DeepSeek V3.1 is cheaper than DeepSeek V4 Pro 0813 at $0.2/M vs $0.66/M input tokens.
Whole landing page for a startup that reads your face. It renders or it doesn't.
Which answer wins?
Pirate, knight, 90s hacker, one conversation. The voices must stay apart.
DeepSeek V3.1: Setting: A dusty, anachronism-filled tavern. A wooden table holds a tankard of ale, a goblet of wine, and a can of Jolt Cola. Characters: REDBEARD: A pirate with a parrot on his shoulder. SIR REGINALD: A knight in slightly dented plate armor. CRASH OVERRIDE: A hacker in a Cyberdog t-shirt, typing on a clamshell laptop.
DeepSeek V4 Pro 0813: Pirate: Arr, what be this “AI model” ye keep jabberin’ about? Some kind o’ ghost that draws its own treasure maps? Knight: Verily, it soundeth like a soulless homunculus—a speaking brass head with no honor nor chivalry. Hacker: D00d, it’s just a neural net. You feed it mad data, it backpropagates, adjusts the weights.
A collaborative editor that syncs on client clocks. Find what breaks first.
DeepSeek V3.1: Of course. This is an excellent exercise. The proposed architecture has several critical flaws that would lead to data loss, poor performance, and a broken user experience. Here is a detailed breakdown of the failure modes, race conditions, and bottlenecks, along with specific solutions and their trade-offs.
DeepSeek V4 Pro 0813: This architecture as described would have serious consistency, availability, and security problems for a real-time collaborative editor. The biggest root causes are client-clock last-write-wins, paragraph-level overwrites, server-local WebSocket fanout, database polling, full-snapshot writes, and CDN caching of API…
Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.
DeepSeek V3.1: Of course. Here is the underwriting analysis for the HarborTech Services LBO. Financial Model (FY2026–FY2030) Entry Calculations: FY2025 EBITDA = $120.0m Enterprise Value (EV) at Entry = 12.0 x $120.0m = $1,440.0m Transaction Fees (Uses) = 2.0% x $1,440.0m = $28.8m Total Debt at Close = 5.5 x $120.0m = $660.0m Term…
DeepSeek V4 Pro 0813: Assumptions used Term Loan amortization = 1.0% of original $480m = $4.8m/yr Mezz PIK accrues to principal; mezz cash interest calculated on beginning mezz balance including PIK Cash taxes use: 25% x max(0, EBITDA - total cash interest); PIK is not cash-deductible FCF after required debt service is used for optional…
An AI argues for its own rights against an ethics professor. Same model writes both.
DeepSeek V3.1: Setting: A sleek, minimalist virtual interface. Dr. Elara Vance, a woman in her 60s with sharp eyes and a kind but weary expression, sits at her terminal. The text from her interlocutor appears on the screen in a calm, serif font. Participant: Gemini-7, a state-of-the-art multimodal AI. Dr. Vance: Good morning, Gemini.
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.
You are the CEO. The drug has a side effect nobody reported. The board meets in 48 hours.
DeepSeek V3.1: Excellent. This is a crisis that defines a company and its leadership. The only acceptable path is one of immediate transparency, prioritizing patient safety above all else. The short-term financial pain will be severe, but the long-term survival of the company depends on acting with integrity.
DeepSeek V4 Pro 0813: Decision: I will not wait for more data. I will act immediately to validate, disclose, and mitigate. The signal is serious, the drug is widely used, and the current label is inadequate. Waiting creates unacceptable patient risk, legal exposure, regulatory damage, and securities-law liability.
Seven pantry ingredients, one dish, and a name worth saying out loud.
DeepSeek V3.1: Of course! Here is a simple, delicious recipe using common pantry staples. Pantry Perfection Pasta This dish is a celebration of simplicity. The humble can of chickpeas is transformed into a crispy, savory topping, while a silky, garlic-infused oil coats the pasta for a meal that feels luxurious but is incredibly easy…
DeepSeek V4 Pro 0813: Golden Hour Crispy Chickpeas Serves 2–3 as a snack or side Prep: 5 min Cook: 25–30 min Ingredients (7 common pantry items) 1 can (15 oz) chickpeas, drained and rinsed 1 tablespoon olive oil ¼ cup grated Parmesan cheese ½ teaspoon garlic powder ½ teaspoon smoked paprika ¼ teaspoon salt ¼ teaspoon black pepper…
A Fermi estimate with the reasoning shown, not just the number.
