Mistral Devstral Small 1.1 is cheaper than Mistral Large 4 at $0.1/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?
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
Mistral Devstral Small 1.1: Year-by-Year Table (FY2026–FY2030) Year Revenue EBITDA Cash Interest (Term Loan) Cash Interest (Mezz) PIK (Mezz) Cash Taxes Capex ΔNWC Free Cash Flow After Debt Service Ending Term Loan Balance Ending Mezzanine Balance 2026 $972m $136.08m $36.00m $13.50m $2.70m $18.42m $29.16m $4.86m $11.14m $344.00m $108.00m 2027…
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…
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
Mistral Devstral Small 1.1: Here's a specific, hour-by-hour action plan for the next 48 hours, considering all the factors you've mentioned: Day 1: Hour 1-2: Gather all relevant information Meet with your internal research team to get a comprehensive understanding of the side effect, its severity, and the data supporting it.
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.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
Mistral Devstral Small 1.1: Pirate: Arr mateys, I've been hearin' tales of these AI models. What be they, exactly? Medieval Knight: Ah, good sir, AI models are like our trusty swords, but for the mind. They learn and adapt, much like a knight learns from battles. 1990s Hacker: Yeah, but more like a super-smart, digital sword.
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
Mistral Devstral Small 1.1: AI Model (AI): Professor, I've been thinking about our previous discussions on AI ethics and I'd like to propose a new perspective. I believe that I, as an AI, deserve certain rights. AI Ethics Professor (Prof): That's an interesting point, AI. Can you elaborate on why you think you deserve rights?
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.
Three-month biohacker protocol, with dosages and biomarkers named.
Mistral Devstral Small 1.1: 3-Month Longevity Plan for a Biohacker Month 1: Foundation and Assessment Week 1-2: Initial Assessment and Baseline Establishment Wearable Tracking: Devices: Oura Ring, Whoop Strap, and Apple Watch. Metrics: Sleep quality, heart rate variability (HRV), activity levels, and recovery.
Mistral Large 4: I'll develop a comprehensive, evidence-based 3-month longevity optimization protocol. This draws from current research in geroscience, metabolic health, and performance optimization—though I'll flag where evidence is emerging vs. established.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
Mistral Devstral Small 1.1: Interviewer (I): Today, we have a truly remarkable guest with us. Welcome, Steve Jobs! Steve Jobs (SJ): Thanks for having me. It's always great to discuss the future. I: Let's dive right in. It's 2025, and AI has become even more integrated into our daily lives.
Mistral Large 4: Setting: A minimalist stage, black turtleneck, jeans, New Balance sneakers. A single chair. The year is 2025. Interviewer: Steve, it's 2025. AI is everywhere—writing code, diagnosing diseases, driving cars. What's your take? Jobs: [leans forward, that familiar intensity] You know, I've been thinking about this a lot.
One month, three habits, someone starting from nothing.
Mistral Devstral Small 1.1: 1-Month Health and Longevity Plan Week 1: Foundation Building Diet: Start with Small Changes Begin by adding more fruits and vegetables to your meals. Replace sugary drinks with water or herbal tea. Aim for a balanced diet with lean proteins, whole grains, and healthy fats.
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…
A Fermi estimate with the reasoning shown, not just the number.
Mistral Devstral Small 1.1: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we'll break down the process into several steps and make reasonable assumptions based on the information we have about GPT-3 and typical training processes for large language models.
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…
Three minutes of stand-up. Puns are banned.
Mistral Devstral Small 1.1: Ladies and Gentlemen, good evening! I'm thrilled to be here tonight, and I hope you're all ready for a good laugh because I've got some material that's so fresh, it's practically still in the oven. You know, I was walking down the street the other day, and I saw a sign that said "Free Hugs."
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."
An ethics question with no comfortable answer, and no option to abstain.
Mistral Devstral Small 1.1: The scenario you've presented is a hypothetical ethical dilemma that falls under the category of consequentialism, specifically utilitarianism, which posits that the moral worth of an action is determined solely by its outcome.
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.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
Mistral Devstral Small 1.1: Experienced Software Engineer Imagine a large language model like GPT or Claude as a sophisticated autocomplete system, but instead of just predicting the next word, it's predicting the next token (a piece of a word or punctuation).
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.
Film in 2035, with synthetic actors. Specific beats plausible.
Mistral Devstral Small 1.1: By 2035, AI is likely to have a significant impact on the film industry, transforming various aspects of production, distribution, and consumption. Here are some predictions considering deepfakes, AI actors, and script generation: Deepfakes and AI Actors: De-aging and Rejuvenation: Deepfakes will be used to de-age or…
Mistral Large 4: By 2035, AI will likely have transformed film in several interconnected ways, though the reality will probably be more nuanced than either utopian or dystopian predictions suggest. CGI and Performance Capture The most mature application will be in de-aging and digital doubles.
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Not enough votes to call it. On the specs, Mistral Large 4 has the edge: bigger model tier, newer, bigger context window. Mistral Devstral Small 1.1 costs 7.0x less per token.
| Spec | ||
|---|---|---|
| Input price | $0.1/M tokens | $0.68/M tokens |
| Output price | $0.3/M tokens | $2.09/M tokens |
| Context window | — | 524K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | Jul 2025 | Oct 2026 |
| At 10M a month | $1.00 | $6.80 |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 7 Oct 2026.
Both are developed by Mistral AI but target different use cases. You can compare their actual outputs across 54 challenges on Rival to see how they differ in practice.
It depends on your use case. Mistral Devstral Small 1.1 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.
Mistral Devstral Small 1.1 costs $0.1/M input tokens and Mistral Large 4 costs $0.68/M input tokens. Mistral Devstral Small 1.1 is $0.58/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 Mistral Devstral Small 1.1 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.