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  5. Kimi K3vsMercury 2.5
Updated Sep 2026

Kimi K3vsMercury 2.5

Mercury 2.5 is cheaper than Kimi K3 at $0.04/M vs $3/M input tokens.

Kimi K3 and Mercury 2.5 compared across 20 shared prompts
SpecKimi K3Mercury 2.5
Input price$3/M tokens$0.04/M tokens
Output price$15/M tokens$0.15/M tokens
Context window1.0M tokens260K tokens
WeightsOpen—
Free API (OpenRouter)NoNo
ReleasedJul 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) –…

Kimi K3669 words
HarborTech Services LBO — IC Underwriting Conventions: interest on beginning-of-year balances; mandatory TL amort = 1% of original principal ($4.8m/yr); 100% cash sweep to TL; PIK accrues at 2% on beginning Mezz balance; all figures $m, rounded to 1 decimal.
Read the full answer
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…
Read the full answer

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…

Kimi K31,055 words
IC MEMO — LedgerLift (LLLT) Price $46 Mkt cap $8.74B Net cash $1.4B EV $7.34B EV/FY25 Rev 9.0x EV/FY26E Rev 7.4x EV/FY26E EBIT 37x Recommendation: SHORT (moderate conviction; 1.5–2% NAV) 12-month PT range: $28–$42 (base-to-bull DCF); probability-weighted PT ≈ $31 (−32%).
Read the full answer
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…
Read the full answer

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 K31,479 words
The Software Engineer An LLM is, mechanically, just a function: a giant composition of matrix multiplications and nonlinearities that maps a sequence of tokens (chunks of text, roughly ¾ of a word each) to a probability distribution over what token comes next.
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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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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 K31,229 words
FIRST FROST Langoustine · Frost-Sweetened Parsnip · Coffee · Vanilla · Horseradish Snow The Narrative This dish is built on a piece of winter biochemistry. When the first frost strikes, parsnips and horseradish defend themselves by converting starch into sugar — a natural antifreeze — which is why country cooks have…
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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…

Kimi K32,513 words
First, a caveat that shapes everything A 1920 transistor is a bigger counterfactual than it appears. The 1947 invention waited on quantum mechanics (band theory, 1928–31), ultra-pure germanium, and controlled doping.
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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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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.…

Kimi K3785 words
The 3 Weakest Claims in MindMeld AI's Pitch "94% accuracy decoding neural patterns into text... any language" (Slide 3) Why it's weak: This claim exceeds the published state of the art — including invasive systems — by a wide margin.
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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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Our Verdict
Kimi K3
Kimi K3
Mercury 2.5
Mercury 2.5Runner-up

Not enough votes to call it. On the specs, Kimi K3 has the edge: bigger model tier, bigger context window.

Mercury 2.5 costs 100x less per token.

Too close to call

Reviewing agent-written code?See a Brief PR report

API pricing

Cost per 1M tokens

Kimi K3
Input
$3.00
Output
$15.00
Mercury 2.5
Input
$0.04
75× cheaper
Output
$0.15
100× cheaper

Mercury 2.5 is cheaper on both: 75× input, 100× output.

Where to run it

18 hosts, cheapest first

Kimi K317 hosts
HostInOutContextUptime
SSail Researchfp4$1.05 in·$13.00 out·1M·99.9% upIInferenceNetfp4$1.95 in·$9.75 out·1M·98.4% upPPhala$1.95 in·$9.75 out·1M·98.5% upRRelacefp4$1.95 in·$9.75 out·1M·99.3% upWWafer$2.49 in·$10.95 out·1M·99.9% upMMorph$2.50 in·$14.00 out·1M·98.8% up
11 more hostsFewer hosts
MMakora$2.55 in·$12.75 out·1M·97.2% upDDigitalOcean$2.55 in·$12.95 out·1M·99.5% upDDeepInfrabf16$2.85 in·$14.25 out·1M·92.1% upBBasetenfp8$3.00 in·$15.00 out·1M·98.7% upCChutesmxfp4$3.00 in·$15.00 out·1M·98.1% upFFireworks$3.00 in·$15.00 out·1M·100% upModalmxfp4$3.00 in·$15.00 out·1M·99.5% upMoonshot AImxfp4$3.00 in·$15.00 out·1M·99.9% upPParasailfp4$3.00 in·$15.00 out·1M·98.5% upTTogether$3.00 in·$15.00 out·1M·99.5% upAlibaba Cloud$3.45 in·$17.25 out·1M·97.5% 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

Kimi K3 is developed by Moonshot AI while Mercury 2.5 is developed by Inception. Kimi K3 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. Kimi K3 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 K3 costs $3/M input tokens and Mercury 2.5 costs $0.04/M input tokens. Mercury 2.5 is $2.96/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 K3 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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Model pages

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