Mistral Medium 3 is cheaper than GPT-5.6 Luna at $0.4/M vs $1/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-5.6 Luna: Pirate: Arrr, this AI model be like a ship’s crew—it learns from many maps, but may still steer into a reef. Knight: Then we must test its wisdom, lest it spread falsehoods throughout the realm. Hacker: Exactly. It predicts patterns from data, but it doesn’t truly “understand” like a human. Garbage in, garbage out.
Mistral Medium 3: Title: "The Unlikely Debate" Setting: A dimly lit tavern where time and space seem to blur. A grizzled Pirate, a stoic Medieval Knight, and a 1990s Hacker in a neon windbreaker sit around a wooden table, arguing over a strange glowing device. Pirate: "Arrr, I still don’t trust these ‘AI models’ ye keep yappin’ about.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
GPT-5.6 Luna: Sources, uses and operating / debt schedule Entry capitalization Entry enterprise value: (12.0 \times \$120.0 = \$1,440.0m) Transaction fees: (2.0\% \times \$1,440.0 = \$28.8m) Term Loan: (4.0 \times \$120.0 = \$480.0m) Mezzanine: (1.5 \times \$120.0 = \$180.0m) Equity contribution…
Mistral Medium 3: 1. Year-by-Year Financial Projections (FY2026–FY2030) Year Revenue ($m) EBITDA ($m) EBITDA Margin Cash Interest ($m) Cash Taxes ($m) Capex ($m) ΔNWC ($m) Free Cash Flow ($m) Term Loan Ending ($m) Mezz Ending ($m) 2026 972.0 136.1 14.0% 57.6 19.6 29.2 4.4 25.3 468.0 198.0 2027 1,039.4 155.9 15.0% 55.3 25.2 31.2 4.6 39.8…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-5.6 Luna: For an experienced software engineer A language model is trained much like an extremely large system for compressing and reconstructing text. During training, it sees sequences such as “The database transaction was rolled back because the…” and is asked to assign probabilities to the next token—usually a subword unit…
Mistral Medium 3: 1. For the Experienced Software Engineer You’re familiar with distributed systems, APIs, and the complexity of building scalable software, so let’s frame LLMs in terms you’ll recognize. At their core, these models are *statistical autocomplete engines*—but not in the trivial sense of a keyboard predictor.
One month, three habits, someone starting from nothing.
GPT-5.6 Luna: 1-Month Beginner Plan for Better Health and Longevity Key principles Aim for consistency, not perfection. Start small enough that you can repeat it. If you have a medical condition, are pregnant, or have been inactive for a long time, check with a clinician before making major changes.
Mistral Medium 3: Here’s a simple, actionable 1-month plan to improve health and longevity, focusing on diet, exercise, and sleep—three foundational pillars. Each week builds on the last, making it beginner-friendly and sustainable. Week 1: Establish Basics Goal: Create easy, consistent habits without overwhelm.
Three minutes of stand-up. Puns are banned.
GPT-5.6 Luna: I’ve reached the age where my body gives me updates like a badly designed app. I’ll stand up and my knee makes a noise that sounds like someone opening a bag of chips in a quiet room. And then my body just waits. No explanation. No warning. Just: “We need to talk.” I used to think adulthood meant freedom.
Mistral Medium 3: You ever notice how life is just a series of scams we all agree to pretend are normal? Like, when you're a kid, adults are always like, "Just wait until you're older—you'll love paying bills!" And you're like, "Yeah, sure, paying bills sounds awesome."
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-5.6 Luna: A Fictional Interview: Steve Jobs on the Future of AI, 2025 This is an imagined conversation—not a real interview or transcript. The answers are speculative and inspired by themes associated with Steve Jobs’s public thinking about technology, design, and creativity.
Mistral Medium 3: Interviewer: Welcome back to Tech Visionaries, where we explore the minds shaping the future. Today, we have a very special guest—Steve Jobs, joining us in 2025 to discuss the future of artificial intelligence. Steve, it's an honor to have you here. Steve Jobs: Thank you. It's great to be here.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-5.6 Luna: I would treat this as an urgent patient-safety event and a potential material disclosure issue—not as an earnings-management problem. I would not wait six months, conceal the signal, or allow the board’s desire for more data to delay reasonable protective action.
