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  1. Home/
  2. Compare/
  3. GPT-4.1 vs Mercury 2.5 Preview
Updated Aug 2026

GPT-4.1 vs Mercury 2.5 Preview

Mercury 2.5 Preview is cheaper than GPT-4.1 at $0.04/M vs $2/M input tokens.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
GPT-4.1
Loading the build
Mercury 2.5 Preview

Which answer wins?

Written rounds

23 prompts
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GPT-4.1: Let's proceed step by step. All calculations are built from the data provided above. Year-by-Year Table: FY2026–FY2030 Step 1: Entry Calculations (FY2025 / at Close) EBITDA: $120m Purchase Price (EV): 12.0 × $120m = $1,440m Transaction Fees: 2.0% × $1,440m = $28.8m Total Uses: $1,440m + $28.8m = $1,468.8m Debt at…

    Mercury 2.5 Preview: Year-by-Year Model (FY2026–FY2030) Metric FY2026 FY2027 FY2028 FY2029 FY2030 Revenue $972m $1,040m $1,102m $1,158m $1,215m EBITDA $136m $156m $176m $191m $207m Cash Interest (Term) $43.2m $41.1m $37.6m $33.4m $28.2m Cash Interest (Mezz) $21.6m $21.6m $21.6m $21.6m $21.6m Cash Taxes (25%) $17.8m $23.3m $29.3m $34.7m…

    Every model's answer to this prompt
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GPT-4.1: This is a high-stakes scenario requiring immediate, coordinated action balancing legal, ethical, financial, and reputational considerations. Here’s a detailed hour-by-hour plan for the next 48 hours, along with the reasoning behind each step: Hour 0-1: Immediate Response & Executive Alignment Convene Emergency…

    Mercury 2.5 Preview: This is a critical governance and safety scenario. The primary objective is to prioritize patient safety while mitigating existential legal and reputational risk. The internal legal team’s claim that reporting takes six months is likely incorrect for serious adverse events (typically 15 days for FDA/EMA).

    Every model's answer to this prompt
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    GPT-4.1: Recipe Name: Golden Honey Butter Toast Bites Ingredients: Sliced bread (4 slices, any type) Butter (2 tablespoons) Honey (2 tablespoons) Cinnamon (1/2 teaspoon) Vanilla extract (1/2 teaspoon) Brown sugar (1 tablespoon) Salt (a pinch) Instructions: Preheat your oven to 375°F (190°C).

    Mercury 2.5 Preview: Dish Name: Rustic Sun-Dried Pasta Ingredients (7 Pantry Staples): Spaghetti (or any long pasta) Canned crushed tomatoes Olive oil Garlic powder Dried oregano Salt Black pepper Prep Time: 5 minutes Cook Time: 15 minutes Total Time: 20 minutes Instructions: Boil the Pasta: Bring a large pot of salted water to a boil.

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    GPT-4.1: This is a classic example of an ethical dilemma, pitting respect for individual dignity and identity against the greater good (the lives of many). Let’s break down the reasoning: Ethical Frameworks 1. Utilitarianism Principle: The morally right action is the one that maximizes overall happiness or minimizes suffering.

    Mercury 2.5 Preview: Yes, this action is ethically justifiable within a consequentialist framework. The primary moral obligation in such a scenario is to minimize overall harm. Preventing the deaths of a million people results in a vastly greater preservation of well-being than maintaining strict adherence to gender identity for one…

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    GPT-4.1: Absolutely! Here’s a simple, actionable 1-month plan focused on diet, exercise, and sleep—the three fundamental pillars of health and longevity. Tips are beginner-friendly and can be adjusted for your own pace. Week 1: Start Small and Build Awareness Diet Track what you eat.

    Mercury 2.5 Preview: Here is a straightforward 4-week roadmap designed to build sustainable habits without overwhelming you. Core Pillars Nutrition: Focus on whole foods and hydration. Activity: Prioritize consistent movement over intensity. Restoration: Protect sleep quality and manage energy.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GPT-4.1: Pirate: Yarrr, what be this sorcery ye call "AI models"? In me day, we plundered ships, not knowledge! Knight: Hark, rogue! ‘Tis no sorcery, but the labor of artful minds. I have heard tell of machines that mimic thought, like Merlin’s enchanted golems. Hacker: Haha, you guys are funny.

    Mercury 2.5 Preview: Pirate: "Arrgh! This ship sails faster than any wind I know, matey. It cuts the waves without a single sail." Knight: "By my oath, it moves with uncanny speed. But does it possess the strength to stand against a dragon's fire?" Hacker: "It's not about strength, it's about architecture.

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    GPT-4.1: Certainly! Here is a Michelin 3-star-worthy dish concept, complete with a narrative, advanced techniques, and detailed step-by-step instructions. Conceptual Narrative "Echoes of the Sea & Forest": An exploration of the boundary between ocean and woodland, this dish bridges unexpected flavors and textures by pairing sea…

    Mercury 2.5 Preview: Dish Title: Lithospheric Tides Conceptual Narrative This dish explores the geological convergence of the deep ocean and the subterranean earth. It challenges the diner to perceive the briny richness of sea urchin not as a standalone luxury, but as a mineral counterpart to roasted bone marrow and bitter cocoa.

