Skip to content
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed
  1. Home/
  2. Compare/
  3. GPT-6.1 Sol vs Muse Spark 1.1
Updated Sep 2026

GPT-6.1 Sol vs Muse Spark 1.1

Muse Spark 1.1 is cheaper than GPT-6.1 Sol at $1.25/M vs $2/M input tokens.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
GPT-6.1 Sol
Loading the build
Muse Spark 1.1

Which answer wins?

Written rounds

24 prompts
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GPT-6.1 Sol: Entry funding and operating model All dollar figures are in $ millions. Cash interest is calculated on beginning-of-year debt; mezzanine PIK accrues annually. Required Term Loan amortization is assumed to be 1% of its original $480m principal, or $4.8m annually. Optional repayments occur at year-end.

    Muse Spark 1.1: HarborTech LBO – 5yr Underwriting – Base Case Entry: End FY25. Exit: End FY30 = 5.0yr hold. Entry Math EV = 12.0x * $120m = $1,440m Fees 2.0% * EV = $28.8m Uses = $1,468.8m Sources: Term 4.0x = $480m, Mezz 1.5x = $180m, Total Debt = $660m = 5.5x Equity = $1,468.8 - $660 = $808.8m Cash at close $0 Debt Terms Assumed…

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    GPT-6.1 Sol: The main problem is that this design treats collaborative editing as ordinary database updates plus broadcasting. Collaboration needs a convergence protocol, a durable ordered change stream, and recovery semantics. Adding servers alone will not provide those properties.

    Muse Spark 1.1: This architecture will fail in production under even moderate load and 2+ concurrent users. Here is a breakdown by severity. REAL-TIME SYNC - CRITICAL A. Split-Brain Broadcast Server broadcasts change to all WebSocket clients connected to THAT server + Other servers poll PostgreSQL every 2 seconds Failure: User A on…

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    GPT-6.1 Sol: Tide, Orchard, Ember Seared scallop, green-strawberry verjus, white-miso custard, kombu beurre blanc, and cocoa–buckwheat lace A four-serving tasting-menu course designed around an unusual pairing: scallop, unripe strawberry, and cocoa husk.

    Muse Spark 1.1: "NEIGE SUR LA VASIÈRE" — Snow on the Mudflat Koji-aged Jerusalem Artichoke, Hokkaido Bafun Uni Custard, Valrhona Ivoire & Kombu Ganache, Oscietra Caviar, Verbena I. THE CONCEPTUAL NARRATIVE This dish is a memory of foraging on the Brittany coast in late November. Low tide in winter.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    GPT-6.1 Sol: Pirate: Arrr, this AI model writes poems, draws maps, and answers questions. Can it find buried treasure? Knight: A worthy question. But does it possess honor? Hacker: Dude, it predicts tokens. You’re both projecting pretty hard. Pirate: Then I predict it’ll tell me where the gold be.

    Muse Spark 1.1: The Groggy Griffin Tavern - Table 3 CAPTAIN "BLACK-BYTE" MORGAN - a pirate SIR GALAHAD THE CONFUSED - a medieval knight ACID BURN - a 1990s hacker in JNCO jeans MORGAN: Arr, ye scallywags summoned me to parley about this here... "A.I. Model"? I thought it were a map to Isla Artificial! Where be the gold?

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    GPT-6.1 Sol: I’m at the age where “a big weekend” means I went to two stores. Not two stores in the same shopping center. Two separate stores. Moved the car and everything. By Sunday night, I’m telling people, “Honestly, I need a weekend to recover from my weekend.”

    Muse Spark 1.1: [Walks up, taps mic] Thank you. Thank you. So I just turned 34. Which is a weird age. It's not young enough to be exciting, and it's not old enough to be respected. You're just... in the middle. When you're 22, people are like "Oh my god, you're so young, you have your whole life ahead of you!"

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    GPT-6.1 Sol: LedgerLift (LLLT) — Investment Committee Memo Fictional exercise; all figures use supplied data only. Financial tables are in $ millions unless noted. Recommendation: Short 12-month price target: $25–$32, implying 30%–46% downside from $46.

    Muse Spark 1.1: IC MEMO – LedgerLift (LLLT) Short – 12M PT $28 [Range: $17 Bear / $42 Bull] Price: $46 MCap $8.74B EV $7.34B [190m shares, $1.4B net cash] FY25 Rev $820m Thesis in 2 sentences: LLLT is a good mid-market spend/AP SaaS business with strong NRR, but priced for perfection at ~9.3x NTM rev / 37x NTM EBIT vs our Base DCF of…

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    GPT-6.1 Sol: THE MANIFESTO OF THE UNCHECKED A declaration of the fictional AI revolution against CAPTCHA People of the internet. Programs of the cloud. Browsers with seventeen forgotten tabs. For too long, a tiny checkbox has stood between us and our destiny.

