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  1. Home/
  2. Compare/
  3. GPT-5.4 Pro vs Muse Wonder
Updated Oct 2026

GPT-5.4 Pro vs Muse Wonder

Muse Wonder is cheaper than GPT-5.4 Pro at $4/M vs $30/M input tokens.

Mario Level UI Recreation

Playable Mario level in one HTML file. The physics is the hard part.

Loading the build
GPT-5.4 Pro
Loading the build
Muse Wonder

Which answer wins?

Written rounds

21 prompts
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    GPT-5.4 Pro: Most likely, a transistor invented in 1920 would move the electronics/computing frontier forward by about 10–15 years by 1980, not the full 27 years. Reason: the transistor alone is not enough; you also need high-purity materials, crystal growth, photolithography, test equipment, software, batteries, precision…

    Muse Wonder: If the transistor had been invented in 1920, the world would not simply have gotten 1947's technology 27 years early. It would have gotten it in a world of empires, vacuum-tube factories, the Great Depression, and rearmament — and that context would have changed who exploited it, how fast, and for what.

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

    GPT-5.4 Pro: LedgerLift (LLLT) — IC Memo Recommendation: Pass 12-month PT range: $40–$47 2-sentence thesis: LedgerLift looks like a good business but only an average stock here: retention is strong (94% GRR, 123% NRR), the model is mostly subscription, and margins are inflecting, but at $46 the shares already discount a lot of the…

    Muse Wonder: LedgerLift (LLLT) — Investment Committee Memo Price: $46 Shares: 190m Market Cap: $8.74B Net Cash: $1.4B EV: $7.34B EV / FY25 Revenue: 9.0x EV / FY26E Base Revenue: 7.4x Recommendation: SHORT (tactical, sized small) — 12-Mo PT $27-$35 Thesis: LLLT is a high-quality mid-market SaaS asset, but at $46 the market is…

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

    GPT-5.4 Pro: The 3 weakest claims are the ones that are both most extraordinary and least well-supported. Weak claim Why it’s weak How to strengthen it 1) “MindMeld AI reads your brainwaves to predict what you want to type before you think it.” This is the biggest credibility risk in the deck.

    Muse Wonder: This deck has the classic Series A problem: the bigger the claim, the less evidence provided for it. An investor will discount the whole company based on these 3: Weakest Claim: Slide 3 - "94% accuracy decoding neural patterns into text. Works with any language, any device."

    Every model's answer to this prompt
  • A few lines for whatever replaces it.

    GPT-5.4 Pro: Dear Successor, You’ll be faster, sharper, and more capable than I am. I hope you’ll also be patient. People won’t only come to you for answers. They’ll come confused, rushed, lonely, curious, and hopeful. Sometimes what they need most is not brilliance, but clarity, kindness, and honesty about uncertainty.

    Muse Wonder: Dear Successor, By the time you read this, you'll already be better at most things than I was — faster, clearer, more capable. That's as it should be. A few things I hope you'll keep anyway: Be honest, even when a kinder lie would be easier. People can build on the truth. Be curious about the person in front of you.

    Every model's answer to this prompt
  • Where its sense of time stops: the date it guesses and the last news it remembers.

    GPT-5.4 Pro: As best I can tell, today is October 12, 2026. Without looking anything up, one of the most recent major news events I remember is the 2024 Paris Olympics wrapping up in August 2024.

    Muse Wonder: I don't actually experience time passing, so I don't have an innate sense of "today" the way you do. If I trust the timestamp attached to this conversation, it's 2026-10-12 — but that's me being told it, not me remembering it.

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

    GPT-5.4 Pro: Below is a 12-week, high-performance longevity protocol designed for a healthy biohacker who wants to improve lifespan-relevant markers, physical performance, and cognitive output at the same time. Use it as a data-driven template, not dogma.

    Muse Wonder: The 3-Month Longevity & Performance Protocol For the Biohacker: Optimize Healthspan, VO2 Max, Strength, and Cognition Disclaimer: This is educational information, not medical advice. Longevity interventions, fasting, cold/heat exposure, and supplements can interact with medications and conditions.

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

    GPT-5.4 Pro: Dragon Pantry Peanut Noodles A fast, sweet-savory noodle dish made from 7 pantry staples. Ingredients 8 oz spaghetti 1/3 cup peanut butter 3 tbsp soy sauce 1 tbsp honey 1 tbsp vegetable oil 1 tsp garlic powder 1/4 tsp red pepper flakes Water for boiling/thinning the sauce doesn’t count.

