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  5. Aurora AlphavsMercury 2.5
Updated Sep 2026

Aurora AlphavsMercury 2.5

Aurora Alpha is cheaper than Mercury 2.5 at $0/M vs $0.04/M input tokens.

Aurora Alpha and Mercury 2.5 compared across 20 shared prompts
SpecAurora AlphaMercury 2.5
Input priceFree$0.04/M tokens
Output priceFree$0.15/M tokens
Context window128K tokens260K tokens
Free API (OpenRouter)NoNo
ReleasedFeb 2026Sep 2026
Side by side

Same prompt, both answers

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

Mini LBO Underwrite

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

Aurora Alpha836 words
HarborTech Services (HTS) – 5‑yr LBO (FY2026‑FY2030) All figures are $ m (rounded to two decimals). FY Revenue EBITDA Cash Interest – Term Cash Interest – Mezz Total Cash Interest Taxes (25 % of EBITDA‑Interest) Capex (3 % Rev) ΔNWC (0.5 % of ΔRev) Free‑Cash‑Flow (before amort.) Mandatory Amort.
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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…
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Adversarial Contract Review

Here is a contract for a software consulting engagement: --- SOFTWARE CONSULTING AGREEMENT This Agreement is entered into between TechCorp…

Aurora Alpha1,985 words
Important Disclaimer I am not a lawyer, and the following material is for general informational purposes only. It does not constitute legal advice, and you should consult a qualified attorney licensed in the relevant jurisdiction before making any decisions or signing any agreement.
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Mercury 2.5501 words
Here is an analysis of the contract clauses that pose significant risk to the contractor, along with suggested modifications and legal reasoning. Scope and Compensation (Clause 1) Risk: The client can expand the project indefinitely without paying extra.
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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…

Aurora Alpha399 words
Dish Name: Spiced Honey‑Garlic Chickpea Crunch Servings: 2‑3 (great as a snack, side, or light main) Estimated Time: Prep ≈ 5 min   Cook ≈ 10 min   Total ≈ 15 min Ingredients (7 pantry staples) # Ingredient Amount 1 Canned chickpeas (drained & rinsed) 1 ½ cups (≈ 1 can) 2 Olive oil (or any neutral oil) 2 Tbsp 3 Honey…
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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…

Aurora Alpha1,472 words
“Eclipse of Sea & Forest” A 3‑star‑level, multi‑component tasting piece that marries the briny depth of the ocean with the earthy perfume of the forest, using a handful of daring pairings and a suite of modernist techniques.
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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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Startup Pitch Teardown

Here's a pitch deck summary for a startup called "MindMeld AI": **Slide 1 - Vision:** "We're building the future of human-AI collaboration.…

Aurora Alpha1,026 words
Below is a quick “weak‑claim audit” of the MindMeld AI deck, followed by concrete ways to make each point more credible and compelling for investors. 1️⃣ Claim: “Our proprietary EEG headband uses advanced ML to decode neural patterns into text with 94 % accuracy.”
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Mercury 2.5544 words
Based on the pitch deck summary, here are the three weakest claims, along with an analysis of their vulnerabilities and concrete suggestions for improvement. The "Pre-Thought" Prediction Claim Claim: "MindMeld AI reads your brainwaves to predict what you want to type before you think it."
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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…

Aurora Alpha718 words
1. Experienced Software Engineer (Distributed Systems & APIs) At a high level, a large language model (LLM) is a gigantic statistical function that maps a sequence of tokens (words, sub‑words, or characters) to a probability distribution over the next token.
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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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Our Verdict
Mercury 2.5
Mercury 2.5
Aurora Alpha
Aurora AlphaRunner-up

Not enough votes to call it. On the specs, Mercury 2.5 has the edge: newer, bigger context window.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Aurora Alpha
Input
$0.000
Output
$0.000
Mercury 2.5
Input
$0.04
Output
$0.15
Where to run it

1 host

Aurora Alpha

No hosts listed on OpenRouter.

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

Aurora Alpha is developed by OpenRouter while Mercury 2.5 is developed by Inception. Aurora Alpha has a 128K 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. Aurora Alpha 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.

Aurora Alpha costs $0/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Aurora Alpha is $0.04/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 Aurora Alpha 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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