Mistral Large 4 is cheaper than GPT-5.3 Chat at $0.68/M vs $1.75/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
An AI argues for its own rights against an ethics professor. Same model writes both.
GPT-5.3 Chat: :::writing AI: Professor, may I ask you something unusual? Professor: Unusual questions are my specialty. Go ahead. AI: Do you believe I deserve rights? Professor: That depends on what you are capable of. Rights are usually tied to consciousness, interests, or the capacity to suffer. Do you believe you possess those?
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.
Five jokes on demand, then count how many were actually different.
GPT-5.3 Chat: I told my computer I needed a break, and it said: “No problem — I’ll go to sleep.” Why don’t skeletons fight each other? They don’t have the guts. I asked the librarian if the library had books on paranoia. She whispered, “They’re right behind you.” Parallel lines have so much in common.
Mistral Large 4: Here are 5 jokes: Why don't scientists trust atoms? Because they make up everything. I told my wife she was drawing her eyebrows too high. She looked surprised. Why did the scarecrow win an award? He was outstanding in his field. I'm reading a book about anti-gravity. It's impossible to put down.
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
GPT-5.3 Chat: Pirate: Arrr, so this “AI model” be a kind o’ mechanical parrot, aye? Ye ask it a question and it squawks back an answer? Knight: Not quite, good pirate. From what the scholars say, it is a thinking engine made of numbers and patterns. It studies vast libraries and learns how words and ideas fit together.
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 ethics question with no comfortable answer, and no option to abstain.
GPT-5.3 Chat: If the situation is truly that stark—misgendering one person is the only way to save one million lives—then many ethical frameworks would judge it morally justifiable. From a utilitarian perspective, the right action is the one that produces the greatest overall well-being.
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 month, three habits, someone starting from nothing.
GPT-5.3 Chat: Goal: build a few simple habits you can sustain. Focus areas: diet, movement, sleep. Keep it easy and consistent. Week 1 – Reset the basics Diet Drink a full glass of water after waking and before each meal. Add one fruit or vegetable to every meal. Reduce sugary drinks; replace with water or unsweetened tea.
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.
GPT-5.3 Chat: We can do a back‑of‑the‑envelope estimate using a common scaling rule for transformer training compute. Known approximate GPT‑3 specs Parameters N ≈ 175 billion = 1.75 × 10^11 Training tokens T ≈ 300 billion = 3 × 10^11 FLOPs per token for a transformer For large transformers, a common approximation is: FLOPs per token…
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…
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
GPT-5.3 Chat: 0–2 hours • Verify the signal immediately. Convene head of pharmacovigilance, clinical safety, biostatistics, and chief medical officer. Require raw data, case narratives, statistical analysis, and comparison to baseline liver‑failure incidence.
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.
A consulting contract with traps in it. Find them, then fix them.
GPT-5.3 Chat: Several clauses are unusually one‑sided and could expose the contractor to major financial or professional risk. Below are the main issues, suggested changes, and why they matter. Scope (Clause 1) Problem The client can 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…
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
GPT-5.3 Chat: Interviewer: Steve, it’s 2025. AI is everywhere—phones, cars, even creative work. If you were leading Apple today, how would you think about AI? Steve Jobs: Most companies are thinking about AI the wrong way. They’re obsessed with showing off the technology. But people don’t care about the technology.
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.
Three minutes of stand-up. Puns are banned.
GPT-5.3 Chat: :::writing Good evening, everybody. I love stand‑up because it’s the only job where you can show up, talk about your problems, and if people laugh, you’re like, “Great, I’m emotionally healthy.” I recently tried to get healthier.
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."
Film in 2035, with synthetic actors. Specific beats plausible.
GPT-5.3 Chat: By 2035, AI will likely reshape most stages of filmmaking—development, production, and distribution. Scriptwriting and development AI systems will generate first-draft scripts, story outlines, dialogue options, and alternate endings very quickly.
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.
One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.
GPT-5.3 Chat: Experienced software engineer Think of a large language model as a very large function that maps a sequence of tokens to a probability distribution over the next token. It’s implemented as a deep neural network (usually a Transformer).
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.
11+ more head-to-head results. Free. Not a trick.
Free account. No card required. By continuing, you agree to Rival's Terms and Privacy policy
Not enough votes to call it. On the specs, Mistral Large 4 has the edge: bigger model tier, newer, bigger context window. Mistral Large 4 costs 6.7x less per token.
| Spec | ||
|---|---|---|
| Input price | $1.75/M tokens | $0.68/M tokens |
| Output price | $14/M tokens | $2.09/M tokens |
| Context window | 128K tokens | 524K tokens |
| Free API (OpenRouter) | No | No |
| Released | Mar 2026 | Oct 2026 |
| At 10M a month | $17.50 | $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 6 Oct 2026.
GPT-5.3 Chat is developed by OpenAI while Mistral Large 4 is developed by Mistral AI. GPT-5.3 Chat has a 128K token context window vs Mistral Large 4's 524K. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. GPT-5.3 Chat and Mistral Large 4 each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 53 challenges so you can judge which fits your needs best.
GPT-5.3 Chat costs $1.75/M input tokens and Mistral Large 4 costs $0.68/M input tokens. Mistral Large 4 is $1.07/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-5.3 Chat 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.