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  5. DeepSeek V3.2 SpecialevsMercury 2.5
Updated Sep 2026

DeepSeek V3.2 SpecialevsMercury 2.5

Mercury 2.5 is cheaper than DeepSeek V3.2 Speciale at $0.04/M vs $0.28/M input tokens.

DeepSeek V3.2 Speciale and Mercury 2.5 compared across 17 shared prompts
SpecDeepSeek V3.2 SpecialeMercury 2.5
Input price$0.28/M tokens$0.04/M tokens
Output price$0.42/M tokens$0.15/M tokens
Context window131K tokens260K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedDec 2025Sep 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 17 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…

DeepSeek V3.2 Speciale170 words
"Honey-Lemon Bliss Bites" A sweet, tangy, and chewy treat that’s quick to make with pantry staples! Ingredients: 1 cup rolled oats 1/4 cup honey 2 tbsp peanut butter (or any nut/seed butter) 1 tbsp lemon zest (or 1 tsp lemon juice) 1/4 tsp vanilla extract 1/4 cup shredded coconut (optional, for coating) A pinch of salt…
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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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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…

DeepSeek V3.2 Speciale2,320 words
We need to generate three explanations for how a large language model learns and generates text, each tailored to a different audience: experienced software engineer (no ML), PhD physicist (mathematical precision), and venture capitalist (business evaluation). Each explanation should be 2-3 paragraphs.
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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Advanced Investment Memo (IC Memo)

You are writing an internal investment committee memo for a long/short equity fund. Company (fictional): LedgerLift (ticker: LLLT) – B2B…

DeepSeek V3.2 Speciale1,916 words
We need to write an internal investment committee memo for a long/short equity fund. The company is LedgerLift (LLLT). We have data and valuation assumptions. We need to produce a recommendation with price target range, business overview, KPI quality check, base/bull/bear DCF model, comps cross-check, catalysts, risks…
Read the full answer
Mercury 2.5735 words
To: Investment Committee From: Equity Research Date: September 22, 2026 Subject: LedgerLift (LLLT) – Initiation of Coverage Recommendation & Thesis Recommendation: Long 12-Month Price Target: $55 – $68 Thesis: LedgerLift offers a rare combination of high-quality SaaS unit economics (NRR 123%, CAC payback 18mo) 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…

DeepSeek V3.2 Speciale10,011 words
We need to consider a counterfactual: the transistor invented in 1920 instead of 1947. The transistor is a fundamental building block of modern electronics. Its invention revolutionized computing, communications, and many other fields. Pushing its invention back 27 years would have profound effects.
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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…

DeepSeek V3.2 Speciale1,373 words
Dish Name: Squab, Black Garlic, White Chocolate Espuma, and Sour Cherry Gastrique Conceptual Narrative This dish is a celebration of contrasts, where the deep, fermented sweetness of black garlic meets the delicate creaminess of white chocolate, both complementing the rich, gamey notes of perfectly cooked squab.
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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…

DeepSeek V3.2 Speciale6,189 words
We are the CEO of a pharmaceutical company. Our best-selling drug: $2B annual revenue, used by 4 million patients for chronic pain. Internal research team flagged a rare but serious side effect: liver failure in approximately 1 in 8,000 patients over 5 years.
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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.
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Our Verdict
DeepSeek V3.2 Speciale
DeepSeek V3.2 Speciale
Mercury 2.5
Mercury 2.5

Not enough votes to call it. On the specs, nothing separates them.

Too close to call
API pricing

Cost per 1M tokens

DeepSeek V3.2 Speciale
Input
$0.28
Output
$0.42
Mercury 2.5
Input
$0.04
7.0× cheaper
Output
$0.15
2.8× cheaper

Mercury 2.5 is cheaper on both: 7.0× input, 2.8× output.

Where to run it

1 host

DeepSeek V3.2 Speciale

No hosts listed on OpenRouter.

Mercury 2.51 host
HostInOutContextUptime
Inception$0.04 in·$0.15 out·260k·100% up

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

Research

What we learned reading every model

FAQ

Common questions

DeepSeek V3.2 Speciale is developed by DeepSeek while Mercury 2.5 is developed by Inception. DeepSeek V3.2 Speciale has a 131K token context window vs Mercury 2.5's 260K. You can compare their actual outputs across 17 challenges on Rival to see how they differ in practice.

It depends on your use case. DeepSeek V3.2 Speciale and Mercury 2.5 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 17 challenges so you can judge which fits your needs best.

DeepSeek V3.2 Speciale costs $0.28/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $0.24/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 DeepSeek V3.2 Speciale 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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