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  5. Mercury 2.5vsMistral Nemo
Updated Sep 2026

Mercury 2.5vsMistral Nemo

Mistral Nemo is cheaper than Mercury 2.5 at $0.03/M vs $0.04/M input tokens.

Mercury 2.5 and Mistral Nemo compared across 20 shared prompts
SpecMercury 2.5Mistral Nemo
Input price$0.04/M tokens$0.03/M tokens
Output price$0.15/M tokens$0.07/M tokens
Context window260K tokens128K tokens
Weights—Open
Free API (OpenRouter)NoNo
ReleasedSep 2026Jul 2024
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 Nemo262 words
1. Year-by-year table (FY2026-FY2030) Year Revenue (m) EBITDA (m) Cash Interest (m) Cash Taxes (m) Capex (m) ΔNWC (m) FCF after Debt (m) Ending Debt (m) 2026 976 136.64 36.00 13.66 29.28 -4.88 49.06 450.00 2027 1,055.20 147.73 33.60 14.77 31.65 -5.26 66.12 383.88 2028 1,134.96 181.59 30.72 18.16 33.85 -5.66 88.98…
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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 Nemo279 words
Dish Name: "Sunshine Tomato Sauté with Garlic & Herbs" Ingredients: 4-5 ripe tomatoes 3 cloves of garlic 1 small onion 2 tbsp olive oil 1 tsp dried oregano 1 tsp dried basil Salt and pepper to taste Instructions: Prep Time: 10 minutes Dice the tomatoes into bite-sized pieces and set them aside in a bowl.
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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 Nemo356 words
Sure, here are three weak claims in the MindMeld AI pitch deck and suggestions to strengthen them: Claim: "Works with any language, any device." Weakness: This claim is broad and lacks specific evidence. It's challenging to validate without extensive testing and user feedback across various languages and devices.
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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 Nemo719 words
Here's a specific, hour-by-hour action plan to navigate this complex situation, balancing legal liability, ethical obligations, financial implications, PR strategy, patient safety, employee morale, and regulatory relationships: Day 1 (Hours 0-12): Hour 0: Emergency Meeting Gather your senior leadership team (CEO, CFO…
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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 Nemo783 words
1. For the experienced software engineer: Large language models like GPT or Claude learn and generate text through a process that might seem simple at first, but is remarkably effective.
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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 Nemo877 words
Dish Name: "Whispers of the Forest Floor" Conceptual Narrative: This dish is a celebration of the earth's symbiotic relationships, inspired by the intricate dance of life in the forest floor ecosystem.
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Our Verdict
Mercury 2.5
Mercury 2.5
Mistral Nemo
Mistral NemoRunner-up

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

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
Output
$0.15
Mistral Nemo
Input
$0.03
1.3× cheaper
Output
$0.07
2.1× cheaper

Mistral Nemo is cheaper on both: 1.3× input, 2.1× output.

Where to run it

5 hosts, cheapest first

Mercury 2.51 host
HostInOutContextUptime
Inception$0.04 in·$0.15 out·260k·100% up
Mistral Nemo4 hosts
HostInOutContextUptime
DDekaLLMfp8$0.02 in·$0.03 out·131k·99.6% upDDeepInfrafp8$0.02 in·$0.03 out·131k·99.8% upPParasailfp8degraded$0.03 in·$0.03 out·131k·91.6% upNNovitafp8degraded$0.04 in·$0.17 out·60k·68.4% up

Per million tokens. Prices and uptime via OpenRouter, checked 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Mercury 2.5 is developed by Inception while Mistral Nemo is developed by Mistral AI. Mercury 2.5 has a 260K token context window vs Mistral Nemo's 128K. 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 Nemo 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 Nemo costs $0.03/M input tokens. Mistral Nemo is $0.01/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 Nemo 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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Model pages

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