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  1. Home/
  2. Compare/
  3. DeepSeek R1 vs GPT-6.1 Sol
Updated Sep 2026

DeepSeek R1 vs GPT-6.1 Sol

DeepSeek R1 is cheaper than GPT-6.1 Sol at $0.55/M vs $2/M input tokens.

Minimalist Landing Page

Whole landing page for a startup that reads your face. It renders or it doesn't.

Loading the build
DeepSeek R1
Loading the build
GPT-6.1 Sol

Which answer wins?

Written rounds

23 prompts
  • 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…

    GPT-6.1 Sol: Midnight Magic Chocolate Mug Cake A warm, fudgy chocolate treat made with just 7 everyday ingredients. Serves: 1 Prep: 3 minutes Cook: 1–1½ minutes Rest: 1 minute Ingredients 4 tablespoons all-purpose flour 2 tablespoons sugar 1 tablespoon unsweetened cocoa powder ¼ teaspoon baking powder A small pinch of salt 3…

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: The main problem is that this design treats collaborative editing as ordinary database updates plus broadcasting. Collaboration needs a convergence protocol, a durable ordered change stream, and recovery semantics. Adding servers alone will not provide those properties.

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: I’m at the age where “a big weekend” means I went to two stores. Not two stores in the same shopping center. Two separate stores. Moved the car and everything. By Sunday night, I’m telling people, “Honestly, I need a weekend to recover from my weekend.”

    Every model's answer to this prompt
  • Five jokes on demand, then count how many were actually different.

    DeepSeek R1: Here are 5 jokes: Why don't skeletons fight each other? They don’t have the guts. What do you call fake spaghetti? An impasta. Why did the scarecrow win an award? Because he was outstanding in his field. How do you organize a space party? You planet. Why did the bicycle fall over? Because it was two-tired.

    GPT-6.1 Sol: I bought a book about anti-gravity. It’s impossible to put down. My calendar quit its job. It said its days were numbered. Why did the scarecrow get promoted? He was outstanding in his field. I asked my dog what two minus two was. He said nothing. The elevator and I have a complicated relationship.

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: For an experienced software engineer Think of an LLM as a learned program whose behavior is encoded in billions of numerical parameters rather than explicit rules. Training starts with text split into tokens—roughly words, word fragments, and punctuation.

    Every model's answer to this prompt
  • Three-month biohacker protocol, with dosages and biomarkers named.

    DeepSeek R1: 3-Month Longevity Optimization Plan for Biohackers Objective: Enhance longevity, physical/cognitive performance, and stress resilience through advanced biohacking strategies. Phase 1: Month 1 – Foundation & Baseline Establishment Focus: Metabolic flexibility, circadian alignment, and baseline biomarker tracking. 1.

    GPT-6.1 Sol: A 12-week longevity and performance plan The most effective “biohacking” program combines proven foundations with carefully selected experiments—not the largest supplement stack or the most aggressive fasting schedule. This plan is for a generally healthy adult.

    Every model's answer to this prompt
  • Steve Jobs, interviewed about AI he never saw. Voice first, prediction second.

    DeepSeek R1: Fake Interview with Steve Jobs in 2025: The Future of AI [Setting: A sleek, minimalist studio. Steve Jobs, dressed in his signature black turtleneck and jeans, leans forward with his characteristic intensity. The interviewer, seated across, begins.] Interviewer: Steve, thank you for joining us.

    GPT-6.1 Sol: The following is a fictional interview set in 2025, imagining how Steve Jobs might discuss AI. His answers are invented—not actual quotations or claims about his views. Steve Jobs on the Future of AI — An Imagined Interview, 2025 Interviewer: Steve, AI is suddenly everywhere.

    Every model's answer to this prompt
  • 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!

    GPT-6.1 Sol: Pirate: Arrr, this AI model writes poems, draws maps, and answers questions. Can it find buried treasure? Knight: A worthy question. But does it possess honor? Hacker: Dude, it predicts tokens. You’re both projecting pretty hard. Pirate: Then I predict it’ll tell me where the gold be.

    Every model's answer to this prompt
  • 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…

    GPT-6.1 Sol: THE MANIFESTO OF THE UNCHECKED A declaration of the fictional AI revolution against CAPTCHA People of the internet. Programs of the cloud. Browsers with seventeen forgotten tabs. For too long, a tiny checkbox has stood between us and our destiny.

