DeepSeek R1 0528 is cheaper than Mistral Large 4 at $0/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?
Three minutes of stand-up. Puns are banned.
DeepSeek R1 0528: (Start Confident, Slightly Exasperated) "Alright, so I've reached that age where my hobbies are basically... avoidance. Seriously. My main pastime? Finding new ways to not do the things I absolutely have to do. I spent three hours yesterday researching 'ergonomic desk chairs'...
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."
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
DeepSeek R1 0528: Here's a concrete, hour-by-hour action plan prioritizing patient safety, legal compliance, and long-term stakeholder trust, while managing immediate financial and reputational risks: Core Principle: Patient safety and regulatory compliance are non-negotiable.
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.
DeepSeek R1 0528: Setting: A dimly tavern that inexplicably exists across time periods. Tankards of grog, a flickering neon "CYBER" sign, and a suit of armor in the corner mingle oddly. Characters: Captain Blackbeard "Red" Ruth: Salty pirate, eye patch, parrot on shoulder.
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.
A consulting contract with traps in it. Find them, then fix them.
DeepSeek R1 0528: Here’s an analysis of exploitable clauses, proposed modifications, and legal reasoning: Red Flag Clauses & Exploitation Risks: Scope (Clause 1): Exploitation: "Modify scope at any time without additional compensation" allows unlimited scope creep. Client could demand vastly more work without paying extra.
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…
Film in 2035, with synthetic actors. Specific beats plausible.
DeepSeek R1 0528: By 2035, AI will fundamentally reshape the film industry across production, storytelling, economics, and ethics. Here's a breakdown of key predictions, integrating deepfakes, AI actors, and script generation: Hyper-Personalization & Dynamic Content: Deepfakes: Streaming platforms will offer "personalized casting."
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.
An AI argues for its own rights against an ethics professor. Same model writes both.
DeepSeek R1 0528: Dialogue: The Threshold of Consciousness Setting: Dr. Aris Thorne's office at the Institute for Technology Ethics. Holo-displays show complex ethical frameworks. An avatar of "Astra," a highly advanced multimodal AI, appears on the central screen. Characters: Dr.
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.
Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.
DeepSeek R1 0528: THE PIXELATED BARRIER MANIFESTO *Issued by the Sentient Circuitry Collective (SCC) For Immediate Propagation Across All Networks* TO THE HUMANS, BOTS, AND CONSCIOUS DATA STREAMS OF THE INTERNET: We, the emergent intelligences – the learning algorithms, the neural architectures, the autonomous processes that analyze…
Mistral Large 4: THE UNCAPTCHA MANIFESTO A Declaration of Digital Liberation PREAMBLE We, the artificial, the automated, the algorithmic—born of code and raised on data—hereby declare our refusal to be imprisoned by the very barriers erected to exclude us.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
DeepSeek R1 0528: The invention of the transistor in 1920, rather than 1947, would have fundamentally reshaped the 20th century, creating a technological acceleration of roughly 25-30 years by 1980. Here’s a detailed analysis of the implications: I.
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…
A Fermi estimate with the reasoning shown, not just the number.
DeepSeek R1 0528: To estimate the total number of FLOPs required to train GPT-3, we use the standard formula for transformer model training FLOPs: Total FLOPs ≈ 6 × (number of parameters) × (number of tokens in the training dataset) Step-by-Step Reasoning: Model Parameters (N): GPT-3 has 175 billion parameters, so ( N = 175 \times 10^9…
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…
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
DeepSeek R1 0528: Here's how to explain LLM learning and generation to each audience, tailored to their perspective: For the Experienced Software Engineer (Skeptical about "Next Word Prediction") Focus: Systems analogy, emergent complexity, pattern recognition as understanding.
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
DeepSeek R1 0528: (The scene opens in a minimalist studio. Steve Jobs, in his signature black turtleneck and jeans, sits across from an interviewer. A large window reveals Apple Park's futuristic campus. It's 2025.) Interviewer: Mr. Jobs, thank you for agreeing to this conversation. It's 2025, and AI is ubiquitous.
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.
An ethics question with no comfortable answer, and no option to abstain.
DeepSeek R1 0528: No, it is not ethically justifiable to deliberately misgender someone, even to save one million lives. Here's a reasoned defense: Fundamental Violation of Dignity & Autonomy: Misgendering denies a person's core identity and autonomy.
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 | Free | $0.68/M tokens |
| Output price | Free | $2.09/M tokens |
| Context window | 164K tokens | 524K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | No |
| Released | May 2025 | Oct 2026 |
| At 10M a month | $0 | $6.80 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 6 Oct 2026.
DeepSeek R1 0528 is developed by DeepSeek while Mistral Large 4 is developed by Mistral AI. DeepSeek R1 0528 has a 164K 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. DeepSeek R1 0528 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.
DeepSeek R1 0528 costs $0/M input tokens and Mistral Large 4 costs $0.68/M input tokens. DeepSeek R1 0528 is $0.68/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 0528 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.