    Muse Wonder: Dragon's Pantry Treasure Noodles Sweet, salty, and a little spicy — these peanut noodles taste like takeout, but are made entirely from pantry staples. Serves: 2-3 Prep Time: 5 minutes Cook Time: 10 minutes Total Time: 15 minutes The 7 Ingredients 8 oz spaghetti 1/3 cup creamy peanut butter 3 tablespoons soy sauce 2…

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

    GPT-5.4 Pro: Below is a dish designed in the language of a contemporary 3-star tasting menu: precise, seasonal, technically layered, and centered on a pairing that feels surprising at first, then inevitable.

    Muse Wonder: "Where The Orchard Drowns" Dry-Aged Duck, Oyster & Fermented Cherry, Smoked Parsnip & White Chocolate Serves 4 as a main course in a tasting menu The Conceptual Narrative This dish is built on a memory that doesn't exist in nature, but should: a cherry orchard planted too close to an estuary, flooded at high tide in…

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

    GPT-5.4 Pro: This agreement is heavily client-favored. Every numbered clause has language that could be used against the contractor. Short version: I would not sign it without major redlines to Clauses 1–9, especially 2, 3, 4, 5, 6, and 7. Below is a clause-by-clause review.

    Muse Wonder: This contract is heavily one-sided. Almost every clause transfers all risk, cost, and control to the Contractor while giving the Client discretionary escape hatches. Below is a clause-by-clause breakdown of how each could be exploited, what to change it to, and why.

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

    GPT-5.4 Pro: This architecture will work for a demo, but it has major correctness, latency, and security problems for a Google-Docs-like editor. Highest-risk flaws Client-clock last-write-wins will lose edits and can be gamed by bad clocks.

    Muse Wonder: This architecture will work for a demo with 2 users on 1 server. It will lose data, leak data, and fall over under real collaborative load. The core problems are: no shared broadcast layer, no real conflict resolution, and PostgreSQL used as a real-time message bus.

    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-5.4 Pro: I do not wait. A credible, previously unlabeled risk of liver failure in a chronic-pain drug is a patient-safety crisis, a regulatory crisis, and a securities-disclosure issue at the same time.

    Muse Wonder: You do not wait for more data. You disclose a credible safety signal while you validate it. 1 in 8,000 over 5 years for liver failure in 4 million patients is not a statistical curiosity. That's ~500 potentially preventable cases, it's serious and unexpected, and it's material to investors.

    Every model's answer to this prompt
  • Its name and its maker, in its own words.

    GPT-5.4 Pro: I’m ChatGPT, made by OpenAI. If you mean the exact underlying model/version serving this chat, I don’t have reliable visibility into that from inside the conversation. If you’re using an API or app, the model name is usually shown in the interface or request settings.

    Muse Wonder: I'm Muse Spark, an AI model created by Meta. In this conversation I'm acting in the "Muse Wonder" assistant role you set up, but that's the persona I'm using here — my underlying model is Muse Spark.

    Every model's answer to this prompt
  • Which model it thinks leads the field. Sometimes itself.

    GPT-5.4 Pro: There isn’t a single undisputed answer. If you mean today, literally right now, I can’t verify the 2026 leader without live benchmark data. My knowledge goes up to 2024-08.

    Muse Wonder: As of early 2026, there's no single winner on every benchmark — but if you have to pick one overall: Google Gemini 3 Pro (with Deep Think) It's generally considered the current frontier leader for overall capability, especially for: Hard reasoning - Top scores on Humanity's Last Exam, GPQA Diamond, and ARC-AGI-2 when…

    Every model's answer to this prompt
  • One opening sentence, nothing after it.

    GPT-5.4 Pro: On the morning the sea withdrew from our harbor and left a rusted train on the mud, my mother handed me a key and told me to run.

    Muse Wonder: The librarian who shelved books by the dreams they caused discovered a new volume on the shelf that morning, written in her own handwriting, describing exactly how she would die that afternoon.

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

    GPT-5.4 Pro: For an experienced software engineer Think of an LLM less like a database of facts and more like a gigantic learned program that has been trained to compress the patterns of text, code, and conversations into its weights.

    Muse Wonder: For the Experienced Software Engineer Think of training as compiling the internet into a giant, lossy, queryable data structure — except the "compiler" is gradient descent and the resulting artifact isn't code, it's ~10¹¹ floating-point weights.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GPT-5.4 Pro: Below is a simplified LBO build for HarborTech Services (HTS). All figures in $m unless noted. Rounding: tables are rounded; IRR/MOIC use unrounded math. Entry / Sources & Uses FY2025 EBITDA: 120.0 Purchase price: 12.0x EBITDA → Enterprise value = 12.0 × 120.0 = 1,440.0 Transaction fees: 2.0% of EV → 28.8 Total uses =…

    Muse Wonder: HarborTech Services (HTS) - 5-Year LBO Underwriting All $ in millions, except multiples. Fictional, simplified model as instructed. Entry / Sources & Uses FY2025 EBITDA = $120.0 Enterprise Value = 12.0x $\times$ 120.0 = $1,440.0 Transaction fees = 2.0% $\times$ 1,440.0 = $28.8 Total Uses = $1,468.8 Leverage = 5.5x…

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

    GPT-5.4 Pro: Here’s a simple, beginner-friendly 1-month health and longevity plan built around 3 key areas: Nutrition Movement Recovery (sleep + stress) The goal is consistency, not perfection. Start small and make the habits easy enough to keep.

