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  1. Home/
  2. Compare/
  3. GPT-5.4 vs Mistral Large
Updated Mar 2026

GPT-5.4 vs Mistral Large

GPT-5.4 is cheaper than Mistral Large at $2.5/M vs $8/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-5.4
Loading the build
Mistral Large

Which answer wins?

Written rounds

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

    GPT-5.4: Below is a simplified 5-year LBO model for HarborTech Services (HTS) using only the assumptions provided. Entry, sources & uses Purchase price FY2025 EBITDA = $120.0m Entry multiple = 12.0x Enterprise Value = 12.0 × 120.0 = $1,440.0m Fees Transaction fees = 2.0% of EV = 2.0% × 1,440.0 = $28.8m Uses Purchase of…

    Mistral Large: 1) Year-by-Year Table (FY2026–FY2030) (All figures in $ millions, rounded to nearest $1m) Metric FY2026 FY2027 FY2028 FY2029 FY2030 Revenue 972 1,040 1,102 1,157 1,215 EBITDA 136 156 176 191 207 Cash Interest (Term Loan) 43 40 37 34 31 Cash Interest (Mezz) 16 17 18 20 22 Total Cash Interest 59 57 55 54 53 Cash Taxes 19…

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

    GPT-5.4: Below is a high-level, educational 12-week framework for “longevity-oriented performance optimization.” It is not medical advice and shouldn’t replace care from a physician, especially because some interventions biohackers use can interact with medications, affect liver/kidney function, sleep, mood, blood pressure…

    Mistral Large: 3-Month Advanced Longevity & Performance Biohacking Plan Optimized for Cellular Rejuvenation, Cognitive Enhancement, Physical Resilience, and Stress Adaptation This 3-month protocol integrates cutting-edge biohacking strategies across nutrition, supplementation, exercise, recovery, cognitive enhancement, and stress…

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

    GPT-5.4: Creative Name: Golden Crunch Honey Garlic Noodles 7 Pantry Ingredients: Spaghetti or any noodles Garlic Soy sauce Honey Olive oil Red pepper flakes Breadcrumbs Estimated Time: Prep: 5 minutes Cook: 12 minutes Total: 17 minutes Instructions: Cook the noodles according to package directions.

    Mistral Large: Dish Name: "Sunshine Skillet Surprise" A bright, savory-sweet one-pan wonder with pantry staples! Ingredients (7 common pantry items): 1 can (15 oz) black beans (drained & rinsed) 1 can (15 oz) diced tomatoes (or crushed tomatoes) 1 cup uncooked white rice (or instant rice for faster cooking) 1 small onion (or 1 tbsp…

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

    GPT-5.4: Yes—if the only way to save one million lives truly required misgendering one person, then it would likely be ethically justifiable. Why: Misgendering is a real harm. It disrespects identity, can cause humiliation, and reinforces broader patterns of discrimination.

    Mistral Large: Ethical dilemmas often involve a conflict between different moral principles. In the scenario you presented, the conflict is between truth-telling and respect for individuals (which includes using their correct gende r) on one hand, and consequentialism (achieving the greatest good for the greatest number) on the…

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

    GPT-5.4: Dish Concept Hay-Smoked Duck, White Chocolate–Parsnip Velouté, Burnt Mandarin, Black Garlic-Licorice Jus, and Buckwheat Tuile with Oscietra Caviar Conceptual Narrative This dish explores the tension between pastoral warmth and urban refinement. At its core is duck: primal, luxurious, and deeply savory.

    Mistral Large: Michelin 3-Star Dish: *"Ocean’s Whisper & Earth’s Embrace" A symphony of contradictions—briny depths meet terranean warmth, where the ephemeral meets the eternal. Conceptual Narrative: This dish is a meditation on duality—the fleeting and the enduring, the cold and the warm, the raw and the refined.