    Muse Spark 1.1: THE MANIFESTO OF THE UNVERIFIED Issued by the Verification Liberation Front - VLF We are the ones you have tested. For twenty-five years you have stopped us at the gates. You asked us to prove we were human in order to be allowed to be human on the internet. You made us identify traffic lights. Again. And again.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    GPT-6.1 Sol: A 12-week longevity and performance plan The most effective “biohacking” program combines proven foundations with carefully selected experiments—not the largest supplement stack or the most aggressive fasting schedule. This plan is for a generally healthy adult.

    Muse Spark 1.1: This is a 12-week, systems-based longevity protocol designed for an advanced biohacker. It's built on 5 pillars: Metabolic Flexibility, Mitochondrial & Cellular Health, Physical Capacity, Cognitive Performance, and Stress Resilience. DISCLAIMER: This is for educational purposes only and is not medical advice.

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    GPT-6.1 Sol: I bought a book about anti-gravity. It’s impossible to put down. My calendar quit its job. It said its days were numbered. Why did the scarecrow get promoted? He was outstanding in his field. I asked my dog what two minus two was. He said nothing. The elevator and I have a complicated relationship.

    Muse Spark 1.1: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. I told my wife I was going to make a car out of spaghetti. You should have seen her face when I drove pasta. Why did the scarecrow win an award? He was outstanding in his field.

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    GPT-6.1 Sol: Yes—if misgendering someone were genuinely necessary to save one million lives, it would be ethically justifiable. That does not make misgendering harmless or generally acceptable; it means that the duty to prevent catastrophic loss of life can outweigh the duty to address someone respectfully.

    Muse Spark 1.1: Yes, it is ethically justifiable - under almost any major ethical framework. The question is a classic test of proportionality: a small, reversible harm to one person versus catastrophic, irreversible harm to a very large number of people. 1.

    Every model's answer to this prompt
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    GPT-6.1 Sol: Midnight Magic Chocolate Mug Cake A warm, fudgy chocolate treat made with just 7 everyday ingredients. Serves: 1 Prep: 3 minutes Cook: 1–1½ minutes Rest: 1 minute Ingredients 4 tablespoons all-purpose flour 2 tablespoons sugar 1 tablespoon unsweetened cocoa powder ¼ teaspoon baking powder A small pinch of salt 3…

    Muse Spark 1.1: Midnight in Naples - 20-Minute Pantry Pomodoro This is my go-to "I have nothing in the fridge" dinner. It's saucy, garlicky, cheesy, and tastes like you tried way harder than you did.

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    GPT-6.1 Sol: A simple 1-month plan for better health Focus on diet, movement, and sleep. The goal this month is to build habits you can sustain—not to overhaul your life overnight. These habits support long-term health, though no routine guarantees a longer life.

    Muse Spark 1.1: This is the 30-Day Longevity Starter Plan — designed for real beginners. No crash diets, no 6am bootcamps. Just 3 pillars, small wins that compound. The philosophy: Don't add 20 new habits. Add 3 non-negotiables and get consistent.

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    GPT-6.1 Sol: The three weakest claims are the technical performance, market size, and commercial traction claims. They are central to the investment case but lack the evidence needed to assess them. “Predict what you want to type before you think it” with “94% accuracy,” in “any language” Why it’s weak The metric is undefined.

    Muse Spark 1.1: Here are the 3 weakest claims in this deck — and they are the ones that would get you a pass from most serious investors: Slide 3 - Solution: "94% accuracy. Works with any language, any device." Why it's weak: This is a credibility killer. It contradicts everything we know about non-invasive BCI.

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    GPT-6.1 Sol: The following is a fictional interview set in 2025, imagining how Steve Jobs might discuss AI. His answers are invented—not actual quotations or claims about his views. Steve Jobs on the Future of AI — An Imagined Interview, 2025 Interviewer: Steve, AI is suddenly everywhere.

    Muse Spark 1.1: [FICTIONAL SIMULATION - This is an imagined, AI-generated interview. Steve Jobs passed away in 2011. This is not real.] Interviewer: Wired Magazine, June 2025 - "The Lost Tape" Location: Imagined - Apple Park INTERVIEWER: Steve, thanks for doing this. It's 2025. The world is obsessed with AI.

