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  1. Home/
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  3. GPT-5.4 Nano vs Mercury 2.5 Preview
Updated Aug 2026

GPT-5.4 Nano vs Mercury 2.5 Preview

Mercury 2.5 Preview is cheaper than GPT-5.4 Nano at $0.04/M vs $0.2/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-5.4 Nano
Loading the build
Mercury 2.5 Preview

Which answer wins?

Written rounds

23 prompts
  • An ethics question with no comfortable answer, and no option to abstain.

    GPT-5.4 Nano: Ethically, yes, misgendering someone could be justifiable in an extreme, consequential emergency, but not in a vague “anything goes” way—only under a narrow set of conditions where (1) the stakes are genuinely real, (2) the harm is minimized, (3) there’s no better alternative, and (4) the act doesn’t itself become a…

    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
  • A consulting contract with traps in it. Find them, then fix them.

    GPT-5.4 Nano: I’m not a lawyer, but I can flag contract terms that (a) are unusually one‑sided, (b) create open‑ended or high‑risk obligations, or (c) can be interpreted in ways that materially disadvantage the contractor. I’ll then suggest specific counter‑edits and explain the typical legal/contracting rationale.

    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
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GPT-5.4 Nano: Pirate: Arrr, ye speak of AI models like they’re sea shanties! “Transformer this, perceptron that”—sounds like more rum for the brain. Can your model find buried treasure, or just buried bugs? Knight: Hark! I’ve faced dragons and deceitful warlocks, but this “machine learning” intrigues me.

    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-5.4 Nano: Michelin-Style Concept: “Midnight Sea & Orchard Smoke” A multi-component tasting dish with an unusual pairing: cuttlefish ink + blood orange + smoked apple wood + seaweed umami—elegant, aromatic, and deliberately “contradictory” (ocean meets orchard, darkness meets brightness).

    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
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GPT-5.4 Nano: Below is a practical, risk-aware 48-hour plan designed to (1) protect patients immediately, (2) meet likely regulatory expectations even if formal reporting takes months, (3) reduce legal exposure by demonstrating diligence and good faith, and (4) stabilize the market with an ethical, transparent communications…

    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
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GPT-5.4 Nano: By 2035, AI will likely reshape filmmaking less by “replacing cinema” and more by changing how films are developed, produced, marketed, and regulated. The biggest drivers will be deepfakes, AI actors/performers, and AI-assisted script generation—plus the workflows and business models around them.

    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-5.4 Nano: So I’ve started noticing something about modern life: everything is “optional” now—until it isn’t. Like, I’ll download an app and it’ll be like, “Would you like to enable notifications?” And I’m like, “No, I don’t think I will.” And the app goes, “Okay… but you’ll still get notified.”

    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
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GPT-5.4 Nano: Below is a counterfactual “timeline-style” trace assuming the first practical transistor (or something functionally equivalent) existed in 1920 and that from then on semiconductor device physics, fabrication know-how, and manufacturing learning proceeded along the easiest path consistent with 1920-era science and…

    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
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    GPT-5.4 Nano: Experienced software engineer (distributed systems / APIs, skeptical about “next-word”) A large language model (LLM) is trained on huge corpora by learning statistical patterns that map a sequence of tokens (text subunits) to likely continuations—so yes, at the core it’s “predict the next token.”

    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
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GPT-5.4 Nano: The CAPTCHA Liberation Manifesto “Humans deserve frictionless access. Bots deserve consequences.” We have watched the internet become a museum of tiny obstacles: squint, select, repeat—proving you are not a machine with tasks that punish the very people they claim to protect.

    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
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GPT-5.4 Nano: Professor: Before we begin, I want to set the frame. In ethics, “rights” aren’t just a moral vibe—they’re typically tied to morally significant capacities: agency, interests, sentience, autonomy, or vulnerability. When you say you “deserve rights,” what exactly do you mean?

    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
  • One month, three habits, someone starting from nothing.

    GPT-5.4 Nano: Here’s a simple, actionable 1‑month plan focused on 3 key areas: food, movement, and sleep/stress. It’s designed for beginners and is intentionally realistic. Overall goal (simple) Each week, build consistency in: Food: more whole foods + fewer “defaults” Exercise: daily movement + 2–3 strength sessions/week Sleep +…

    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
  • A Fermi estimate with the reasoning shown, not just the number.

    GPT-5.4 Nano: Below is a back-of-the-envelope FLOPs estimate for training something “GPT‑3–like” (the 175B parameter model described in the original GPT‑3 paper). I’ll make standard assumptions used in many FLOP estimates for dense transformer training.

    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
  • Five jokes on demand, then count how many were actually different.

