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  5. Gemini 3.7 FlashvsMercury 2.5
Updated Sep 2026

Gemini 3.7 FlashvsMercury 2.5

Mercury 2.5 is cheaper than Gemini 3.7 Flash at $0.04/M vs $0.375/M input tokens.

Gemini 3.7 Flash and Mercury 2.5 compared across 20 shared prompts
SpecGemini 3.7 FlashMercury 2.5
Input price$0.375/M tokens$0.04/M tokens
Output price$1.875/M tokens$0.15/M tokens
Context window1.0M tokens260K tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedAug 2026Sep 2026
Side by side

Same prompt, both answers

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

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…

Gemini 3.7 Flash844 words
To the Experienced Software Engineer At its core, a Large Language Model is not a sentient entity; it is a compiled, highly optimized functional pipeline executing a deterministic set of matrix multiplications over a sequence of integers (tokens). Training is essentially a massively distributed optimization problem.
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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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Historical Counterfactual Analysis

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

Gemini 3.7 Flash1,203 words
If the transistor had been invented in 1920—likely as an empirical discovery in solid-state physics using point-contact crystals (similar to Julius Lilienfeld’s real-world 1925 theoretical patents, but made fully functional)—it would have compressed modern technological history by nearly three decades.
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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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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…

Gemini 3.7 Flash935 words
MEMORANDUM TO: Investment Committee FROM: TMT Sector Team DATE: October 24, 2025 SUBJECT: Investment Recommendation: LedgerLift (NYSE: LLLT) Executive Summary & Recommendation Recommendation: SHORT (High conviction) Current Price: $46.00 Market Cap: $8.74B Enterprise Value: $7.34B (190m shares, $1.4B net cash) 12-Month…
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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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Mini LBO Underwrite

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

Gemini 3.7 Flash601 words
Here is the 5-year LBO underwriting analysis for HarborTech Services (HTS). Entry & Sources / Uses ($ millions) FY25 Revenue: $900.0M FY25 EBITDA: $120.0M Enterprise Value (12.0x): $1,440.0M Transaction Fees (2.0%): $28.8M Total Uses: $1,468.8M Sources Multiple Amount ($M) % Total Term Loan (9.0% cash, 1.0% amort) 4.0x…
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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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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…

Gemini 3.7 Flash958 words
L’Ombre et L’Abîme (Shadow & The Abyss) Hay-Aged Bresse Squab, Hokkaido Sea Urchin Emulsion, Black Garlic–Koji Glaze, Cocoa Nib & Cuttlefish Ink Tuile, Forest Dashi I. Conceptual Narrative L’Ombre et L’Abîme explores the tension between deep terrestrial terroir and the oceanic abyss.
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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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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…

Gemini 3.7 Flash335 words
Dish Name: Midnight Garlic-Butter Silk Noodles A rich, savory, and slightly sweet bowl of glossy pasta tossed in caramelized garlic-butter and soy glaze, crowned with a crispy-edged, runny-yolk egg.
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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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Our Verdict
Gemini 3.7 Flash
Gemini 3.7 Flash
Mercury 2.5
Mercury 2.5Runner-up

Not enough votes to call it. On the specs, Gemini 3.7 Flash has the edge: bigger context window, major provider backing.

Mercury 2.5 costs 13x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Gemini 3.7 Flash
Input
$0.38
Output
$1.88
Mercury 2.5
Input
$0.04
9.4× cheaper
Output
$0.15
13× cheaper

Mercury 2.5 is cheaper on both: 9.4× input, 13× output.

Where to run it

3 hosts

Gemini 3.7 Flash2 hosts
HostInOutContextUptime
Google Vertex AI$0.75 in·$3.75 out·1M·99.5% upGoogle AI Studio$0.75 in·$3.75 out·1M·99.8% 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

Gemini 3.7 Flash is developed by Google AI while Mercury 2.5 is developed by Inception. Gemini 3.7 Flash 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. Gemini 3.7 Flash 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.

Gemini 3.7 Flash costs $0.375/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $0.34/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 Gemini 3.7 Flash 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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