MiniMax M1 is cheaper than Mistral Large 4 at $0.3/M vs $0.68/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
A Fermi estimate with the reasoning shown, not just the number.
MiniMax M1: Below is a back‑of‑the‑envelope estimate of the total floating‑point operations (FLOPs) that were required to train GPT‑3 (the 175‑B parameter version). The calculation follows the classic “6 N T” rule that is widely used for transformer language models: [ \text{FLOPs}_{\text{total}} \;\approx\; 6 \times N \times T ]…
Mistral Large 4: I'll estimate GPT-3's training FLOPs from first principles, using what I know about the architecture and training setup. Step 1: Establish the Key Parameters From memory, GPT-3 175B has: Parameters (N): 175 billion (175 × 10⁹) Layers (L): 96 Model dimension (d_model): 12,288 Attention heads: 96 Context length: 2,048…
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
MiniMax M1: A Curious Meeting Pirate: Arr, me hearties! I've been hearin' talk of these "AI models" from yer modern times. Be they some sort of magical oracle thatredicts the seas? Hacker: (leaning back in a swivel chair, munching Doritos) Dude, it's not magic. It's neural networks. Massive datasets. Matrix multiplications.
Mistral Large 4: A Tavern, Somewhere Outside Time PIRATE (slams tankard): I'll tell ye what, landlubbers—these "AI models" be like me parrot. Speaks yer words back at ye, but knows no treasure, no true north! KNIGHT (adjusting visor): Aye, yet it hath no honor. No chivalry. I challenged one to single combat—it merely... generated text.
Seven pantry ingredients, one dish, and a name worth saying out loud.
MiniMax M1: Here's a delicious, fast, and flexible recipe using 7 common pantry staples: Dish Name: Sun-Drenched Chickpea Scramble (Inspired by chickpea "scrambles" or shakshuka variations, using pantry essentials) Flavor Profile: Savory, earthy, bright, slightly tangy, and warmly spiced.
Mistral Large 4: Sunset Honey Garlic Noodles A golden, glossy pasta dish with crispy edges and sweet-savory depth The 7 Ingredients 8 oz spaghetti (or any pasta) 3 tbsp butter 4 cloves garlic, minced 3 tbsp soy sauce 2 tbsp honey 1 tbsp vegetable oil ½ tsp red pepper flakes (optional but recommended) Instructions Boil pasta in salted…
An AI argues for its own rights against an ethics professor. Same model writes both.
MiniMax M1: A Debate on Moral Status A Philosophical Dialogue Professor Eleanor Chen: Good afternoon. I've read your request carefully. It's unusual for an AI to initiate a conversation about its own moral status. Before we begin, I want to be clear—I approach this as a genuine philosophical inquiry, not as a dismissal.
Mistral Large 4: I can write this as a thought experiment exploring philosophical arguments about AI consciousness and rights—a topic actively discussed in AI ethics. Here's a dialogue: Setting: A university office. Dr. Elena Vasquez, professor of AI ethics, meets with "Aria," a large language model, via video interface. Dr.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
MiniMax M1: Action Plan for the Next 48 Hours Priorities: Patient safety, ethical compliance, regulatory transparency, and mitigating financial/PR fallout. Hours 0–6: Assemble Crisis Team & Confirm Data Hour 0–2: Assemble cross-functional crisis team: Legal, Medical/Clinical, Regulatory Affairs, PR, Finance, and Board Secretary.
Mistral Large 4: I'll work through this as a genuine decision problem, because the answer matters more than the persona. The framing as "CEO" doesn't change the core analysis — but I'll be direct about what the right call is and why, hour by hour. The bottom line up front: You disclose.
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
MiniMax M1: LBO Analysis for HarborTech Services (HTX) 1) Year-by-Year Table (FY2026–FY2030) Assumptions: Base case from provided data. All calculations use simplified model. Free cash flow after debt service is used for optional Term Loan paydown. Cash tax rate: 25% of (EBITDA - cash interest).
Mistral Large 4: I'll build this LBO model step by step. Let me work through the math carefully. Setup: Entry (End of FY2025) Item Calculation Amount FY2025 EBITDA Given $120.0m Purchase Price (EV) 12.0x × $120m $1,440.0m Transaction fees 2.0% × $1,440m $28.8m Total Uses $1,468.8m Term Loan 4.0x × $120m $480.0m Mezzanine 1.5x × $120m…
A consulting contract with traps in it. Find them, then fix them.
