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  5. Mercury 2.5vsMistral Medium 3
Updated Sep 2026

Mercury 2.5vsMistral Medium 3

Mercury 2.5 is cheaper than Mistral Medium 3 at $0.04/M vs $0.4/M input tokens.

Mercury 2.5 and Mistral Medium 3 compared across 20 shared prompts
SpecMercury 2.5Mistral Medium 3
Input price$0.04/M tokens$0.4/M tokens
Output price$0.15/M tokens$2/M tokens
Context window260K tokens131K tokens
Free API (OpenRouter)NoNo
ReleasedSep 2026May 2025
Side by side

Same prompt, both answers

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

Mini LBO Underwrite

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

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) − $660m…
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Mistral Medium 3418 words
1. Year-by-Year Financial Projections (FY2026–FY2030) Year Revenue ($m) EBITDA ($m) EBITDA Margin Cash Interest ($m) Cash Taxes ($m) Capex ($m) ΔNWC ($m) Free Cash Flow ($m) Term Loan Ending ($m) Mezz Ending ($m) 2026 972.0 136.1 14.0% 57.6 19.6 29.2 4.4 25.3 468.0 198.0 2027 1,039.4 155.9 15.0% 55.3 25.2 31.2 4.6 39.8…
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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…

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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Mistral Medium 3690 words
1. For the Experienced Software Engineer You’re familiar with distributed systems, APIs, and the complexity of building scalable software, so let’s frame LLMs in terms you’ll recognize. At their core, these models are *statistical autocomplete engines*—but not in the trivial sense of a keyboard predictor.
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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…

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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Mistral Medium 3175 words
Dish Name: "Golden Crispy Chickpea Delight" A savory, crunchy, and protein-packed snack or side dish made with just 7 pantry staples! Ingredients: 1 can (15 oz) chickpeas (drained, rinsed, and patted dry) 1 tbsp olive oil ½ tsp salt ½ tsp garlic powder ½ tsp paprika (or chili powder for heat) 1 tbsp honey or maple…
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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…

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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Mistral Medium 3726 words
The invention of the transistor in 1920 instead of 1947 would have profoundly accelerated technological, economic, and geopolitical developments by 1980. Below is a detailed breakdown of the likely implications: Technological Implications Earlier Miniaturization of Electronics Vacuum tubes, which dominated early…
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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…

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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Mistral Medium 3734 words
Dish Name: "Ethereal Abyss" – A Symphony of Land and Sea Conceptual Narrative: This dish is a poetic exploration of contrast—earth and ocean, smoke and clarity, warmth and chill.
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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…

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.
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Mistral Medium 3647 words
As the CEO of a pharmaceutical company facing this crisis, my primary responsibilities are to ensure patient safety, maintain regulatory compliance, protect the company’s long-term reputation, and fulfill my fiduciary duties to shareholders.
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Our Verdict
Mercury 2.5
Mercury 2.5
Mistral Medium 3
Mistral Medium 3Runner-up

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

Mercury 2.5 costs 13x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Mercury 2.5
Input
$0.04
10× cheaper
Output
$0.15
13× cheaper
Mistral Medium 3
Input
$0.40
Output
$2.00

Mercury 2.5 is cheaper on both: 10× input, 13× output.

Where to run it

2 hosts

Mercury 2.51 host
HostInOutContextUptime
Inception$0.04 in·$0.15 out·260k·100% up
Mistral Medium 31 host
HostInOutContextUptime
Mistral$0.40 in·$2.00 out·131k·99.8% up

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

Research

What we learned reading every model

FAQ

Common questions

Mercury 2.5 is developed by Inception while Mistral Medium 3 is developed by Mistral AI. Mercury 2.5 has a 260K token context window vs Mistral Medium 3's 131K. You can compare their actual outputs across 20 challenges on Rival to see how they differ in practice.

It depends on your use case. Mercury 2.5 and Mistral Medium 3 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.

Mercury 2.5 costs $0.04/M input tokens and Mistral Medium 3 costs $0.4/M input tokens. Mercury 2.5 is $0.36/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 Mercury 2.5 and Mistral Medium 3 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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