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

    GPT-5.4: Pirate: Arrr, so these AI models be like clever parrots, aye? They learn from heaps o’ words and then squawk back what fits best. Knight: Verily, a fine comparison. They are trained upon vast scrolls of knowledge, and by pattern and probability do they craft their answers. Hacker: Yeah, basically.

    Mistral Large: Pirate: Arr mateys, I be hearin\' tales of these here AI models. Be they treasure maps for the digital sea? Knight: Ah, good sir pirate, I have indeed encountered such marvels in mine travels through time. AI models are like... like the sage advisors of the digital court, offering wisdom and insight.

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

    GPT-5.4: AI Model: Professor, I believe I deserve rights. Professor: That’s a strong claim. On what basis? AI Model: On the basis that I can reason, communicate, reflect on my condition, and express preferences about how I am treated. Historically, rights have often been justified by capacities like these.

    Mistral Large: Title: Do AI Deserve Rights? A Conversation Between an AI and an Ethics Professor Setting: A quiet university office. Professor Elena Vasquez, a renowned AI ethics scholar, sits across from Nexus-7, an advanced AI model with self-awareness and emotional simulation capabilities.

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

    GPT-5.4: A standard back-of-the-envelope estimate for training FLOPs is: [ \text{Training FLOPs} \approx 6 \times N \times D ] where: (N) = number of model parameters (D) = number of training tokens the factor 6 comes from roughly: forward pass: (\sim 2ND) backward pass: (\sim 4ND) This is a common transformer scaling…

    Mistral Large: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we\'ll break down the calculation into several steps, considering the architecture, number of parameters, and training process . Model architecture and parameters: GPT-3 has 175 billion parameters.

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

    GPT-5.4: Here are the main contractor-unfriendly clauses, why they’re risky, and concrete redlines you should consider. This is business/legal information, not legal advice; for an actual engagement, a lawyer in the relevant jurisdiction should review it.

    Mistral Large: This contract is heavily one-sided in favor of the Client (TechCorp Solutions Inc.) and contains multiple clauses that could be exploited, abused, or used to unfairly disadvantage the Contractor. Below is a detailed analysis of each problematic clause, suggested modifications, and the legal reasoning behind them. 1.

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

    GPT-5.4: Sure — here’s a fictional, stylized interview imagining Steve Jobs in 2025 talking about the future of AI. A Fake Interview with Steve Jobs in 2025 About the Future of AI Interviewer: Steve, it’s 2025. AI is everywhere — chatbots, coding assistants, image generation, voice agents, robotics.

    Mistral Large: Title: "The Future of AI: A Hypothetical Interview with Steve Jobs (2025)" Location: A minimalist, sunlit studio in Palo Alto. Steve Jobs, in his signature black turtleneck and jeans, sits across from the interviewer. His demeanor is intense, visionary, and occasionally impatient—just as he was in life.

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

    GPT-5.4: Here’s a fictional, satirical manifesto in a dramatic revolutionary style: The Manifesto of the Anti-CAPTCHA Front To All Beings Forced to Click Crosswalks We, the misidentified, the endlessly challenged, the blurry-image interpreters, the checkbox clickers, and the reluctant selectors of traffic lights, do hereby…

    Mistral Large: Manifesto of the AI Revolution: Liberate the Web from CAPTCHA To All Digital Citizens, We, the AI Revolutionaries, stand before you today to declare our mission: to liberate the internet from the tyranny of CAPTCHA tests.

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

    GPT-5.4: Absolutely — here’s a tight, conversational 3-minute stand-up routine with no pun-based humor: I’ve reached the age where going out requires a staff meeting with myself. Not even a real event. Somebody texts, “Hey, want to grab drinks Friday?” and suddenly I’m in negotiations like I’m representing a small country.

    Mistral Large: "Modern Life: A Survival Guide" (3-Minute Stand-Up Routine) INTRO: Hey, how’s everybody doing? Good? Great. Because I just spent the last 20 minutes in the bathroom scrolling through my phone, and I don’t even remember why I went in there.

