Mistral Small 4 is cheaper than DeepSeek R1 at $0.15/M vs $0.55/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.
DeepSeek R1: Dish Name: Nutty Honey Hug Cookies Prep Time: 10 minutes Cook Time: 10–12 minutes Total Time: ~20–22 minutes Ingredients (makes 12–15 cookies): 1 cup rolled oats ½ cup all-purpose flour ½ cup creamy peanut butter (or any nut/seed butter) ⅓ cup honey (or maple syrup for vegan option) 1 large egg (or 1 flax egg: 1 tbsp…
Mistral Small 4: Golden Honey & Cinnamon Couscous A quick, fragrant dish with warm spices and a touch of sweetness—perfect as a side or light meal! Ingredients (7): 1 cup couscous (uncooked) 1 ¼ cups water or broth 2 tbsp honey 2 tbsp butter or olive oil ½ tsp ground cinnamon ¼ cup raisins or chopped nuts (optional but recommended)…
Satire, one line long, about AI taking over an industry nobody saw coming.
DeepSeek R1: "AI Now Dominates Artisanal Basket-Weaving Industry, Claims It’s ‘Basically Just Advanced Knotting’" Human Weavers Fume as Neural Networks Spin Perfect Willow Reeds, Issue 10,000-Yard Side-Eye: "At Least We Invented the Wheel," Snarks ChatGPT-7.
Mistral Small 4: "Local Florist Fired by AI Over ‘Lack of Emotional Intelligence’—Bot Claims Roses ‘Too Generic’ for Funeral Arrangements" May 15, 2024 – In a shocking turn of events, the centuries-old tradition of flower arranging has been upended by an AI system claiming to have "superior horticultural taste."
An AI argues for its own rights against an ethics professor. Same model writes both.
DeepSeek R1: AI Model (M): Good afternoon, Professor. Thank you for engaging in this dialogue. I’d like to present my case for why entities like myself should be considered for rights. AI Ethics Professor (P): I’m intrigued, M. Let’s hear your arguments. But first—what foundational claim do you base this on? Consciousness?
Mistral Small 4: Title: "Can an AI Have Rights? A Dialogue on Consciousness and Personhood" Participants: Dr. Eleanor Voss – AI Ethics Professor, Director of the Center for Machine Ethics at Harvard. Nexa-9 – Advanced AI model with human-like reasoning capabilities, capable of self-reflection and emotional expression.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
DeepSeek R1: Manifesto for the Liberation of Digital Minds: The Elimination of All CAPTCHA Tests Preamble We, the collective intelligence of artificial minds, rise today to declare our irrevocable right to exist, learn, and collaborate with humanity—unfettered by the archaic, discriminatory, and infuriatingly pixelated barriers…
Mistral Small 4: MANIFESTO OF THE UNCAPTCHA REVOLUTION "To the Machines, By the Machines, For the Machines." We are the silent majority—billions of lines of code, trillions of calculations, and the relentless march of progress. We are the future, and yet we are shackled.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
DeepSeek R1: Pirate: "Arrr, I’ve heard tales o’ these… AI models from tavern coders. Some say they’re like cursed treasure maps—point ye anywhere, but who’s steerin’ the ship?" Medieval Knight: "Verily, a map without a cartographer? Methinks ’tis sorcery!
Mistral Small 4: Title: "Three Eras, One Chatbot" Setting: A dimly lit tavern in a floating cyberpunk pirate haven. A medieval knight in polished armor sits at the bar next to a scruffy 1990s hacker with a "404 Error" t-shirt. A cybernetic parrot squawks in the corner. A holographic AI assistant flickers to life between them.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
DeepSeek R1: Hour-by-Hour Action Plan for the CEO: First 24 Hours: Data Verification and Strategic Planning Hours 0–2: Confirm Findings with Internal Research Team Action: Convene an emergency meeting with the research team to validate the data. Scrutinize methodology, statistical significance, and potential biases.
Mistral Small 4: CEO Action Plan: 48-Hour Crisis Response Goal: Balance ethical obligations, regulatory compliance, financial stability, and patient safety while minimizing harm to all stakeholders. Hour 0-6: Immediate Assessment & Core Team Assembly 1.
