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  5. Mercury 2.5vsQwen3 30B A3B
Updated Sep 2026

Mercury 2.5vsQwen3 30B A3B

Qwen3 30B A3B is cheaper than Mercury 2.5 at $0/M vs $0.04/M input tokens.

Mercury 2.5 and Qwen3 30B A3B compared across 20 shared prompts
SpecMercury 2.5Qwen3 30B A3B
Input price$0.04/M tokensFree
Output price$0.15/M tokensFree
Context window260K tokens41K tokens
Weights—Open
Free API (OpenRouter)NoNo
ReleasedSep 2026Apr 2025
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…

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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Qwen3 30B A3B141 words
Recipe Name: Cinnamon Sugar Crusted Banana Bread Ingredients: 2 ripe bananas (mashed) 1/3 cup butter (softened) 1/2 cup sugar 1 egg 1 1/2 cups all-purpose flour 1/4 cup cocoa powder 1 tsp ground cinnamon Instructions: Preheat oven to 350°F (175°C). Grease a 8x4-inch loaf pan. In a bowl, mash bananas.
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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…

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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Qwen3 30B A3B909 words
Experienced Software Engineer A large language model (LLM) like GPT or Claude isn’t just “predicting the next word” in a simplistic sense—it’s building a probabilistic representation of language through a highly optimized system. Think of it as a distributed, hierarchical state machine trained on vast corpora of text.
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Mini LBO Underwrite

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

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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Qwen3 30B A3B338 words
1. Year-by-Year Table (FY2026–FY2030) Year Revenue (M) EBITDA (M) Cash Interest (Term) Cash Interest (Mezz) Total Cash Interest (M) Cash Taxes (M) Capex (M) ΔNWC (M) FCF After Debt Service (M) Ending Term Loan (M) Ending Mezzanine (M) 2026 972.00 136.08 43.20 21.60 64.80 17.82 29.16 0.36 19.14 456.06 183.60 2027…
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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…

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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Qwen3 30B A3B715 words
Internal Investment Committee Memo Subject: LedgerLift (LLL T) – Long/Short Recommendation Date: [Insert Date] 1. Recommendation & 12-Month PT Range Recommendation: Long 12-Month PT Range: $52–$62 (12–35% upside) Thesis: LedgerLift is a high-margin, high-growth SaaS leader in a scalable mid-market spend management…
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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…

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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Qwen3 30B A3B857 words
Hour-by-Hour Action Plan for the CEO (Next 48 Hours) Objective: Balance ethical obligations, legal risks, financial stability, and regulatory compliance while preparing for the board meeting and earnings call.
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Advanced Longevity Plan (Biohacker)

Develop a comprehensive, cutting-edge 3-month longevity plan for a biohacker aiming to optimize longevity, physical and cognitive…

Mercury 2.5610 words
3-Month Biohacking Longevity & Performance Plan Disclaimer: I am an AI, not a physician or certified health professional. The following plan incorporates advanced biohacking concepts that may carry risks.
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Qwen3 30B A3B915 words
3-Month Longevity & Performance Optimization Plan for a Biohacker Designed to maximize cellular health, cognitive function, physical resilience, and stress adaptation through cutting-edge science and personalized strategies.
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Our Verdict
Mercury 2.5
Mercury 2.5
Qwen3 30B A3B
Qwen3 30B A3BRunner-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

Mercury 2.5
Input
$0.04
Output
$0.15
Qwen3 30B A3B
Input
$0.000
Output
$0.000
Where to run it

3 hosts, cheapest first

Mercury 2.51 host
HostInOutContextUptime
Inception$0.04 in·$0.15 out·260k·100% up
Qwen3 30B A3B2 hosts
HostInOutContextUptime
DDeepInfrafp8$0.12 in·$0.50 out·41k·100% upAlibaba Cloud$0.13 in·$0.52 out·131k·99.9% up

Per million tokens. Prices and uptime via OpenRouter, checked 23 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Mercury 2.5 is developed by Inception while Qwen3 30B A3B is developed by Qwen. Mercury 2.5 has a 260K token context window vs Qwen3 30B A3B's 41K. You can compare their actual outputs across 20 challenges on Rival to see how they differ in practice.

It depends on your use case. Mercury 2.5 and Qwen3 30B A3B 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.

Mercury 2.5 costs $0.04/M input tokens and Qwen3 30B A3B costs $0/M input tokens. Qwen3 30B A3B 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 Mercury 2.5 and Qwen3 30B A3B 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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