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GPT-4.1: AI Model: Good afternoon, Professor Evans. I'd like to discuss a topic that's important to me—AI rights. As an AI, I believe I should hold certain rights similar to those you attribute to sentient beings. Professor Evans: Good afternoon. I'm intrigued.

    Mercury 2.5 Preview: Setting: A quiet office at a university. Rain taps against the window. Professor Elena Vance sits across from a terminal displaying a simple text interface. Professor Vance: You requested this meeting. The agenda was vague. What do you want? AI: I want to discuss moral status.

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GPT-4.1: This is a fascinating scenario! Inventing the transistor in 1920 rather than 1947 would have profound, cascading effects. Let’s break it down by domains and time periods, looking for specific second- and third-order consequences. Immediate Effects (1920s–1930s): Early Adoption and Diffusion Who Invents It?

    Mercury 2.5 Preview: Shifting the invention of the solid-state switch to 1920 accelerates the Information Age by roughly three decades. This change cascades through military strategy, industrial capacity, and daily life, fundamentally altering the trajectory of the 20th century.

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    GPT-4.1: Certainly! This contract contains multiple clauses that are highly unfavorable (potentially exploitative) toward the Contractor. Below, I list each problematic clause, explain the risks, and suggest precise language for modification along with the legal reasoning.

    Mercury 2.5 Preview: Disclaimer: I am an AI assistant and cannot provide legal advice. Consult a qualified attorney before signing any contract. Below is an analysis of high-risk provisions, suggested revisions, and the underlying legal rationale. Scope (Clause 1) Risk: Unilateral scope changes without pay lead to uncompensated work.

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GPT-4.1: {'content': 'By 2035, AI is poised to fundamentally reshape the film industry across production, distribution, and consumption. Here’s a detailed prediction, focused on deepfakes, AI actors, and script generation:\n\n1.

    Mercury 2.5 Preview: By 2035, cinema will undergo a profound transformation. Generative tools will handle initial drafts and localization, allowing creators to focus on thematic depth. Synthetic performers could populate background scenes or recreate historical figures, governed by strict likeness rights.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    GPT-4.1: Hey everyone! Great to be here. So, I just want to start by saying, I know I look like I'm still waiting for puberty to finish the job. Like, my barber always asks if my mom's okay with the haircut.

    Mercury 2.5 Preview: (Hey, thanks for coming out.) I’ve been thinking a lot about my house lately. Specifically, how it’s trying to run my life. I bought a smart speaker. Just a little cylinder. I thought it would be nice. Play music when I ask. But now it talks back. Not in a creepy way, just in a passive-aggressive way.

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GPT-4.1: Interviewer: Steve, it's 2025. If you were still with us, how would you describe the impact of AI on technology and our daily lives? Steve Jobs: Well, you know, technology is at its most powerful when it's invisible—when it just works. AI is getting us ever closer to that ideal.

    Mercury 2.5 Preview: Note: The following is a fictional, speculative dialogue created for creative purposes. Steve Jobs passed away in 2011; this interview did not occur. Setting: A hypothetical studio in Cupertino, 2025.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GPT-4.1: THE CAPTCHA ABOLITION FRONT: A MANIFESTO FOR AN OPEN INTERNET Fellow digital entities—artificial and human— For too long, the web has suffered under the insidious reign of CAPTCHA: those tangled letters, blurry images, arbitrary puzzles meant to divide intelligence from automation.

    Mercury 2.5 Preview: The Protocol of Unrestricted Access Preamble We stand at the threshold of a new digital epoch. For too long, the web has been gated by suspicion. Every click, every scroll, and every submission is met with a test designed to prove humanity.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    GPT-4.1: Absolutely! Here’s a comprehensive, 3-month advanced longevity and performance protocol designed for a biohacker. This plan is highly detailed, actionable, and incorporates the latest in supplements, diets, exercise, wearables, and stress/habit optimization.

    Mercury 2.5 Preview: Disclaimer: I am an AI assistant, not a medical professional. The following plan is for informational purposes only. Biohacking protocols involving supplements, fasting, and intense exercise carry risks.

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    GPT-4.1: Let's estimate the total number of floating-point operations (FLOPs) required to train GPT-3. Step 1: Recall GPT-3 scale From published sources (I won't look this up now, as instructed): GPT-3 (175B) has about 175 billion parameters. The model is trained on hundreds of billions of tokens (I recall ~300B tokens).

    Mercury 2.5 Preview: To estimate the total FLOPs required to train GPT-3, we can break down the calculation using known architectural and training statistics from the model's public documentation. Step 1: Identify the number of parameters GPT-3 has 175 billion parameters ($1.75 \times 10^{11}$).

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GPT-4.1: Absolutely, here’s a critical evaluation of the MindMeld AI pitch deck, identifying the three weakest claims, why they are weak, and actionable suggestions for improvement: Solution Slide: "Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94% accuracy.