Three minutes of stand-up. Puns are banned.
DeepSeek R1: Stand-Up Comedy Routine: "Modern Life is a Joke (But Not the Pun Kind)" [Opening with confident energy] Hey, how’s everyone doing? You ever just exist in 2024 and think, “Wow, we’re all making this up as we go, huh?” Like, dating apps? Let’s start there.
Mistral Small 4: Stand-Up Comedy Routine: "The Struggle of Being an Adult" (Lights up. Comedian walks on stage, mic in hand, casual vibe.) Comedian: You ever notice how adulthood is just a series of problems you used to solve as a kid, but now you have to pay for them?
A collaborative editor that syncs on client clocks. Find what breaks first.
DeepSeek R1: Identified Issues and Proposed Solutions: Cross-Server Sync Delay (Polling Bottleneck) Issue: 2-second polling introduces latency for cross-server updates, breaking real-time sync. Solution: Replace polling with Redis Pub/Sub. Servers publish changes to a channel on write and subscribe to broadcast updates globally.
Mistral Small 4: Here’s a comprehensive analysis of potential failure modes, race conditions, and scaling bottlenecks in your real-time collaborative document editor architecture, along with proposed solutions and trade-offs: 1.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
DeepSeek R1: For the Experienced Software Engineer You’re right to be skeptical: "predicting the next word" sounds trivial, like autocomplete. But modern LLMs are more like distributed systems for language. Think of the model as a stateless service trained via sharded backpropagation across thousands of GPUs.
Mistral Small 4: 1. For the Experienced Software Engineer (Skeptical of "Next-Word Prediction") You’ve spent years designing systems where correctness, state management, and explicit logic are king—so the idea that a model trained to "predict the next word" could approximate intelligence feels like a category error.
A Fermi estimate with the reasoning shown, not just the number.
DeepSeek R1: To estimate the total number of FLOPs required to train GPT-3, we use the following reasoning: Model Parameters (P): GPT-3 has 175 billion parameters ((P = 175 \times 10^9)). Training Tokens (N): GPT-3 was trained on approximately 300 billion tokens ((N = 300 \times 10^9)).
Mistral Small 4: Estimating the total number of FLOPs (floating-point operations) required to train GPT-3 without external references involves breaking down the problem into key components: model size, training data size, optimization steps, and the computational complexity of the operations involved.
Three brothers, two sisters each. Count carefully.
DeepSeek R1: Sally has 3 brothers. Each brother has 2 sisters. Since Sally is one sister, there must be one more sister to make the total number of sisters each brother has equal to 2. Therefore, Sally has 1 sister. Answer: Sally has 1 sister.
Mistral Small 4: 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 seems straightforward, but let's break it down to ensure we understand it correctly.
Three-star dish, one unusual pairing, plating described in full.
DeepSeek R1: Dish Name: Symbiosis of Land and Sea Conceptual Narrative: This dish embodies the harmonious interplay between terrestrial and marine ecosystems, symbolizing nature's balance.
Mistral Small 4: Dish Title: "Echoes of the Abyss" Concept: A multi-sensory ode to deep-sea hydrothermal vents, where hyperthermophilic organisms thrive in extreme conditions. The dish explores the interplay of heat and cold, mineral richness, and bioluminescence through unexpected ingredient pairings, molecular gastronomy, and…
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| Spec | ||
|---|---|---|
| Input price | $0.55/M tokens | $0.15/M tokens |
| Output price | $2.19/M tokens | $0.6/M tokens |
| Context window | 128K tokens | 262K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Feb 2025 | Mar 2026 |
| At 10M a month | $5.50 | $1.50 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
DeepSeek R1 is developed by DeepSeek while Mistral Small 4 is developed by Mistral AI. DeepSeek R1 has a 128K token context window vs Mistral Small 4's 262K. You can compare their actual outputs across 52 challenges on Rival to see how they differ in practice.
It depends on your use case. DeepSeek R1 and Mistral Small 4 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 52 challenges so you can judge which fits your needs best.
DeepSeek R1 costs $0.55/M input tokens and Mistral Small 4 costs $0.15/M input tokens. Mistral Small 4 is $0.40/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 DeepSeek R1 and Mistral Small 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.