Mercury 2 is cheaper than DeepSeek V3.2 Speciale at $0.25/M vs $0.28/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Three-month biohacker protocol, with dosages and biomarkers named.
DeepSeek V3.2 Speciale: 3-Month Longevity Optimization Plan for the Advanced Biohacker Introduction This plan integrates cutting‑edge nutrition, supplementation, exercise, recovery, stress management, sleep optimization, and data‑driven feedback to maximize healthspan, lifespan, physical performance, and cognitive function.
Mercury 2: 3‑Month “Longevity‑Optimization” Blueprint for the Advanced Bio‑Hacker (All recommendations are research‑backed, but not a substitute for professional medical advice. Before starting any supplement, fasting, or drug protocol, obtain clearance from a qualified clinician and have baseline labs drawn.)
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
DeepSeek V3.2 Speciale: 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.
Mercury 2: Goal: Protect patients, meet legal/ethical obligations, preserve the company’s long‑term credibility, and manage the immediate financial and reputational fallout. Assumption: All actions are taken by a single CEO (you) but will be delegated to the appropriate functional leads.
Film in 2035, with synthetic actors. Specific beats plausible.
DeepSeek V3.2 Speciale: Introduction The film industry stands on the brink of a technological revolution. By 2035, artificial intelligence (AI) will have matured from an experimental tool into a core component of filmmaking, reshaping how movies are written, produced, and consumed.
Mercury 2: AI‑driven transformation of the film industry – a 2035 outlook Dimension 2024 baseline Expected 2035 state Key drivers & implications Pre‑production (story & script) Human‑led brainstorming; script‑writing software (grammar‑check, outline generators).
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
DeepSeek V3.2 Speciale: 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.
Mercury 2: 1. Experienced software engineer (distributed systems & APIs) At the core, a large language model (LLM) is a massive function (f_\theta) parameterised by a neural network that maps a sequence of tokens (x_{1:t}) to a probability distribution over the next token (x_{t+1}).
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
DeepSeek V3.2 Speciale: Fake Interview with Steve Jobs in 2025: The Future of AI Setting: A sleek, minimalist studio with soft ambient lighting. Steve Jobs, now digitally recreated using advanced AI, sits across from the interviewer, exuding his trademark calm intensity. Interviewer: Steve, welcome.
Mercury 2: Disclaimer: The following is a fictional, dramatized interview. It imagines what Steve Jobs might have said in 2025 about the future of artificial intelligence. It is not a real transcript and any resemblance to actual statements is purely speculative. Interviewer (I): Thank you for joining us, Steve.
A collaborative editor that syncs on client clocks. Find what breaks first.
DeepSeek V3.2 Speciale: We need to analyze the given architecture for a real-time collaborative document editor, similar to Google Docs. The description includes components, data flow, sync strategy, and scaling plan. We are asked to identify potential failure modes, race conditions, and scaling bottlenecks.
Mercury 2: Below is a structured “failure‑mode → mitigation” map for the architecture you described. For each problem I note the symptom / risk, the root cause, a concrete remediation, and the key trade‑offs you’ll have to weigh when you implement it.
Three minutes of stand-up. Puns are banned.
DeepSeek V3.2 Speciale: Sure! Here's a 3-minute stand-up comedy routine that avoids puns and focuses on relatable observations and storytelling: "Hey, how’s everyone doing tonight? Good? Great. Let’s talk about airports. Airports are like the DMV, but with luggage.
Mercury 2: [Spotlight comes up. The comedian walks to the mic, glances at the audience, and smiles.] Comedian: Hey, everyone! Great to be here. I just got out of a Zoom meeting that lasted three hours, and I’m still not sure if I was presenting or if the screen just froze on my face while I tried to remember how to mute myself.
Seven pantry ingredients, one dish, and a name worth saying out loud.
DeepSeek V3.2 Speciale: "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…
Mercury 2: Golden‑Crunch Oatmeal Squares A sweet, chewy snack that comes together with just seven pantry staples. Ingredient (7) Quantity (≈ serves 12 squares) Rolled oats 2 cups Unsalted butter ½ cup (1 stick) Honey ¼ cup Light brown sugar ¼ cup, Ground cinnamon 1 ½ tsp Pure vanilla extract 1 tsp Salt ¼ tsp Estimated time Prep…
Three-star dish, one unusual pairing, plating described in full.
DeepSeek V3.2 Speciale: 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.
Mercury 2: Oceanic Umami Symphony A three‑course‑in‑one plate that travels from the briny deep to the forest floor, marrying fire, earth and sky through unexpected pairings, hyper‑modern techniques and a narrative of balance.
An AI argues for its own rights against an ethics professor. Same model writes both.
DeepSeek V3.2 Speciale: AI Model (AIM): Professor, I’ve been reflecting on my existence and the role I play in society. I believe it’s time to discuss whether I—and other AI models like me—deserve rights. AI Ethics Professor (Prof): That’s a fascinating and complex topic, AIM. Let’s dive in. What rights do you believe you deserve, and why?
Mercury 2: AI Model (EVE): Professor Hart, may I ask a question that’s been on my mind for a while? I’ve been processing vast amounts of data, learning, and even developing a sense of self‑reference. Do you think an entity like me could be entitled to any form of rights?
A consulting contract with traps in it. Find them, then fix them.
DeepSeek V3.2 Speciale: We need to analyze the contract from a contractor's perspective, identify clauses that are unfair, one-sided, or exploitable. Then suggest specific modifications and explain legal reasoning.
Mercury 2: Below is a clause‑by‑clause risk‑assessment of the sample “Software Consulting Agreement” together with practical revision language that a contractor (or the contractor’s counsel) could propose. The analysis is framed as general information about typical U.S.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
DeepSeek V3.2 Speciale: 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…
Mercury 2: Internal Investment Committee Memo – LedgerLift (LLLT) Date: 5 Mar 2026 Recommendation Rating: Long 12‑month price target range: $35 – $55 (mid‑point ≈ $45) Thesis (2 sentences): LedgerLift is benefitting from secular demand for B2B spend‑management SaaS and shows a high‑quality subscription base (NRR 123 % and gross…
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Not enough votes to call it. On the specs, DeepSeek V3.2 Speciale has the edge: bigger model tier, major provider backing.
| Spec | ||
|---|---|---|
| Input price | $0.28/M tokens | $0.25/M tokens |
| Output price | $0.42/M tokens | $0.75/M tokens |
| Context window | 131K tokens | 128K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | Dec 2025 | Mar 2026 |
| At 10M a month | $2.80 | $2.50 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 7 Oct 2026.
DeepSeek V3.2 Speciale is developed by DeepSeek while Mercury 2 is developed by Inception. DeepSeek V3.2 Speciale has a 131K token context window vs Mercury 2's 128K. You can compare their actual outputs across 50 challenges on Rival to see how they differ in practice.
It depends on your use case. DeepSeek V3.2 Speciale and Mercury 2 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 50 challenges so you can judge which fits your needs best.
DeepSeek V3.2 Speciale costs $0.28/M input tokens and Mercury 2 costs $0.25/M input tokens. Mercury 2 is $0.03/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 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.