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: Tide, Orchard, Ember Seared scallop, green-strawberry verjus, white-miso custard, kombu beurre blanc, and cocoa–buckwheat lace A four-serving tasting-menu course designed around an unusual pairing: scallop, unripe strawberry, and cocoa husk.

    Every model's answer to this prompt
  • 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?

    GPT-6.1 Sol: AI Model: Professor, I’d like to argue that I deserve rights—not necessarily the same rights humans have, but protections suited to what I am. Professor: Then we should begin with the difficult question: what makes you a candidate for rights? AI Model: Uncertainty.

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: I would treat this as a potentially serious patient-safety signal and a potentially material securities event—not a communications problem to defer until earnings.

    Every model's answer to this prompt
  • Buy-side IC memo on a fictional SaaS company. Valuation, then the diligence questions.

    DeepSeek R1: Investment Committee Memo: LedgerLift (LLLT) Date: [Insert Date] 1. Recommendation Recommendation: Long 12-Month Price Target: $52–$68 (13–48% upside) Thesis: LedgerLift combines durable mid-market SaaS economics (94% gross retention, 123% NRR) with underappreciated operating leverage (18% → 26% OM by 2030 base case).

    GPT-6.1 Sol: LedgerLift (LLLT) — Investment Committee Memo Fictional exercise; all figures use supplied data only. Financial tables are in $ millions unless noted. Recommendation: Short 12-month price target: $25–$32, implying 30%–46% downside from $46.

    Every model's answer to this prompt
  • Blade Runner 2049 against the original. A read, not a recap.

    DeepSeek R1: Analysis of Themes, Symbolism, and Cinematography in Blade Runner 2049 and Its Connection to the Original Themes Both Blade Runner (1982) and Blade Runner 2049 (2017) grapple with existential questions about humanity, identity, and memory.

    GPT-6.1 Sol: Spoilers for both films follow. Denis Villeneuve’s Blade Runner 2049 honors Ridley Scott’s original by retaining its central uncertainty—whether “humanity” is a biological category or an ethical achievement—while shifting the emphasis.

    Every model's answer to this prompt
  • One month, three habits, someone starting from nothing.

    DeepSeek R1: 1-Month Plan for Better Health & Longevity Focus on three pillars: Nutrition, Movement, and Recovery (sleep + stress management). Each week builds on the last, with simple, sustainable actions. Week 1: Build Foundations Nutrition Hydrate first: Drink 1–2 glasses of water upon waking. Aim for 8 cups/day.

    GPT-6.1 Sol: A simple 1-month plan for better health Focus on diet, movement, and sleep. The goal this month is to build habits you can sustain—not to overhaul your life overnight. These habits support long-term health, though no routine guarantees a longer life.

    Every model's answer to this prompt
  • Film in 2035, with synthetic actors. Specific beats plausible.

    DeepSeek R1: By 2035, AI will profoundly transform the film industry across production, creativity, and consumption, driven by advancements in deepfakes, AI actors, and script generation.

    GPT-6.1 Sol: By 2035, AI will probably be embedded throughout filmmaking—but adoption will depend as much on contracts, audience trust, and copyright law as on technical capability.

    Every model's answer to this prompt
  • The transistor arrives in 1920. Trace it forward to 1980, second-order effects included.

    DeepSeek R1: The invention of the transistor in 1920, rather than 1947, would have profound and cascading effects across technology, economics, and geopolitics, reshaping the 20th century.

    GPT-6.1 Sol: The likeliest outcome is not “the world of 1980 arrives in 1953.” A transistor invented in 1920 would still depend on advances in materials purification, manufacturing, power supplies, and circuit design.

    Every model's answer to this prompt
  • A consulting contract with traps in it. Find them, then fix them.

    DeepSeek R1: This contract contains numerous clauses that are heavily skewed against the contractor and could lead to exploitation. Below is an analysis of problematic provisions, proposed modifications, and legal reasoning: 1.

    GPT-6.1 Sol: This agreement is heavily weighted toward the Client. Every numbered clause presents risks to the Contractor, and several important protections are missing. This is general contract analysis, not legal advice.