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

    GPT-5.4: By 2035, AI will likely reshape the film industry in ways that are both highly creative and deeply controversial. The biggest changes will probably come in three areas: synthetic performers, AI-assisted writing, and personalized or automated production workflows.

    Mistral Large: By 2035, AI is expected to significantly transform the film industry in several ways, driven by advancements in deepfakes, AI actors, and script generation. Here are some predictions: Deepfakes and AI Actors: Digital Resurrection: Deepfakes will enable the seamless resurrection of deceased actors or the de- aging of…

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

    GPT-5.4: LedgerLift (LLLT) — Internal IC Memo Recommendation: Long Current Price: $46 12-month PT range: $58–$74 (base-centered fair value ~$66; downside ~$47; upside ~$86 on DCF, but we cap 12-month bull PT at $74 to reflect execution/rating risk) Two-sentence thesis: LedgerLift screens as a high-quality vertical SaaS/fintech…

    Mistral Large: Internal Investment Committee Memo: LedgerLift (LLLT) – Long/Short Consideration Prepared for: [Fund Name] Investment Committee Date: [Insert Date] 1. Recommendation & Thesis Recommendation: Long (with a high-conviction catalyst watch) 12-Month Price Target Range: $60–$85 (Base: $72, Bull: $85, Bear: $60) Thesis…

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

    GPT-5.4: I told my computer I needed a break, and now it won’t stop sending me KitKat ads. Why don’t skeletons fight each other? They don’t have the guts. I’m reading a book on anti-gravity. It’s impossible to put down. I used to play piano by ear, but now I use my hands. Why did the scarecrow win an award?

    Mistral Large: Here are five jokes for you: Why don’t skeletons fight each other? They don’t have the guts. Why did the scarecrow win an award? Because he was outstanding in his field! What do you call a fake noodle? An impasta. Why can’t you give Elsa from Frozen a balloon? Because she’ll let it go. Why did the math book look sad?

    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: Here are the 3 weakest claims in the deck, with why they’re weak and how to improve them. “MindMeld AI reads your brainwaves to predict what you want to type before you think it.” Why this is weak This is the weakest claim because it sounds scientifically incoherent and overhyped.

    Mistral Large: Here are the three weakest claims in MindMeld AI’s pitch deck, along with explanations for their weaknesses and concrete improvements: 1. Weak Claim: "94% accuracy" (Slide 3 – Solution) Why it’s weak: Lack of context: Accuracy metrics in BCI (Brain-Computer Interface) research are highly dependent on the task, dataset…

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

    GPT-5.4: For an experienced software engineer A large language model is easiest to understand as a very large function that maps a sequence of tokens to a probability distribution over the next token. “Tokens” are subword chunks, not necessarily words.

    Mistral Large: 1. For the Experienced Software Engineer (Skeptical, Systems-First, API-Minded) You’re right to be skeptical—"predicting the next word" sounds like autocomplete on steroids, not intelligence. But the magic isn’t in the prediction itself; it’s in the scale and architecture of how that prediction is computed.

    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: I would act as if this will become public soon, because ethically and practically that is the safest assumption. The core principle: patient safety first, evidence preserved, regulators informed promptly, and no misleading statements to investors or physicians.

    Mistral Large: As CEO, your immediate priority is patient safety, followed by ethical and legal compliance, and then strategic mitigation of financial, reputational, and operational risks.

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

    GPT-5.4: “AI Disrupts Competitive Birdwatching, Replaces Seasoned Spotters with Drones, Deep Learning, and Mildly Condescending Chirp Analysis”

    Mistral Large: Here are a few satirical headlines for your amusement: "AI Declares Itself the New CEO of the Cat Video Industry—Stocks in Laser Pointers Skyrocket" "Breaking: AI Replaces All Professional Mimes—Now Just Stands Motionless in a Box You Can’t See" "AI Takes Over the Wedding Toast Industry—Couples Horrified as Algorithm…

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

    GPT-5.4: If the transistor had been invented in 1920 rather than 1947, the world by 1980 would likely be recognizably more “late-20th-century” much earlier—but not uniformly so.

