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  5. Mercury 2.5vsQwen3.8 27B
Updated Sep 2026

Mercury 2.5vsQwen3.8 27B

Mercury 2.5 is cheaper than Qwen3.8 27B at $0.04/M vs $0.4/M input tokens.

Mercury 2.5 and Qwen3.8 27B compared across 20 shared prompts
SpecMercury 2.5Qwen3.8 27B
Input price$0.04/M tokens$0.4/M tokens
Output price$0.15/M tokens$3/M tokens
Context window260K tokens262K tokens
Weights—Open
Free API (OpenRouter)NoYes (1 provider)
ReleasedSep 2026Aug 2026
Side by side

Same prompt, both answers

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

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…

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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Qwen3.8 27B3,174 words
Tidal Cellar Koji-Cured Scallop, Oyster, Black Garlic, 24-Month Parmigiano, Apple, and Sea Fennel Menu line: Koji-cured sea scallop, oyster-apple gel, charred celeriac, black garlic and 24-month Parmigiano velouté, apple-hazelnut shard, sea fennel–chive oil, black garlic–oak ash.
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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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Qwen3.8 27B837 words
Fictional internal memo; not investment advice. Recommendation: Short (moderate conviction) 12-month PT range: $30–$44 Thesis: LLLT is a high-quality, cash-rich spend-management SaaS business, but at $46 it embeds a peer EBIT multiple that its 20% FY26E EBIT margin and conservative DCF do not support.
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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…

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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Qwen3.8 27B3,682 words
This is a general contractor-side risk review, not legal advice. The agreement is heavily one-sided and should not be signed as drafted. Many provisions may be negotiable, and some may be unenforceable depending on jurisdiction, but you should not rely on unenforceability as your main protection.
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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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Qwen3.8 27B692 words
All figures in $m unless noted. Assumptions: 12.0x entry multiple is EV/EBITDA. Interest is calculated on beginning-of-year debt balances. Mandatory Term Loan amortization = 1% of original Term Loan = $4.8m/yr. Mezz PIK = 2% of beginning Mezz balance, compounded into principal.
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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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Qwen3.8 27B1,144 words
For an experienced software engineer Think of a large language model as a stateless inference service plus an enormous offline training pipeline. At inference time, the model takes a prompt, converts it into tokens, and produces a probability distribution over the next token.
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Debug This Architecture

Here's the architecture for a real-time collaborative document editor (like Google Docs): **System Overview:** - Frontend: React SPA with…

Mercury 2.5672 words
Critical Analysis of Collaborative Editor Architecture Below is a detailed breakdown of failure modes, race conditions, and scaling bottlenecks, along with proposed solutions and their trade-offs.
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Qwen3.8 27B4,268 words
Below is a comprehensive issue register based on the architecture you described. I’m assuming: “change event” can be a small edit, possibly per keystroke or per paragraph. Documents are stored as HTML blobs. Each API server owns only its own WebSocket clients.
Read the full answer
Our Verdict
Mercury 2.5
Mercury 2.5
Qwen3.8 27B
Qwen3.8 27BRunner-up

Not enough votes to call it. On the specs, Mercury 2.5 has the edge: bigger model tier.

Mercury 2.5 costs 20x less per token.

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
10× cheaper
Output
$0.15
20× cheaper
Qwen3.8 27B
Input
$0.40
Output
$3.00

Mercury 2.5 is cheaper on both: 10× input, 20× output.

Where to run it

17 hosts, cheapest first

Mercury 2.51 host
HostInOutContextUptime
Inception$0.04 in·$0.15 out·260k·100% up
Qwen3.8 27B16 hosts
HostInOutContextUptime
DDarkbloomfp4$0.10 in·$1.80 out·262k·99% upDDekaLLM$0.10 in·$2.50 out·262k·99.7% upWWafer$0.11 in·$2.50 out·262k·99.9% upRRekafp8$0.12 in·$2.48 out·262k·99.9% upDDeepInfrabf16$0.15 in·$1.88 out·262k·97% upPPhala$0.20 in·$2.08 out·262k·98% up
10 more hostsFewer hosts
MMancerfp8$0.20 in·$2.50 out·262k·99.8% upCChutesfp8$0.24 in·$2.20 out·262k·99.4% upPParasailfp8$0.24 in·$2.20 out·262k·99.9% upAAkashMLfp8$0.25 in·$2.20 out·262k·100% upIIonstreamfp8$0.28 in·$2.55 out·262k·97.9% upCCoreWeavefp8$0.40 in·$3.00 out·262k·99.5% upNNovita$0.42 in·$3.00 out·1M·99.9% upAlibaba Cloud$0.42 in·$2.55 out·1M·100% upCloudflare Workers AI$0.45 in·$3.20 out·262k·91.7% upVVenicefp8$0.45 in·$3.20 out·262k·97.8% 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 Qwen3.8 27B is developed by Qwen. Mercury 2.5 has a 260K token context window vs Qwen3.8 27B's 262K. 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 Qwen3.8 27B 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 Qwen3.8 27B costs $0.4/M input tokens. Mercury 2.5 is $0.36/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 Qwen3.8 27B 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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