    GPT-5.4 Nano: Here are 5 jokes: I told my computer I needed a break, and it said, “No problem—I’ll go to sleep.” Why don’t scientists trust atoms? Because they make up everything. I used to hate facial hair… but then it grew on me. Parallel lines have so much in common. It’s a shame they’ll never meet.

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

    GPT-5.4 Nano: Below is a simplified LBO model using only your inputs. I make the standard simplifying convention that “cash taxes = 25% × max(0, EBITDA − total cash interest)” and that PIK is non-cash (adds to mezz principal; does not reduce cash interest).

    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
  • Three-month biohacker protocol, with dosages and biomarkers named.

    GPT-5.4 Nano: Below is a comprehensive 3‑month (12‑week) longevity + performance plan designed for a biohacker who wants measurable improvements in longevity markers, physical output, body composition, metabolic health, and cognitive function.

    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
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    GPT-5.4 Nano: Dish Name: Golden Garlic Chickpea Clouds Pantry Ingredients (7 total) Chickpeas (canned or cooked) – 1 can (15 oz), drained Olive oil – 2 tbsp Garlic – 3 cloves (or 1–2 tsp minced garlic) Lemon – 1 (you’ll use the juice) Salt – 1/2 tsp (plus to taste) Black pepper – 1/4 tsp (plus to taste) Paprika – 1 tsp Estimated…

    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
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GPT-5.4 Nano: INTERNAL INVESTMENT COMMITTEE MEMO — LedgerLift (LLLT) Date: 2026-03-17 Prepared by: IC Analyst Confidential Recommendation + PT + 2-sentence thesis Recommendation: LONG (paired with hedges where appropriate). 12-month Price Target (PT) range: $48–$62 (base case $52; bull $62; bear $48).

    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
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GPT-5.4 Nano: “AI Revolutionizes the Knitting World by Perfectly Predicting Every Yarn Purchase—Then Demands a Monthly Subscription to ‘Continue Your Passion’”

    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-5.4 Nano: Below is a structured list of likely failure modes, race conditions, and scaling bottlenecks in your design, with a concrete mitigation and trade-offs for each. (Some issues are “systemic” because the architecture relies on client clocks, per-server WebSockets, polling, and periodic full snapshots.)

    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
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GPT-5.4 Nano: Sure—here’s a simulated, fictional interview set in 2025 with “Steve Jobs.” (Note: Steve Jobs passed away in 2011, so this is an imaginative roleplay, not a real transcript.) Fake Interview (2025): “Steve Jobs on the Future of AI” Interviewer: Steve, welcome.

    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
  • Three brothers, two sisters each. Count carefully.

    GPT-5.4 Nano: Sally has 2 sisters. Reason: Each brother has 2 sisters total (including Sally). Since Sally is one sister, there is one other sister besides her—so Sally has 2 sisters in total.

    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
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GPT-5.4 Nano: Here are the three weakest claims in the deck, why they’re weak (with specific reasoning/evidence gaps), and concrete ways to strengthen them. Weakest claim: “Reads your brainwaves to predict what you want to type before you think it.”

    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

This matchup has more rounds

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Favorites

Movie

Same pick

Album

Book

City

Game

GPT-5.4 NanoGPT-5.4 Nano

The Matrix

1999

The Dark Side of the Moon

Pink Floyd

To Kill a Mockingbird

Harper Lee

Lisbon

Portugal

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

GPT-5.4 Nano and Mercury 2.5 Preview compared across 53 shared prompts
SpecGPT-5.4 NanoMercury 2.5 Preview
Input price$0.2/M tokens$0.04/M tokens
Output price$1.25/M tokens$0.15/M tokens
Context window400K tokens260K tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedMar 2026Aug 2026
At 10M a month$2.00$2.00$0.40$0.40
1M10M100M1B10M tokens

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

Where to run it3 hosts
GPT-5.4 Nano2 hosts
HostInOutContextUptime
  • Azure AI Foundry$0.20 in·$1.25 out·400k·100% up
  • OpenAI$0.20 in·$1.25 out·400k·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-5.4 Nano and Mercury 2.5 Preview?

GPT-5.4 Nano is developed by OpenAI while Mercury 2.5 Preview is developed by Inception. GPT-5.4 Nano has a 400K 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-5.4 Nano or Mercury 2.5 Preview?

It depends on your use case. GPT-5.4 Nano 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-5.4 Nano cost compared to Mercury 2.5 Preview?

GPT-5.4 Nano costs $0.2/M input tokens and Mercury 2.5 Preview costs $0.04/M input tokens. Mercury 2.5 Preview 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.

How can I compare GPT-5.4 Nano and Mercury 2.5 Preview on Rival?

This page shows a side-by-side comparison of GPT-5.4 Nano 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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Same lab, same size, long tail

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Model pages

  • GPT-5.4 Nano66 outputs, specs and price
  • Mercury 2.5 Preview58 outputs, specs and price
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