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  5. GPT-4.1 MinivsMercury 2.5
Updated Sep 2026

GPT-4.1 MinivsMercury 2.5

Mercury 2.5 is cheaper than GPT-4.1 Mini at $0.04/M vs $0.4/M input tokens.

GPT-4.1 Mini and Mercury 2.5 compared across 20 shared prompts
SpecGPT-4.1 MiniMercury 2.5
Input price$0.4/M tokens$0.04/M tokens
Output price$1.6/M tokens$0.15/M tokens
Context window1.0M tokens260K tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedApr 2025Sep 2026
Side by side

Same prompt, both answers

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

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-4.1 Mini162 words
Dish Name: Honey Garlic Chickpea Stir-fry Ingredients: 1 can chickpeas (drained and rinsed) 2 cloves garlic (minced) 2 tbsp honey 2 tbsp soy sauce 1 tbsp olive oil (or any cooking oil) 1 tsp chili flakes (optional for a little heat) 1 small onion (sliced) Instructions: Heat the olive oil in a skillet over medium heat.
Read the full answer
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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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-4.1 Mini737 words
Certainly! Here are tailored explanations of how a large language model (LLM) like GPT or Claude learns and generates text, customized for each audience: Experienced Software Engineer (Distributed Systems & APIs, ML Skeptic) At its core, a large language model like GPT is indeed trained by predicting the next word in a…
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.
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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-4.1 Mini1,033 words
Certainly! Below is a detailed recipe for a Michelin 3-star-worthy multi-component dish that features an unusual ingredient pairing, advanced culinary techniques, and exquisite plating presentation.
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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.
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Historical Counterfactual Analysis

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

GPT-4.1 Mini1,034 words
This is a fascinating alternate history scenario. If the transistor had been invented in 1920 instead of 1947, the ripple effects across technology, economy, and geopolitics through 1980 would be profound and multifaceted.
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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.
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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…

GPT-4.1 Mini766 words
This is a complex, high-stakes crisis requiring rapid, transparent, and carefully coordinated action to balance legal, ethical, clinical, financial, and reputational risks.
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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.
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Advanced Investment Memo (IC Memo)

You are writing an internal investment committee memo for a long/short equity fund. Company (fictional): LedgerLift (ticker: LLLT) – B2B…

GPT-4.1 Mini1,036 words
Internal Investment Committee Memo: LedgerLift (Ticker: LLLT) Date: [Insert Date] Analyst: [Your Name] Recommendation & Price Target Recommendation: Long 12-Month Price Target Range: $52 – $62 per share Thesis: LedgerLift is a leading SaaS player in B2B spend management and AP automation, serving mid-market enterprises…
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Mercury 2.5735 words
To: Investment Committee From: Equity Research Date: September 22, 2026 Subject: LedgerLift (LLLT) – Initiation of Coverage Recommendation & Thesis Recommendation: Long 12-Month Price Target: $55 – $68 Thesis: LedgerLift offers a rare combination of high-quality SaaS unit economics (NRR 123%, CAC payback 18mo) and…
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Our Verdict
GPT-4.1 Mini
GPT-4.1 Mini
Mercury 2.5
Mercury 2.5

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

Mercury 2.5 costs 11x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

GPT-4.1 Mini
Input
$0.40
Output
$1.60
Mercury 2.5
Input
$0.04
10× cheaper
Output
$0.15
11× cheaper

Mercury 2.5 is cheaper on both: 10× input, 11× output.

Where to run it

3 hosts

GPT-4.1 Mini2 hosts
HostInOutContextUptime
Azure AI Foundry$0.40 in·$1.60 out·1M·99.9% upOpenAI$0.40 in·$1.60 out·1M·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 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

GPT-4.1 Mini is developed by OpenAI while Mercury 2.5 is developed by Inception. GPT-4.1 Mini has a 1.0M 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-4.1 Mini 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-4.1 Mini costs $0.4/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $0.36/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-4.1 Mini 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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