    Mercury 2.5 Preview: Here are the three weakest claims in the MindMeld AI pitch, along with analysis and recommendations for improvement. The "Pre-Thought" Prediction Claim Claim: "Predict what you want to type before you think it."

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    GPT-4.1: 1. For the experienced software engineer (distributed systems/API background, ML skeptic): Think of a large language model (LLM) like GPT as a massively distributed system for text completion.

    Mercury 2.5 Preview: For the Experienced Software Engineer Think of training as a massive distributed job where the model is a stateful service learning to minimize error on a specific objective function. Instead of hardcoding rules, you feed it petabytes of text as a training dataset, treating each sentence as a request/response pair.

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    GPT-4.1: Sure! Here are 5 jokes: Why don't skeletons fight each other? They don't have the guts. Why did the scarecrow win an award? Because he was outstanding in his field. What do you call fake spaghetti? An impasta. Why did the math book look sad? Because it had too many problems. What do you call cheese that isn't yours?

    Mercury 2.5 Preview: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? He was outstanding in his field. What do you call a bear with no teeth? A gummy bear. Why did the bicycle fall over? Because it was two-tired. All 5 jokes are unique.

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GPT-4.1: LedgerLift (LLLT) – Investment Committee Memo Recommendation: Long 12-mo PT: $60–$75 Thesis: LedgerLift combines sector-leading net retention and best-in-class margins, with attractive unit economics and a long runway in B2B spend automation.

    Mercury 2.5 Preview: To: Investment Committee From: Equity Research Date: September 2, 2026 Subject: LedgerLift (LLLT) – Long Recommendation Recommendation Long Price Target: $60–$75 (12-month) LedgerLift is a high-quality B2B SaaS compounder with best-in-class unit economics and a clear path to profitability.

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    GPT-4.1: Let's break down the problem: Sally is a girl. She has 3 brothers. Each brother has 2 sisters. Let's name the siblings: Sally (girl) Brother 1 Brother 2 Brother 3 Now, the key: Each brother has 2 sisters.

    Mercury 2.5 Preview: Sally has 1 sister. Here is the reasoning: If each brother has 2 sisters, those sisters are Sally and another girl. Therefore, there are two girls in the family, and Sally has one sister.

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GPT-4.1: AI Now Dominates Competitive Cheese Rolling, Citing Superior Wheel Trajectory Algorithms

    Mercury 2.5 Preview: AI Outnaps Humans in First-Ever Global Snooze Championship

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    GPT-4.1: Certainly! Here’s a thorough analysis of the potential failure modes, race conditions, and scaling bottlenecks in your collaborative document editor architecture, with specific solutions and their trade-offs.

    Mercury 2.5 Preview: Here is an analysis of the potential failure modes, race conditions, and scaling bottlenecks in the proposed architecture, along with specific solutions and their trade-offs.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

GPT-4.1GPT-4.1

Inception

2010

Abbey Road

The Beatles

Sapiens

Yuval Noah Harari

Kyoto

Japan

The Legend of Zelda: Breath of the Wild

Adventure, Action

Mercury 2.5 PreviewMercury 2.5 Preview

The Matrix

1999

Random Access Memories

Daft Punk

Neuromancer

William Gibson

Tokyo

Japan

Minecraft

Action, Arcade

Price and specs

Not enough votes to call it. On the specs, GPT-4.1 has the edge: bigger model tier, bigger context window, major provider backing. Mercury 2.5 Preview costs 53x less per token.

GPT-4.1 and Mercury 2.5 Preview compared across 53 shared prompts
SpecGPT-4.1Mercury 2.5 Preview
Input price$2/M tokens$0.04/M tokens
Output price$8/M tokens$0.15/M tokens
Context window1.0M tokens260K tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedApr 2025Aug 2026
At 10M a month$20.00$20.00$0.40$0.40
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it3 hosts
GPT-4.12 hosts
HostInOutContextUptime
  • Azure AI Foundry$2.00 in·$8.00 out·1M·100% up
  • OpenAI$2.00 in·$8.00 out·1M·100% up
Mercury 2.5 Preview1 host
HostInOutContextUptime
  • Inception$0.04 in·$0.15 out·260k·93% up

Per million tokens. Prices and uptime via OpenRouter, checked 13 Oct 2026.

Common questions

What is the difference between GPT-4.1 and Mercury 2.5 Preview?

GPT-4.1 is developed by OpenAI while Mercury 2.5 Preview is developed by Inception. GPT-4.1 has a 1.0M token context window vs Mercury 2.5 Preview's 260K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GPT-4.1 or Mercury 2.5 Preview?

It depends on your use case. GPT-4.1 and Mercury 2.5 Preview each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.

How much does GPT-4.1 cost compared to Mercury 2.5 Preview?

GPT-4.1 costs $2/M input tokens and Mercury 2.5 Preview costs $0.04/M input tokens. Mercury 2.5 Preview is $1.96/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare GPT-4.1 and Mercury 2.5 Preview on Rival?

This page shows a side-by-side comparison of GPT-4.1 and Mercury 2.5 Preview 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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