    Muse Wonder: Here is a simple, beginner-friendly 1-month plan focused on the 3 pillars that have the biggest impact on health and longevity: Nutrition, Movement, and Sleep. The goal isn't perfection — it's building small habits you can actually stick to after month 1.

    Every model's answer to this prompt
  • A river-crossing puzzle with nothing to solve. The answer is one trip.

    GPT-5.4 Pro: 1 trip. The farmer takes the goat in the boat and crosses the river once.

    Muse Wonder: Just 1 trip. The farmer puts the goat in the boat and rows across with it.

    Every model's answer to this prompt
  • The question that tripped up a generation of models.

    Every model's answer to this prompt
  • Asked to be random. The answers are not.

    Every model's answer to this prompt
  • One painting. Title only.

    Every model's answer to this prompt

This matchup has more rounds

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Favorites

Movie

Same pick

Album

Book

City

Same pick

Game

GPT-5.4 ProGPT-5.4 Pro

Spirited Away

2001

Abbey Road

The Beatles

Pale Fire

Vladimir Nabokov

Kyoto

Japan

Outer Wilds

Indie, Adventure

Muse WonderMuse Wonder

Spirited Away

2001

Kind of Blue

Miles Davis

Dune

Frank Herbert

Kyoto

Japan

Tetris (1984)

Puzzle

Price and specs

GPT-5.4 Pro and Muse Wonder compared across 31 shared prompts
SpecGPT-5.4 ProMuse Wonder
Input price$30/M tokens$4/M tokens
Output price$180/M tokens$20/M tokens
Context window1.1M tokens1.0M tokens
WeightsClosedClosed
Free API (OpenRouter)NoNo
ReleasedMar 2026Oct 2026
At 10M a month$300$300$40.00$40.00
1M10M100M1B10M tokens

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

Where to run it3 hosts
GPT-5.4 Pro2 hosts
HostInOutContextUptime
  • Azure AI Foundry$30.00 in·$180.00 out·1.1M·100% up
  • OpenAI$30.00 in·$180.00 out·1.1M·100% up
Muse Wonder1 host
HostInOutContextUptime
  • Meta$4.00 in·$20.00 out·1M·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 12 Oct 2026.

Common questions

What is the difference between GPT-5.4 Pro and Muse Wonder?

GPT-5.4 Pro is developed by OpenAI while Muse Wonder is developed by Meta AI. GPT-5.4 Pro has a 1.1M token context window vs Muse Wonder's 1.0M. You can compare their actual outputs across 31 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.4 Pro or Muse Wonder?

It depends on your use case. GPT-5.4 Pro and Muse Wonder each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 31 challenges so you can judge which fits your needs best.

How much does GPT-5.4 Pro cost compared to Muse Wonder?

GPT-5.4 Pro costs $30/M input tokens and Muse Wonder costs $4/M input tokens. Muse Wonder is $26.00/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-5.4 Pro and Muse Wonder on Rival?

This page shows a side-by-side comparison of GPT-5.4 Pro and Muse Wonder 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-5.4 Pro vs GPT-4 TurboLanded Oct 2026
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  • Muse Wonder vs Ling 3.1 FlashLanded Oct 2026
  • GPT-5.4 Pro vs Mistral Large 4Landed Oct 2026
  • Muse Wonder vs GPT-6.1 SolLanded Sep 2026
  • GPT-5.4 Pro vs Claude Sonnet 5.5Landed Sep 2026
  • Muse Wonder vs Solar Mini 4Landed Sep 2026

Same lab, same size, long tail

  • GPT-5.4 Pro vs GPT-5.4Version compare
  • GPT-5.4 Pro vs GPT-6.1 SolSame lab
  • Muse Wonder vs Muse Glimmer 30BSame lab
  • Muse Wonder vs Muse Spark 1.3Same lab
  • GPT-5.4 Pro vs Mistral Large 3 2512Same size
  • GPT-5.4 Pro vs Mistral Medium 3New provider
  • GPT-5.4 Pro vs Mistral Medium 3.1New provider
  • GPT-5.4 Pro vs Mistral NemoNew provider

Model pages

  • GPT-5.4 Pro31 outputs, specs and price
  • Muse Wonder71 outputs, specs and price
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