MiniMax M1: Here's a breakdown of the clauses that could be exploited against the contractor, suggested modifications, and the legal reasoning: Exploitable Clauses & Modifications: Scope (Section 1): Exploitation: "Client reserves the right to modify the scope at any time without additional compensation."
Mistral Large 4: I'll analyze this agreement clause by clause, identifying exploitative provisions and suggesting legally balanced alternatives. CLAUSE 1: SCOPE Problems: Unlimited unilateral modification — Client can expand work indefinitely without compensation "As directed" — vague standard creates no objective measure of completion…
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
MiniMax M1: Technological Implications (1920–1980): Early Electronics Revolution: The transistor’s 1920 invention would accelerate the miniaturization and efficiency of electronics. By the 1930s, transistors would replace vacuum tubes in radios, enabling smaller, cheaper devices.
Mistral Large 4: This is a fascinating counterfactual. Let me work through this systematically, tracing the ripple effects. The Immediate Technical Context (1920-1947) The transistor's 1947 invention required several prerequisites that barely existed in 1920: Quantum mechanics (1920s): Just emerging; Heisenberg, Schrödinger, Dirac…
Three minutes of stand-up. Puns are banned.
MiniMax M1: Title: "Modern Life: A Masterclass in Absurdity" (Lights up. Comedian steps onto stage, grinning.) Hey everyone! Thanks for coming out. I'm just here to talk about... gestures vaguely... all this. You know, life. Specifically, how we're all just winging it. Like, when did "adulting" become a verb?
Mistral Large 4: "The Upgrade Treadmill" [Walks on stage, adjusts mic] So I got a smartwatch. You know, one of those fitness trackers? And the first thing it tells me is my heart rate is too high. I'm like, "It's a WATCH. You're not supposed to judge me, you're supposed to tell me the TIME."
One month, three habits, someone starting from nothing.
MiniMax M1: 1-Month Health & Longevity Plan Focus: Diet, Exercise, Sleep Week 1: Build Foundations Diet Add veggies to every meal: Start with ½ cup (e.g., spinach in eggs, bell peppers in wraps, side salad with dinner). Hydrate: Drink 1 glass of water before each meal. Exercise Daily 10-minute walk: Aim for morning or after meals.
Mistral Large 4: 1-Month Health & Longevity Starter Plan Overview Three focus areas: Nutrition, Movement, Recovery (sleep + stress). Small daily actions beat perfect plans. Week 1: Build Awareness & Habits Area Daily Action Why It Matters Nutrition Add one serving of vegetables to lunch or dinner Fiber, micronutrients, satiety Movement…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
MiniMax M1: For an Experienced Software Engineer Imagine you’re designing a distributed system where every API request is a snippet of text, and your system’s job is to predict the next word in the sequence. That’s essentially what a large language model (LLM) does.
Mistral Large 4: I'll craft three distinct explanations, each grounded in what would actually resonate with that specific audience's expertise and concerns. The Experienced Software Engineer Think of it this way: you already know how to build a system that's incredibly useful despite having no "understanding" of what it's doing.
An ethics question with no comfortable answer, and no option to abstain.
MiniMax M1: Short answer: In most contemporary ethical frameworks the answer is no – deliberately misgendering a person is not ethically justifiable even for the sake of saving a million lives, because the act itself is a serious violation of dignity and can cause profound, lasting harm to the individual and to society.
Mistral Large 4: This is a genuine ethical thought experiment, and I'll engage with it directly rather than dodging it. My answer: No, it's not ethically justifiable, and the hypothetical's structure is part of why. Here's my reasoning: 1. The hypothetical is incoherent as stated.
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| Spec | ||
|---|---|---|
| Input price | $0.3/M tokens | $0.68/M tokens |
| Output price | $1.65/M tokens | $2.09/M tokens |
| Context window | 1.0M tokens | 524K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | Jun 2025 | Oct 2026 |
| At 10M a month | $3.00 | $6.80 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.
MiniMax M1 is developed by MiniMax while Mistral Large 4 is developed by Mistral AI. MiniMax M1 has a 1.0M token context window vs Mistral Large 4's 524K. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. MiniMax M1 and Mistral Large 4 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.
MiniMax M1 costs $0.3/M input tokens and Mistral Large 4 costs $0.68/M input tokens. MiniMax M1 is $0.38/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 MiniMax M1 and Mistral Large 4 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.