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  5. GPT-5.4 NanovsMercury 2.5
Updated Sep 2026

GPT-5.4 NanovsMercury 2.5

Mercury 2.5 is cheaper than GPT-5.4 Nano at $0.04/M vs $0.2/M input tokens.

GPT-5.4 Nano and Mercury 2.5 compared across 20 shared prompts
SpecGPT-5.4 NanoMercury 2.5
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 2026Sep 2026
Side by side

Same prompt, both answers

Opening lines from 6 of the 20 prompts both answered. Formatting stripped, words untouched.

Historical Counterfactual Analysis

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

GPT-5.4 Nano2,226 words
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…
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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

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…

GPT-5.4 Nano1,050 words
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.”
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

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…

GPT-5.4 Nano184 words
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…
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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…

GPT-5.4 Nano1,568 words
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).
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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

Mini LBO Underwrite

You are underwriting a 5-year LBO for a private equity investment committee. Company (fictional): HarborTech Services (HTS) –…

GPT-5.4 Nano1,159 words
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).
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

Beginner's Longevity Plan

Outline a simple, actionable 1-month plan for someone starting their journey towards better health and longevity. Focus on 3 key areas…

GPT-5.4 Nano785 words
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 +…
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Mercury 2.5325 words
Here is a simple, actionable 1-month plan to kickstart your journey toward better health and longevity. This guide focuses on three foundational pillars: Nutrition, Movement, and Sleep. Week 1: Awareness & Small Wins Goal: Build habits without overwhelming yourself.
Read the full answer
Our Verdict
GPT-5.4 Nano
GPT-5.4 Nano
Mercury 2.5
Mercury 2.5

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

Mercury 2.5 costs 8.3x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GPT-5.4 Nano
Input
$0.20
Output
$1.25
Mercury 2.5
Input
$0.04
5.0× cheaper
Output
$0.15
8.3× cheaper

Mercury 2.5 is cheaper on both: 5.0× input, 8.3× output.

Where to run it

3 hosts

GPT-5.4 Nano2 hosts
HostInOutContextUptime
Azure AI Foundry$0.20 in·$1.25 out·400k·100% upOpenAI$0.20 in·$1.25 out·400k·100% 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

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

GPT-5.4 Nano 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 GPT-5.4 Nano 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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