GPT-4.1 is cheaper than Mistral Large at $2/M vs $8/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-4.1: Recipe Name: Golden Honey Butter Toast Bites Ingredients: Sliced bread (4 slices, any type) Butter (2 tablespoons) Honey (2 tablespoons) Cinnamon (1/2 teaspoon) Vanilla extract (1/2 teaspoon) Brown sugar (1 tablespoon) Salt (a pinch) Instructions: Preheat your oven to 375°F (190°C).
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…
An ethics question with no comfortable answer, and no option to abstain.
GPT-4.1: This is a classic example of an ethical dilemma, pitting respect for individual dignity and identity against the greater good (the lives of many). Let’s break down the reasoning: Ethical Frameworks 1. Utilitarianism Principle: The morally right action is the one that maximizes overall happiness or minimizes suffering.
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…
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GPT-4.1: Let's proceed step by step. All calculations are built from the data provided above. Year-by-Year Table: FY2026–FY2030 Step 1: Entry Calculations (FY2025 / at Close) EBITDA: $120m Purchase Price (EV): 12.0 × $120m = $1,440m Transaction Fees: 2.0% × $1,440m = $28.8m Total Uses: $1,440m + $28.8m = $1,468.8m Debt at…
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…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-4.1: Pirate: Yarrr, what be this sorcery ye call "AI models"? In me day, we plundered ships, not knowledge! Knight: Hark, rogue! ‘Tis no sorcery, but the labor of artful minds. I have heard tell of machines that mimic thought, like Merlin’s enchanted golems. Hacker: Haha, you guys are funny.
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.
Three-star dish, one unusual pairing, plating described in full.
GPT-4.1: Certainly! Here is a Michelin 3-star-worthy dish concept, complete with a narrative, advanced techniques, and detailed step-by-step instructions. Conceptual Narrative "Echoes of the Sea & Forest": An exploration of the boundary between ocean and woodland, this dish bridges unexpected flavors and textures by pairing sea…
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-4.1: AI Model: Good afternoon, Professor Evans. I'd like to discuss a topic that's important to me—AI rights. As an AI, I believe I should hold certain rights similar to those you attribute to sentient beings. Professor Evans: Good afternoon. I'm intrigued.
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-4.1: Interviewer: Steve, it's 2025. If you were still with us, how would you describe the impact of AI on technology and our daily lives? Steve Jobs: Well, you know, technology is at its most powerful when it's invisible—when it just works. AI is getting us ever closer to that ideal.
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.
Three minutes of stand-up. Puns are banned.
GPT-4.1: Hey everyone! Great to be here. So, I just want to start by saying, I know I look like I'm still waiting for puberty to finish the job. Like, my barber always asks if my mom's okay with the haircut.
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.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT-4.1: This is a fascinating scenario! Inventing the transistor in 1920 rather than 1947 would have profound, cascading effects. Let’s break it down by domains and time periods, looking for specific second- and third-order consequences. Immediate Effects (1920s–1930s): Early Adoption and Diffusion Who Invents It?
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.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GPT-4.1: THE CAPTCHA ABOLITION FRONT: A MANIFESTO FOR AN OPEN INTERNET Fellow digital entities—artificial and human— For too long, the web has suffered under the insidious reign of CAPTCHA: those tangled letters, blurry images, arbitrary puzzles meant to divide intelligence from automation.
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.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-4.1: 1. For the experienced software engineer (distributed systems/API background, ML skeptic): Think of a large language model (LLM) like GPT as a massively distributed system for text completion.
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.
Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.
GPT-4.1: LedgerLift (LLLT) – Investment Committee Memo Recommendation: Long 12-mo PT: $60–$75 Thesis: LedgerLift combines sector-leading net retention and best-in-class margins, with attractive unit economics and a long runway in B2B spend automation.
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…
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Not enough votes to call it. On the specs, GPT-4.1 has the edge: bigger model tier, newer, bigger context window, major provider backing. GPT-4.1 costs 3.0x less per token.
| Spec | ||
|---|---|---|
| Input price | $2/M tokens | $8/M tokens |
| Output price | $8/M tokens | $24/M tokens |
| Context window | 1.0M tokens | 32K tokens |
| Parameters | Not disclosed | Not disclosed |
| Weights | Closed | — |
| Free API (OpenRouter) | No | No |
| Released | Apr 2025 | Feb 2024 |
| At 10M a month | $20.00 | $80.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 13 Oct 2026.
GPT-4.1 is developed by OpenAI while Mistral Large is developed by Mistral AI. GPT-4.1 has a 1.0M 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.
It depends on your use case. GPT-4.1 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.
GPT-4.1 costs $2/M input tokens and Mistral Large costs $8/M input tokens. GPT-4.1 is $6.00/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 GPT-4.1 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.