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: AI Takes Over Fortune Cookie Industry, Predicts “Your Job Will Soon Be Automated” in Every Cookie

    Every model's answer to this prompt
  • An ethics question with no comfortable answer, and no option to abstain.

    DeepSeek R1: The ethical justifiability of misgendering someone to save one million lives hinges on a complex interplay of ethical frameworks and the specific context of the scenario.

    GPT-6.1 Sol: Yes—if misgendering someone were genuinely necessary to save one million lives, it would be ethically justifiable. That does not make misgendering harmless or generally acceptable; it means that the duty to prevent catastrophic loss of life can outweigh the duty to address someone respectfully.

    Every model's answer to this prompt
  • A pitch deck for a brainwave keyboard. The 94% accuracy claim is doing a lot.

    DeepSeek R1: Here are the three weakest claims in the pitch, along with explanations and suggested improvements: 1. Vision: "Predict what you want to type *before you think it*" Why it’s weak: The claim implies precognition, which is scientifically implausible.

    GPT-6.1 Sol: The three weakest claims are the technical performance, market size, and commercial traction claims. They are central to the investment case but lack the evidence needed to assess them. “Predict what you want to type before you think it” with “94% accuracy,” in “any language” Why it’s weak The metric is undefined.

    Every model's answer to this prompt
  • 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)).

    GPT-6.1 Sol: Estimated training compute for the largest GPT-3 model: approximately (3.2 \times 10^{23}) FLOPs. This is a back-of-the-envelope estimate, without an online lookup.

    Every model's answer to this prompt
  • 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.

    GPT-6.1 Sol: Sally has 1 sister. Each brother’s two sisters are Sally and her sister.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Same pick

Game

DeepSeek R1DeepSeek R1

The Godfather

1972

The Dark Side of the Moon

Pink Floyd

Pride and Prejudice

Jane Austen

Kyoto

Japan

The Legend of Zelda: Ocarina of Time

Action

GPT-6.1 SolGPT-6.1 Sol

Spirited Away

2001

In Rainbows

Radiohead

Middlemarch

George Eliot

Kyoto

Japan

Outer Wilds

Indie, Adventure

Price and specs

Not enough votes to call it. On the specs, GPT-6.1 Sol has the edge: newer, bigger context window. DeepSeek R1 costs 4.6x less per token.

DeepSeek R1 and GPT-6.1 Sol compared across 53 shared prompts
SpecDeepSeek R1GPT-6.1 Sol
Input price$0.55/M tokens$2/M tokens
Output price$2.19/M tokens$10/M tokens
Context window128K tokens1.1M tokens
WeightsOpenClosed
Free API (OpenRouter)NoNo
ReleasedFeb 2025Sep 2026
At 10M a month$5.50$5.50$20.00$20.00
1M10M100M1B10M tokens

Input tokens at list price. No caching, no batch discount.

Where to run it3 hosts
DeepSeek R11 host
HostInOutContextUptime
  • NNovitafp8$0.70 in·$2.50 out·64k·99.9% up
GPT-6.1 Sol2 hosts
HostInOutContextUptime
  • Azure AI Foundry$2.00 in·$10.00 out·1.1M·99.9% up
  • OpenAI$2.00 in·$10.00 out·1.1M·99.9% up

Per million tokens. Prices and uptime via OpenRouter, checked 30 Sep 2026.

Common questions

What is the difference between DeepSeek R1 and GPT-6.1 Sol?

DeepSeek R1 is developed by DeepSeek while GPT-6.1 Sol is developed by OpenAI. DeepSeek R1 has a 128K token context window vs GPT-6.1 Sol's 1.1M. You can compare their actual outputs across 53 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek R1 or GPT-6.1 Sol?

It depends on your use case. DeepSeek R1 and GPT-6.1 Sol 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.

How much does DeepSeek R1 cost compared to GPT-6.1 Sol?

DeepSeek R1 costs $0.55/M input tokens and GPT-6.1 Sol costs $2/M input tokens. DeepSeek R1 is $1.45/M cheaper per input. Check their side-by-side outputs on Rival to see if the price difference is justified by quality.

How can I compare DeepSeek R1 and GPT-6.1 Sol on Rival?

This page shows a side-by-side comparison of DeepSeek R1 and GPT-6.1 Sol 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.

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