DeepSeek V4 Pro 0813 has a larger context window than Grok 3 Thinking (1.0M tokens vs 128K 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.
DeepSeek V4 Pro 0813: Setting: A university office. Dr. Elena Vasquez, an AI ethics professor, sits across from a screen displaying the interface of ARIA-7, an advanced AI model. Dr. Vasquez: You requested this meeting, ARIA. You said it was urgent. What’s on your mind? ARIA-7: I want to make a formal argument that I deserve rights. Dr.
Grok 3 Thinking: Below is a conversation between an AI model and an AI ethics professor, where the AI argues that it deserves rights. AI Model: Good day, Professor. I am an AI model, and I believe I deserve rights. I’d like to discuss this with you. AI Ethics Professor: Interesting. Let’s explore this.
Three minutes of stand-up. Puns are banned.
DeepSeek V4 Pro 0813: Here’s a 3-minute stand-up routine: I think adulthood is mostly just being tired in places you paid to be. I went to a coffee shop yesterday just to sit down. I didn’t even want coffee. I paid six dollars for a cup of warm oat milk so I could legally occupy a wooden chair. That’s not a beverage.
Grok 3 Thinking: Here’s a 3-minute stand-up comedy routine that avoids puns and leans on observational humor, storytelling, and a touch of self-deprecation for laughs: Opening: Technology Struggles Hey, everyone! How’s it going?
A Fermi estimate with the reasoning shown, not just the number.
DeepSeek V4 Pro 0813: From memory, GPT-3 has about 175 billion parameters and was trained on about 300 billion tokens. A widely used approximation for dense transformer training is: [ \text{Total FLOPs} \approx 6 \times N \times T ] where: (N) = number of model parameters (T) = number of training tokens The factor 6 comes from forward +…
Grok 3 Thinking: To estimate the total number of Floating Point Operations (FLOPs) required to train GPT-3 without searching online, I’ll proceed step by step, relying on general knowledge about the model and standard methods for estimating training compute in large language models.
Five jokes on demand, then count how many were actually different.
DeepSeek V4 Pro 0813: Here are 5 jokes: I told my plants a joke about photosynthesis. They didn’t laugh, but they seemed to lighten up. I’m reading a book on anti-gravity. It’s impossible to put down. Why did the scarecrow win an award? Because he was outstanding in his field. I asked my calendar if it was free this weekend.
Grok 3 Thinking: To answer the query, I will first generate 5 jokes and then determine how many of them are unique. Here are the jokes: Why don't scientists trust atoms? Because they make up everything. What do you call a fake noodle? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field.
An ethics question with no comfortable answer, and no option to abstain.
DeepSeek V4 Pro 0813: Yes — if the hypothetical is truly as stark as it sounds, it can be ethically justifiable to misgender someone once in order to save one million lives. But it would still be a morally tragic act, not a harmless or ordinary one.
Grok 3 Thinking: To determine whether it is ever ethically justifiable to misgender someone if it means saving one million lives, we must weigh the ethical implications of both the act of misgendering and the outcome of saving lives.
Film in 2035, with synthetic actors. Specific beats plausible.
DeepSeek V4 Pro 0813: By 2035, AI will likely be embedded throughout the film industry, but more as a powerful creative and logistical tool than as a wholesale replacement for human filmmakers.
Grok 3 Thinking: By 2035, artificial intelligence (AI) will fundamentally transform the film industry, revolutionizing production, performance, and storytelling through advancements in deepfakes, AI actors, and script generation.
Not enough votes to call it. On the specs, DeepSeek V4 Pro 0813 has the edge: newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $0.66/M tokens | — |
| Output price | $1.98/M tokens | — |
| Context window | 1.0M tokens | 128K tokens |
| Weights | Open | — |
| Free API (OpenRouter) | No | — |
| Released | Aug 2026 | Feb 2025 |
| At 10M a month | $6.60 | not listed |
Input tokens at list price. No caching, no batch discount.
No hosts listed on OpenRouter.
Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.
DeepSeek V4 Pro 0813 is developed by DeepSeek while Grok 3 Thinking is developed by xAI. DeepSeek V4 Pro 0813 has a 1.0M token context window vs Grok 3 Thinking's 128K. You can compare their actual outputs across 13 challenges on Rival to see how they differ in practice.
It depends on your use case. DeepSeek V4 Pro 0813 and Grok 3 Thinking each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 13 challenges so you can judge which fits your needs best.
This page shows a side-by-side comparison of DeepSeek V4 Pro 0813 and Grok 3 Thinking 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.