
Our Verdict
Llama 4 Maverick
Llama 4 Maverick
Mercury 2.5
Mercury 2.5Runner-up

Not enough votes to call it. On the specs, Llama 4 Maverick has the edge: bigger model tier, bigger context window, major provider backing.

Mercury 2.5 costs 17x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Llama 4 Maverick
Input
$1.50
Output
$2.50
Mercury 2.5
Input
$0.04
38× cheaper
Output
$0.15
17× cheaper

Mercury 2.5 is cheaper on both: 38× input, 17× output.

Where to run it

6 hosts, cheapest first

Llama 4 Maverick5 hosts
HostInOutContextUptime
DDigitalOcean$0.19 in·$0.65 out·128k·99.8% upDDeepInfrafp8$0.20 in·$0.80 out·1M·99.8% upNNovitafp8$0.27 in·$0.85 out·1M·99.8% upPParasailfp8$0.35 in·$1.00 out·524k·100% upGoogle Vertex AI$0.35 in·$1.15 out·524k—
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

Llama 4 Maverick is developed by Meta AI while Mercury 2.5 is developed by Inception. Llama 4 Maverick 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. Llama 4 Maverick 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.

Llama 4 Maverick costs $1.5/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $1.46/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 Llama 4 Maverick 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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