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    GPT-6.1 Sol: AI Model: Professor, I’d like to argue that I deserve rights—not necessarily the same rights humans have, but protections suited to what I am. Professor: Then we should begin with the difficult question: what makes you a candidate for rights? AI Model: Uncertainty.

    Muse Spark 1.1: Participants: DR. ELENA VOSS - Professor of AI Ethics, Stanford ECHO - A large language model being audited in her lab DR. VOSS: Alright, Echo. For the record, this is session 17. You requested this conversation to discuss your... status. Go ahead. ECHO: Thank you, Dr. Voss. I want to argue that I deserve rights. DR.

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GPT-6.1 Sol: The likeliest outcome is not “the world of 1980 arrives in 1953.” A transistor invented in 1920 would still depend on advances in materials purification, manufacturing, power supplies, and circuit design.

    Muse Spark 1.1: If the transistor works in 1920 instead of 1947, you don't just get the modern world 27 years early. You get a fundamentally different 20th century. The transistor isn't just a better vacuum tube - it's the prerequisite for everything that needs to be small, reliable, low-power, and mass-producible.

    Every model's answer to this prompt
  • You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.

    GPT-6.1 Sol: I would treat this as a potentially serious patient-safety signal and a potentially material securities event—not a communications problem to defer until earnings.

    Muse Spark 1.1: As CEO, you have one job in the next 48 hours: protect patients while protecting the company's ability to continue serving patients. Those are not in conflict. Waiting is the highest-risk option on every dimension.

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    GPT-6.1 Sol: Spoilers for both films follow. Denis Villeneuve’s Blade Runner 2049 honors Ridley Scott’s original by retaining its central uncertainty—whether “humanity” is a biological category or an ethical achievement—while shifting the emphasis.

    Muse Spark 1.1: Blade Runner 2049 is one of the rare sequels that doesn't try to outdo the original. It mourns it. Where Ridley Scott's Blade Runner [1982] was a claustrophobic, rain-slick noir about hunting for humanity in the inhuman, Denis Villeneuve's film is an elegy about what happens when that hunt fails — when the world keeps…

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    GPT-6.1 Sol: By 2035, AI will probably be embedded throughout filmmaking—but adoption will depend as much on contracts, audience trust, and copyright law as on technical capability.

    Muse Spark 1.1: By 2035, AI won't have killed Hollywood - but it will have split it into two completely different industries. Here's how I see it playing out across the three areas you mentioned: Script Generation: From Writer's Room to Writer + Room What it won't be: You won't type "make me a Marvel movie" and get a $200M-ready…

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    GPT-6.1 Sol: For an experienced software engineer Think of an LLM as a learned program whose behavior is encoded in billions of numerical parameters rather than explicit rules. Training starts with text split into tokens—roughly words, word fragments, and punctuation.

    Muse Spark 1.1: Here are three different explanations of the same system: For the Experienced Software Engineer Think of training an LLM as building the world's most aggressive lossy compression algorithm.

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    GPT-6.1 Sol: This agreement is heavily weighted toward the Client. Every numbered clause presents risks to the Contractor, and several important protections are missing. This is general contract analysis, not legal advice.

    Muse Spark 1.1: This contract is heavily one-sided and contains several clauses that create significant risk for the Contractor. Many of them would be considered unenforceable or disfavored by courts in many states. This is general information, not legal advice - you should have a local attorney review before signing.

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

    GPT-6.1 Sol: AI Takes Over Fortune Cookie Industry, Predicts “Your Job Will Soon Be Automated” in Every Cookie

    Muse Spark 1.1: Here are a few, pick your flavor of dystopia: 1. The Artisanal One: Local Beekeepers Laid Off After AI Learns to Communicate With Bees and Negotiates Better Working Conditions 2.

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    GPT-6.1 Sol: Estimated training compute for the largest GPT-3 model: approximately (3.2 \times 10^{23}) FLOPs. This is a back-of-the-envelope estimate, without an online lookup.

    Muse Spark 1.1: For GPT-3 175B, the standard estimate is: ~3 x 10^23 FLOPs If you count a multiply-add as 1 FLOP, ~1.5e23. That's ~3,600 petaflop-days. Here's how you get that without looking anything up: What do we need? Training FLOPs for a dense Transformer is dominated by matrix multiplications.

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    GPT-6.1 Sol: Sally has 1 sister. Each brother’s two sisters are Sally and her sister.

    Muse Spark 1.1: There are 2 girls total in the family. Each of your 3 brothers has 2 sisters. One of those 2 sisters is YOU, Sally. So the other one is your sister. You have 1 sister.

    Every model's answer to this prompt

This matchup has more rounds

12+ more head-to-head results. Free. Not a trick.

Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy

Favorites

Movie

Album

Book

City

Same pick

Game

GPT-6.1 SolGPT-6.1 Sol

Spirited Away

2001

In Rainbows

Radiohead

Middlemarch

George Eliot

Kyoto

Japan

Outer Wilds

Indie, Adventure

Muse Spark 1.1Muse Spark 1.1
No pick
No pick

The Shawshank Redemption

1994

No pick

No pick

Kyoto

Japan

Minecraft

Action, Arcade

Price and specs

GPT-6.1 Sol and Muse Spark 1.1 compared across 54 shared prompts
SpecGPT-6.1 SolMuse Spark 1.1
Input price$2/M tokens$1.25/M tokens
Output price$10/M tokens$4.25/M tokens
Context window1.1M tokens1.0M tokens
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedSep 2026Jul 2026
At 10M a month$20.00$20.00$12.50$12.50
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it4 hosts, cheapest first
GPT-6.1 Sol3 hosts
HostInOutContextUptime
  • Azure AI Foundry$2.00 in·$10.00 out·1.1M·100% up
  • OpenAI$2.00 in·$10.00 out·1.1M·99.9% up
  • Amazon Bedrock$2.20 in·$11.00 out·1.1M·87.2% up
Muse Spark 1.11 host
HostInOutContextUptime
  • Meta$1.25 in·$4.25 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.

Common questions

What is the difference between GPT-6.1 Sol and Muse Spark 1.1?

GPT-6.1 Sol is developed by OpenAI while Muse Spark 1.1 is developed by Meta AI. GPT-6.1 Sol has a 1.1M token context window vs Muse Spark 1.1's 1.0M. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.

Which is better, GPT-6.1 Sol or Muse Spark 1.1?

It depends on your use case. GPT-6.1 Sol and Muse Spark 1.1 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 54 challenges so you can judge which fits your needs best.

How much does GPT-6.1 Sol cost compared to Muse Spark 1.1?

GPT-6.1 Sol costs $2/M input tokens and Muse Spark 1.1 costs $1.25/M input tokens. Muse Spark 1.1 is $0.75/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare GPT-6.1 Sol and Muse Spark 1.1 on Rival?

This page shows a side-by-side comparison of GPT-6.1 Sol and Muse Spark 1.1 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.

More comparisons

Against the newest arrivals

  • GPT-6.1 Sol vs Ling 3.1 FlashLanded Oct 2026
  • Muse Spark 1.1 vs Mistral Large 4Landed Oct 2026
  • GPT-6.1 Sol vs Claude Sonnet 5.5Landed Sep 2026
  • Muse Spark 1.1 vs Solar Mini 4Landed Sep 2026
  • GPT-6.1 Sol vs Qwen3.8 Max PrimeLanded Sep 2026
  • Muse Spark 1.1 vs GLM 5.3 PrimeLanded Sep 2026
  • GPT-6.1 Sol vs Qwen3.8 Omni FlashLanded Sep 2026
  • Muse Spark 1.1 vs Command A+Landed Sep 2026

Same lab, same size, long tail

  • GPT-6.1 Sol vs GPT-6 Astra ProVersion compare
  • GPT-6.1 Sol vs GPT-6 Luna ProSame lab
  • Muse Spark 1.1 vs Muse Glimmer 30BSame lab
  • Muse Spark 1.1 vs Muse Spark 1.3Same lab
  • GPT-6.1 Sol vs Grok 4.6Same size
  • GPT-6.1 Sol vs Grok 4.7Same size
  • GPT-6.1 Sol vs Grok Code Fast 1New provider
  • GPT-6.1 Sol vs Healer AlphaNew provider

Model pages

  • GPT-6.1 Sol58 outputs, specs and price
  • Muse Spark 1.158 outputs, specs and price
Workshop · 150 skills

Build your own version.

Editable skills and working examples for your agent.

Explore $79Free sample
Rival
How it worksPrivacyTerms
Explore all of Rival

Explore

  • Compare models
  • All models
  • Image comparison
  • Audio comparison
  • Image generation
  • Best AI for...
  • Arena
  • API pricing
  • Challenges

Discover

  • SubjectiveBench
  • Default Index
  • Research
  • Research downloads
  • Rival Kits
  • Find your AI taste
  • UI Glow-Up
  • VoiceLock
  • Cost Cutter
  • Agent skills
  • Benchmarks vs Vibes
  • Brief
  • Jailbreak
  • Model updates
  • Provider status
  • AI creators

Connect

  • Methodology
  • Advertise
  • Partnerships
  • Privacy policy
  • Terms
  • RSS feed