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  5. DeepSeek V4 ProvsMercury 2.5
Updated Sep 2026

DeepSeek V4 ProvsMercury 2.5

Mercury 2.5 is cheaper than DeepSeek V4 Pro at $0.04/M vs $1.74/M input tokens.

DeepSeek V4 Pro and Mercury 2.5 compared across 20 shared prompts
SpecDeepSeek V4 ProMercury 2.5
Input price$1.74/M tokens$0.04/M tokens
Output price$3.48/M tokens$0.15/M tokens
Context window1.0M tokens260K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedApr 2026Sep 2026
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) –…

DeepSeek V4 Pro602 words
HarborTech Services (HTS) – 5-Year LBO Model Projected Financials & Debt Schedule (US$ millions, fiscal years ending December) FY2026 FY2027 FY2028 FY2029 FY2030 Revenue 972.0 1,040.0 1,102.4 1,157.6 1,215.4 EBITDA 136.1 156.0 176.4 191.0 206.6 Margin 14.0% 15.0% 16.0% 16.5% 17.0% Cash Interest Term Loan (9% cash) 43.2…
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…

DeepSeek V4 Pro336 words
Midnight Pantry Pomodoro Penne A deeply comforting, no-fuss pasta that proves the humblest staples can shine like a moonlit trattoria dinner. Ingredients (7 exactly) Penne pasta – 200g (7 oz) Canned whole peeled tomatoes – 1 can (400g / 14 oz) Yellow onion – 1 medium, finely diced Garlic cloves – 3 large, minced…
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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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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 V4 Pro1,680 words
Unagi: Midori no Yume (Eel’s Green Dream) A multi-sensory kaiseki-inspired contemporary dish that unites the deep umami of charcoal-grilled freshwater eel with the ethereal chill of matcha snow, punctuated by dashi pearls, yuzu kosho gel, and a wisp of cherry blossom smoke.
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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.
Read the full answer

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 V4 Pro1,002 words
1. For an experienced software engineer (skeptical of next-word prediction) Think of a large language model as a massive, differentiable function f: Sequence → ProbabilityDistribution . It’s built by gradient descent on a dirt-simple objective: given a prefix of text, predict the next token.
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.
Read the full answer

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.…

DeepSeek V4 Pro531 words
Here are the three weakest claims in the pitch, why they undermine credibility, and exactly how to fix them. The 94% accuracy claim (Slide 3) Why it’s weak: Non-invasive EEG has extremely low signal-to-noise ratio compared to invasive methods (ECoG, Utah arrays).
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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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Historical Counterfactual Analysis

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

DeepSeek V4 Pro2,259 words
If the transistor had emerged in 1920—a full 27 years earlier—the entire trajectory of the 20th century would have been radically altered. The vacuum-tube era would have been cut short, unleashing a cascade of miniaturized, reliable, and energy-efficient electronics that would accelerate computing, communications, and…
Read the full answer
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.
Read the full answer
Our Verdict
DeepSeek V4 Pro
DeepSeek V4 Pro
Mercury 2.5
Mercury 2.5Runner-up

Not enough votes to call it. On the specs, DeepSeek V4 Pro has the edge: bigger model tier, bigger context window, major provider backing.

Mercury 2.5 costs 23x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

DeepSeek V4 Pro
Input
$1.74
Output
$3.48
Mercury 2.5
Input
$0.04
44× cheaper
Output
$0.15
23× cheaper

Mercury 2.5 is cheaper on both: 44× input, 23× output.

Where to run it

16 hosts, cheapest first

DeepSeek V4 Pro15 hosts
HostInOutContextUptime
SStreamLakefp8$0.96 in·$1.91 out·1M·99% upGGMI Cloudfp8$0.96 in·$1.91 out·1M·99.8% upDDigitalOcean$1.04 in·$2.09 out·1M·100% upDDeepInfrafp8$1.30 in·$2.60 out·1M·99.2% upAlibaba Cloudfp8$1.42 in·$2.83 out·1M·100% upSSiliconFlowfp8$1.50 in·$3.13 out·1M·99.4% up
9 more hostsFewer hosts
NNovitafp8$1.60 in·$3.20 out·1M·100% upVVenice$1.65 in·$3.30 out·1M·97.9% upAAtlasCloudfp4$1.68 in·$3.38 out·1M·99.8% upBaidu Qianfanfp8$1.69 in·$3.38 out·1M·99.9% upBBasetenfp4$1.74 in·$3.48 out·1M·99.6% upNNextBitfp8$1.74 in·$3.48 out·1M·97.6% upPParasailfp8$1.74 in·$3.48 out·1M·99% upAzure AI Foundry$1.91 in·$3.83 out·1M·95.5% upCloudflare Workers AIdegraded$1.15 in·$2.55 out·1M·94.2% up
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

DeepSeek V4 Pro is developed by DeepSeek while Mercury 2.5 is developed by Inception. DeepSeek V4 Pro 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. DeepSeek V4 Pro 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.

DeepSeek V4 Pro costs $1.74/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $1.70/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 V4 Pro 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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