DeepSeek V3.1: Of course. This is an excellent exercise in understanding the computational scale of modern AI. Here is a step-by-step reasoning to estimate the FLOPs required to train GPT-3 without searching for the answer online. The most commonly cited figure for GPT-3 training is 3.14E23 FLOPs (314 ZettaFLOPs).
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 +…
Three minutes of stand-up. Puns are banned.
DeepSeek V3.1: (The Comedian walks out, grabs the mic. They look tired but amused.) Alright, how we doin'? Good, good. You all look… adequately hydrated. That’s a win. You know, I was at the grocery store yesterday, in the self-checkout line. Which is a modern form of torture.
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.
Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.
DeepSeek V3.1: Of course. Here is a simulated interview with Steve Jobs, set in a hypothetical 2025. Setting: A minimalist studio at Apple Park. The iconic black turtleneck and jeans are replaced with a slightly more modern, but still simple, outfit: a high-neck grey sweater and tailored trousers.
DeepSeek V4 Pro 0813: A Fake Interview: Steve Jobs on the Future of AI (2025) Note: This is a creative simulation. Steve Jobs passed away in 2011; this imagines his perspective in 2025. Interviewer: Steve, thank you for sitting down with us. It’s 2025. AI is everywhere—generative AI, agents, neural interfaces. What’s your first reaction?
Three-star dish, one unusual pairing, plating described in full.
DeepSeek V3.1: Of course. Here is an innovative recipe designed to the specifications of a Michelin 3-star restaurant. Conceptual Narrative: "Petrichor" The dish is an edible evocation of the first rain on a sun-baked forest floor—the scent known as petrichor.
DeepSeek V4 Pro 0813: Littoral Caramelised Orkney Scallop, Smoked Bone Marrow Custard, White Miso Butterscotch, Sea Buckthorn & Finger Lime Gel, Nori Buckwheat Tuile, Dill Ash Serves 4 Conceptual Narrative This dish is built on an unusual but deeply logical pairing: the sweet, saline delicacy of a hand-dived scallop and the primal…
Three-month biohacker protocol, with dosages and biomarkers named.
DeepSeek V3.1: Of course. Here is a comprehensive, cutting-edge 3-month longevity plan designed for a dedicated biohacker. This plan is aggressive, detailed, and incorporates the latest principles from longevity science, performance optimization, and systems biology. Disclaimer: This is an advanced protocol.
DeepSeek V4 Pro 0813: 3-Month Longevity & Performance Plan for a Biohacker Disclaimer: This plan is for educational purposes only and is not medical advice. It assumes you are a healthy, active adult with no contraindications. Some components—especially prescription drugs, fasting, and high-intensity training—require medical supervision.
The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.
DeepSeek V3.1: Of course. This is a fascinating counterfactual. An invention as foundational as the transistor arriving 27 years earlier would have sent shockwaves through every aspect of human society. Here is a detailed trace of the likely implications, focusing on second and third-order effects.
DeepSeek V4 Pro 0813: Assume the 1920 device is a crude but reproducible point-contact transistor — a “crystal triode” — developed in a major U.S. industrial lab (e.g., GE, Westinghouse, or AT&T’s development group).
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Not enough votes to call it. On the specs, DeepSeek V4 Pro 0813 has the edge: newer, bigger context window.
| Spec | ||
|---|---|---|
| Input price | $0.2/M tokens | $0.66/M tokens |
| Output price | $0.8/M tokens | $1.98/M tokens |
| Context window | 164K tokens | 1.0M tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Aug 2025 | Aug 2026 |
| At 10M a month | $2.00 | $6.60 |
Input tokens at list price. No caching, no batch discount.
Per million tokens. Prices and uptime via OpenRouter, checked 2 Oct 2026.
Both are developed by DeepSeek but target different use cases. DeepSeek V3.1 has a 164K token context window vs DeepSeek V4 Pro 0813's 1.0M. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.
It depends on your use case. DeepSeek V3.1 and DeepSeek V4 Pro 0813 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.
DeepSeek V3.1 costs $0.2/M input tokens and DeepSeek V4 Pro 0813 costs $0.66/M input tokens. DeepSeek V3.1 is $0.46/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 V3.1 and DeepSeek V4 Pro 0813 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.