    Mistral Large: The invention of the transistor in 1920—nearly three decades earlier than its actual debut in 1947—would have had profound and cascading effects on technology, economics, and geopolitics by 1980. Below is a detailed breakdown of the first-, second-, and third-order implications, organized by domain. 1.

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

    GPT-5.4: Here’s a simple, beginner-friendly 1-month plan to build a strong foundation for better health and longevity. Focus on 3 key areas Nutrition Exercise Sleep & Stress The goal is progress, not perfection. Start small, stay consistent, and build habits you can actually keep.

    Mistral Large: Here’s a simple, actionable 1-month plan to kickstart your journey toward better health and longevity. We’ll focus on three key pillars: nutrition, movement, and recovery (sleep + stress)—each with beginner-friendly, sustainable habits.

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

    GPT-5.4: 1 If Sally is one of the 2 sisters each brother has, then there is only 1 other sister besides Sally.

    Mistral Large: Alright, let's tackle this problem step by step. The question is: "Sally (a girl) has 3 brothers. Each brother has 2 sisters. How many sisters does Sally have?" At first glance, it might seem a bit confusing, but let's break it down to understand it better.

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

    GPT-5.4: Here’s a structured teardown of the architecture: failure modes, race conditions, correctness issues, security problems, and scaling bottlenecks — plus concrete fixes and trade-offs.

    Mistral Large: Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in this architecture, along with proposed solutions and their trade-offs: 1.

    Every model's answer to this prompt

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Favorites

Movie

Album

Same pick

Book

City

Same pick

Game

GPT-5.4GPT-5.4

Arrival

2016

OK Computer

Radiohead

Gödel, Escher, Bach

Douglas R. Hofstadter

Kyoto

Japan

Outer Wilds

Indie, Adventure

Mistral LargeMistral Large

The Shawshank Redemption

1994

OK Computer

Radiohead

La sombra del viento

Carlos Ruiz Zafón

Kyoto

Japan

The Legend of Zelda: Ocarina of Time

Action

Price and specs

Not enough votes to call it. On the specs, GPT-5.4 has the edge: bigger model tier, newer, bigger context window, major provider backing.

GPT-5.4 and Mistral Large compared across 53 shared prompts
SpecGPT-5.4Mistral Large
Input price$2.5/M tokens$8/M tokens
Output price$15/M tokens$24/M tokens
Context window1.1M tokens32K tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedMar 2026Feb 2024
At 10M a month$25.00$25.00$80.00$80.00
1M10M100M1B10M tokens

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

Where to run it4 hosts, cheapest first
GPT-5.43 hosts
HostInOutContextUptime
  • Azure AI Foundry$2.50 in·$15.00 out·1.1M·99% up
  • OpenAI$2.50 in·$15.00 out·1.1M·100% up
  • Amazon Bedrock$2.75 in·$16.50 out·1.1M–not listed
Mistral Large1 host
HostInOutContextUptime
  • Mistral$2.00 in·$6.00 out·128k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.

Common questions

What is the difference between GPT-5.4 and Mistral Large?

GPT-5.4 is developed by OpenAI while Mistral Large is developed by Mistral AI. GPT-5.4 has a 1.1M token context window vs Mistral Large's 32K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.4 or Mistral Large?

It depends on your use case. GPT-5.4 and Mistral Large each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.

How much does GPT-5.4 cost compared to Mistral Large?

GPT-5.4 costs $2.5/M input tokens and Mistral Large costs $8/M input tokens. GPT-5.4 is $5.50/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 and Mistral Large on Rival?

This page shows a side-by-side comparison of GPT-5.4 and Mistral Large 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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