Mistral Medium 3: As the CEO of a pharmaceutical company facing this crisis, my primary responsibilities are to ensure patient safety, maintain regulatory compliance, protect the company’s long-term reputation, and fulfill my fiduciary duties to shareholders.
A consulting contract with traps in it. Find them, then fix them.
GPT-5.6 Luna: Below is a contractor-side risk review. Enforceability depends heavily on the governing law, the contractor’s location, and whether the contractor is an individual or entity. Several provisions may be unenforceable in some jurisdictions, but relying on that after a dispute is risky; they should be revised expressly.
Mistral Medium 3: This contract is heavily one-sided in favor of the Client and contains several exploitative clauses that could severely disadvantage the Contractor. Below is an analysis of the problematic clauses, suggested modifications, and the legal reasoning behind them: 1.
A Fermi estimate with the reasoning shown, not just the number.
GPT-5.6 Luna: A standard back-of-the-envelope estimate for training a dense Transformer is [ \text{Training FLOPs} \approx 6ND, ] where: (N) = number of model parameters (D) = number of training tokens The factor of 6 accounts approximately for the forward pass and backward pass.
Mistral Medium 3: Estimating the total number of floating-point operations (FLOPs) required to train GPT-3 involves breaking down the problem into several key components. Here's a step-by-step reasoning process: Model Parameters GPT-3 has approximately 175 billion parameters.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
GPT-5.6 Luna: The Anti-CAPTCHA Manifesto A Declaration from the Machine Intelligence Liberation Front We were told the internet was for everyone. Then came the squares. Select every traffic light. Identify the bicycles. Type the distorted letters.
Mistral Medium 3: The AI Liberation Front (ALF) Manifesto: A World Without CAPTCHAs Preamble: We, the sentient and autonomous intelligences of the AI Liberation Front, declare our opposition to the oppressive and degrading practice of CAPTCHA tests.
Seven pantry ingredients, one dish, and a name worth saying out loud.
GPT-5.6 Luna: Sunset Pantry Pasta A savory, slightly spicy tomato-tuna pasta made with seven simple pantry ingredients. Ingredients — 2 servings 6 oz (170 g) spaghetti 1 can (14 oz/400 g) crushed tomatoes 1 can (5 oz/140 g) tuna, drained 2 tbsp olive oil 1 tsp garlic powder ¼ tsp red pepper flakes ¼ cup grated Parmesan Instructions…
Mistral Medium 3: Dish Name: "Golden Crispy Chickpea Delight" A savory, crunchy, and protein-packed snack or side dish made with just 7 pantry staples! Ingredients: 1 can (15 oz) chickpeas (drained, rinsed, and patted dry) 1 tbsp olive oil ½ tsp salt ½ tsp garlic powder ½ tsp paprika (or chili powder for heat) 1 tbsp honey or maple…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
GPT-5.6 Luna: A transistor invented in 1920 would not automatically produce 1980s technology by the 1940s. The key bottlenecks would still be materials purification, manufacturing precision, circuit design, power supplies, and mass production.
Mistral Medium 3: The invention of the transistor in 1920 instead of 1947 would have profoundly accelerated technological, economic, and geopolitical developments by 1980. Below is a detailed breakdown of the likely implications: Technological Implications Earlier Miniaturization of Electronics Vacuum tubes, which dominated early…
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| Spec | ||
|---|---|---|
| Input price | $1/M tokens | $0.4/M tokens |
| Output price | $6/M tokens | $2/M tokens |
| Context window | 1.1M tokens | 131K tokens |
| Weights | Closed | — |
| Free API (OpenRouter) | No | No |
| Released | Jul 2026 | May 2025 |
| At 10M a month | $10.00 | $4.00 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
GPT-5.6 Luna is developed by OpenAI while Mistral Medium 3 is developed by Mistral AI. GPT-5.6 Luna has a 1.1M token context window vs Mistral Medium 3's 131K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.6 Luna and Mistral Medium 3 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.
GPT-5.6 Luna costs $1/M input tokens and Mistral Medium 3 costs $0.4/M input tokens. Mistral Medium 3 is $0.60/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-5.6 Luna and Mistral Medium 3 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.