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  5. Mercury 2.5vsMuse Spark 1.3 Contributor
Updated Sep 2026

Mercury 2.5vsMuse Spark 1.3 Contributor

Mercury 2.5 is cheaper than Muse Spark 1.3 Contributor at $0.04/M vs $0.1/M input tokens.

Mercury 2.5 and Muse Spark 1.3 Contributor compared across 20 shared prompts
SpecMercury 2.5Muse Spark 1.3 Contributor
Input price$0.04/M tokens$0.1/M tokens
Output price$0.15/M tokens$0.2/M tokens
Context window260K tokens1.0M tokens
Weights—Closed
Free API (OpenRouter)NoNo
ReleasedSep 2026Sep 2026
Side by side

Same prompt, both answers

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

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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Muse Spark 1.3 Contributor701 words
LedgerLift (LLLT) – Short Internal IC Memo Price $46 Mkt Cap $8.74B (190m shares) EV $7.34B (Net Cash $1.4B) FY25 Rev $820m, EBIT 18% 1. Recommendation: SHORT. 12-mo PT $28, range $17 (Bear) – $42 (Bull). Base DCF $27.9 (-39%), Bull $42.4 (-8%), Bear $17.3 (-62%). Even flawless Bull execution does not support $46.
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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…

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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Muse Spark 1.3 Contributor2,344 words
If the transistor arrives in 1920 instead of 1947, you don't just get the iPhone 27 years early. You change WWII, you change who wins the peace, and you get the Information Age before television has even matured. Here’s the most plausible trace, with the caveat that invention is not production.
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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…

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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Muse Spark 1.3 Contributor1,526 words
Shiretoko Dawn — Kombu-Cured Hokkaido Scallop, Fermented Porcini, Yuzu, Oscietra Caviar Butter-poached scallop, roasted porcini & toasted hay consommé, yuzu-buttermilk pearls, seaweed brown-butter, porcini soil, coral tuile Serves 4 as a tasting-course Autumn The Concept / Narrative On the Shiretoko Peninsula in…
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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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Muse Spark 1.3 Contributor1,006 words
1. For the experienced software engineer Think of training not as writing business logic, but as building a lossy compression of the internet into a queryable API. Raw text is first serialized into tokens — roughly 3/4 of a word chunks, the way you'd chunk a byte stream for protobuf.
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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.…

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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Muse Spark 1.3 Contributor969 words
These are classic red flags for BCI - here are the 3 claims that will kill your credibility fastest with a technical investor, and how to fix them: Weakest: "94% accuracy. Works with any language, any device." Why it's weak: This contradicts physics and the state-of-the-art, and it's undefined.
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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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Muse Spark 1.3 Contributor1,634 words
This contract is extremely one-sided. Every clause contains language that could be exploited to get unpaid work, withhold pay, seize your own IP, or leave you with unlimited liability. Disclaimer: This is general legal information, not legal advice and not an attorney-client relationship.
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Our Verdict
Muse Spark 1.3 Contributor
Muse Spark 1.3 Contributor
Mercury 2.5
Mercury 2.5Runner-up

Not enough votes to call it. On the specs, Muse Spark 1.3 Contributor has the edge: bigger context window, major provider backing.

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
2.5× cheaper
Output
$0.15
1.3× cheaper
Muse Spark 1.3 Contributor
Input
$0.10
Output
$0.20

Mercury 2.5 is cheaper on both: 2.5× input, 1.3× output.

Where to run it

2 hosts

Mercury 2.51 host
HostInOutContextUptime
Inception$0.04 in·$0.15 out·260k·100% up
Muse Spark 1.3 Contributor1 host
HostInOutContextUptime
Meta$0.10 in·$0.20 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 22 Sep 2026.

Research

What we learned reading every model

FAQ

Common questions

Mercury 2.5 is developed by Inception while Muse Spark 1.3 Contributor is developed by Meta AI. Mercury 2.5 has a 260K token context window vs Muse Spark 1.3 Contributor's 1.0M. 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 Muse Spark 1.3 Contributor 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 Muse Spark 1.3 Contributor costs $0.1/M input tokens. Mercury 2.5 is $0.06/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 Muse Spark 1.3 Contributor 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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