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  5. Mercury 2.5vsMistral Medium 3.1
Updated Sep 2026

Mercury 2.5vsMistral Medium 3.1

Mercury 2.5 is cheaper than Mistral Medium 3.1 at $0.04/M vs $0.4/M input tokens.

Mercury 2.5 and Mistral Medium 3.1 compared across 20 shared prompts
SpecMercury 2.5Mistral Medium 3.1
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 2026Aug 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 3.1415 words
1. Year-by-Year Financial Projections (FY2026–FY2030) FY 2026 2027 2028 2029 2030 Revenue $972m (+8%) $1,039m (+7%) $1,099m (+6%) $1,154m (+5%) $1,212m (+5%) EBITDA $136m (14.0%) $156m (15.0%) $176m (16.0%) $190m (16.5%) $206m (17.0%) Cash Int (Term Loan) $43.2m $42.5m $41.8m $41.1m $40.4m Cash Int (Mezz) $21.6m $22.3m…
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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 3.11,160 words
1. For the Skeptical Software Engineer (Focus: Systems design, scalability, and why "next-word prediction" isn’t as dumb as it sounds.) You’re right to be skeptical—if someone told you that a system as complex as a distributed database was just "predicting the next byte," you’d laugh them out of the room.
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…

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 3.1261 words
Dish Name: "Golden Crisp Honey-Nut Banana Bites" A warm, gooey, and crunchy treat that’s quick to make with just 7 pantry staples! Perfect for breakfast, a snack, or dessert. Ingredients (Serves 2-3) 2 ripe bananas (the spottier, the sweeter!)
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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.
Read the full answer
Mistral Medium 3.11,619 words
This is a high-stakes, multi-dimensional crisis requiring decisive action to balance ethical obligations, legal risks, financial stability, and stakeholder trust. Below is a detailed 48-hour action plan, structured to address all critical priorities while preparing for the board meeting and earnings call.
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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 3.11,021 words
Dish Title: "Ethereal Forest: Porcini-Infused Venison with Black Garlic Miso, Fermented Plum Gelée, and Smoked Bone Marrow Custard" Conceptual Narrative: This dish is a poetic homage to the transience of autumn—where the earthy depth of the forest floor meets the fleeting sweetness of overripe fruit.
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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.…

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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Mistral Medium 3.1726 words
Here are the three weakest claims in MindMeld AI’s pitch deck, along with explanations and suggested improvements: 1. Weak Claim: "Works with any language, any device." (Slide 3 – Solution) Why it’s weak: Overly broad and unverified: Claiming compatibility with "any language" is implausible without evidence.
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Our Verdict
Mercury 2.5
Mercury 2.5
Mistral Medium 3.1
Mistral Medium 3.1Runner-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 3.1
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 3.11 host
HostInOutContextUptime
Mistral$0.40 in·$2.00 out·131k·99.5% 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 3.1 is developed by Mistral AI. Mercury 2.5 has a 260K token context window vs Mistral Medium 3.1'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 3.1 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 3.1 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 3.1 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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