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  5. Kimi K2.7 CodevsMercury 2.5
Updated Sep 2026

Kimi K2.7 CodevsMercury 2.5

Mercury 2.5 is cheaper than Kimi K2.7 Code at $0.04/M vs $0.95/M input tokens.

Kimi K2.7 Code and Mercury 2.5 compared across 20 shared prompts
SpecKimi K2.7 CodeMercury 2.5
Input price$0.95/M tokens$0.04/M tokens
Output price$4/M tokens$0.15/M tokens
Context window262K tokens260K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedJun 2026Sep 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…

Kimi K2.7 Code218 words
Sunshine Chickpea & Rice Pilaf A cozy, one-pot pantry meal that’s earthy, lightly spiced, and satisfying. Ingredients (7 pantry staples) 1 can (15 oz) chickpeas, drained and rinsed 1 can (14.5 oz) diced tomatoes, with juices 1 cup long-grain white rice 1 small yellow onion, diced 2 cloves garlic, minced 2 tbsp olive…
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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…

Kimi K2.7 Code1,291 words
Aurora over the Boreal Forest Arctic Char • Birch-Sap & Horseradish Meringue • Fermented Sea Buckthorn • Black Garlic–Parsnip Soil • Dill Ash • Rye-Malt Tuile A dish designed for a Michelin 3-star tasting menu: technically demanding, visually striking, and built around an unexpected Nordic flavor story—silky cold-water…
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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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Mini LBO Underwrite

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

Kimi K2.7 Code495 words
All figures in $m. Assumptions: 1% Term Loan amortization = 1% of original $480m principal ($4.8m/year); “FCF after debt service” = EBITDA – cash interest – cash taxes – capex – ΔNWC – mandatory amortization; all of that FCF is used for optional Term Loan paydown.
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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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Historical Counterfactual Analysis

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

Kimi K2.7 Code1,566 words
Counterfactual: The Transistor Invented in 1920 Core assumption: A practical solid-state amplifying/switching device—the transistor—is demonstrated in a major industrial lab around 1920–1922, is patented by 1925, and is in modest mass production by the early 1930s.
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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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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…

Kimi K2.7 Code880 words
For the experienced software engineer You can think of a large language model as a distributed compression engine that has been forced to become a pattern-matching oracle. During training, it is shown trillions of tokens of text and is optimized for one task: predict the next token given all previous tokens.
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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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Adversarial Contract Review

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

Kimi K2.7 Code2,205 words
Disclaimer: This is a general educational analysis of contract terms, not legal advice. Enforceability varies by jurisdiction, and a contractor should have a qualified attorney review and negotiate any agreement before signing.
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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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Our Verdict
Kimi K2.7 Code
Kimi K2.7 Code
Mercury 2.5
Mercury 2.5

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

Mercury 2.5 costs 27x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Kimi K2.7 Code
Input
$0.95
Output
$4.00
Mercury 2.5
Input
$0.04
24× cheaper
Output
$0.15
27× cheaper

Mercury 2.5 is cheaper on both: 24× input, 27× output.

Where to run it

15 hosts, cheapest first

Kimi K2.7 Code14 hosts
HostInOutContextUptime
DDeepInfrafp4$0.68 in·$3.40 out·262k—IInceptronint4$0.71 in·$3.30 out·262k·99.8% upCCoreWeaveint4$0.71 in·$3.50 out·262k·99.9% upSStreamLake$0.71 in·$3.00 out·256k·99.8% upVVeniceint4$0.75 in·$3.50 out·256k·95.5% upMModelRunfp4$0.85 in·$3.75 out·262k·100% up
8 more hostsFewer hosts
SSiliconFlowfp8$0.86 in·$3.80 out·262k·99.9% upNNovitaint4$0.91 in·$3.84 out·262k·100% upAlibaba Cloudfp8$0.95 in·$4.00 out·262k·99.9% upBBasetenfp4$0.95 in·$4.00 out·262k·99.9% upCloudflare Workers AI$0.95 in·$4.00 out·262k·100% upFFireworks$0.95 in·$4.00 out·262k·95.2% upGGMI Cloudfp8$0.95 in·$4.00 out·262k·99.9% upMoonshot AIint4$0.95 in·$4.00 out·262k·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

Kimi K2.7 Code is developed by Moonshot AI while Mercury 2.5 is developed by Inception. Kimi K2.7 Code has a 262K 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. Kimi K2.7 Code 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.

Kimi K2.7 Code costs $0.95/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $0.91/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 Kimi K2.7 Code 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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