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  5. DeepSeek V3.2 ExpvsMercury 2.5
Updated Sep 2026

DeepSeek V3.2 ExpvsMercury 2.5

Mercury 2.5 is cheaper than DeepSeek V3.2 Exp at $0.04/M vs $0.2/M input tokens.

DeepSeek V3.2 Exp and Mercury 2.5 compared across 20 shared prompts
SpecDeepSeek V3.2 ExpMercury 2.5
Input price$0.2/M tokens$0.04/M tokens
Output price$0.4/M tokens$0.15/M tokens
Context window164K tokens260K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedSep 2025Sep 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 V3.2 Exp330 words
Year-by-Year Financial Projections (FY2026–FY2030) All figures in $ millions. Year Revenue EBITDA Cash Interest (Term Loan / Mezz / Total) Cash Taxes Capex ΔNWC FCF after Debt Service* Ending Term Loan Ending Mezzanine 2026 972.00 136.08 43.20 / 21.60 / 64.80 17.82 29.16 0.36 40.74 434.46 183.60 2027 1,040.04 156.01…
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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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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 Exp334 words
Of course! Here is a simple, delicious recipe for a savory dish that feels comforting and elegant, using just 7 common pantry staples. Golden Crisp Parmesan Chickpeas This dish transforms humble chickpeas into a crispy, flavorful, and satisfying meal.
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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…
Read the full answer

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 Exp785 words
Hour-by-Hour Action Plan (Next 48 Hours) Hour 0–2 (Immediate Actions) Secure all data & convene core crisis team Gather Head of R&D, Chief Medical Officer, General Counsel, CFO, Head of Communications, and Head of Regulatory Affairs.
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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.
Read the full answer

Debug This Architecture

Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with…

DeepSeek V3.2 Exp664 words
Critical Issues in Proposed Architecture 1. WebSocket Broadcast Isolation Issue: Each server only broadcasts to its own WebSocket clients → users connected to different servers won't see real-time updates from each other.
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Mercury 2.5672 words
Critical Analysis of Collaborative Editor Architecture Below is a detailed breakdown of failure modes, race conditions, and scaling bottlenecks, along with proposed solutions and their trade-offs.
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 V3.2 Exp708 words
For the Experienced Software Engineer Think of it less like a deterministic program and more like an emergent API for knowledge. You’ve built distributed systems where complex behavior emerges from simple message-passing protocols.
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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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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 V3.2 Exp720 words
Here are the 3 weakest claims in the pitch deck, along with analysis and suggested improvements. Claim: "Our proprietary EEG headband... decodes neural patterns into text with 94% accuracy." (Slide 3) Why it's weak: This claim is extraordinarily high for non-invasive EEG-based text decoding and lacks critical context.
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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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Our Verdict
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp
Mercury 2.5
Mercury 2.5

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

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

DeepSeek V3.2 Exp
Input
$0.20
Output
$0.40
Mercury 2.5
Input
$0.04
5.0× cheaper
Output
$0.15
2.7× cheaper

Mercury 2.5 is cheaper on both: 5.0× input, 2.7× output.

Where to run it

4 hosts

DeepSeek V3.2 Exp3 hosts
HostInOutContextUptime
AAtlasCloudfp8$0.27 in·$0.41 out·164k·98.3% upNNovitafp8$0.27 in·$0.41 out·164k·99.4% upSSiliconFlowfp8$0.27 in·$0.41 out·164k·98.1% 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 V3.2 Exp is developed by DeepSeek while Mercury 2.5 is developed by Inception. DeepSeek V3.2 Exp has a 164K 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 V3.2 Exp 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 V3.2 Exp costs $0.2/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $0.16/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 Exp 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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