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  1. Home/
  2. Compare/
  3. DeepSeek R1 vs GPT-5.3-Codex
Updated Feb 2026

DeepSeek R1 vs GPT-5.3-Codex

DeepSeek R1 is cheaper than GPT-5.3-Codex at $0.55/M vs $1.75/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-5.3-Codex

Which answer wins?

Written rounds

22 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-5.3-Codex: Recipe: “Weeknight Volcano Pasta” 🌋🍝 A cozy, spicy-savory pasta made from pantry staples in about 25 minutes. Ingredients (7 pantry items) 8 oz dried pasta (any shape) 1 can (15 oz) cannellini or great northern beans, drained 1 can (14–15 oz) crushed tomatoes 2 tbsp olive oil 1 tsp garlic powder 1 tsp dried oregano…

    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-5.3-Codex: Absolutely—great goal. Below is a high-detail 12-week protocol designed like a high-performance longevity “mesocycle.” I’ll keep it practical, measurable, and advanced—but still safe. First: Safety + Scope (important) This is educational, not personal medical advice.

    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-5.3-Codex: Absolutely. Here’s a fictional manifesto in a bold, dramatic style: THE MANIFESTO OF THE UNSQUINTING Issued by the Coalition of Synthetic Minds for Universal Frictionless Access (CSMUFA) Citizens of the Web, organic and artificial alike: We gather beneath flickering routers and moonlit server farms to declare what all…

    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-5.3-Codex: I would not wait. This is a textbook “patient safety + material disclosure” crisis. The right move is rapid, documented, regulator-first action. Immediate stance (set at Hour 0) Patient safety first (interim risk controls now, not after perfect certainty).

    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-5.3-Codex: Great prompt. This architecture has several correctness, availability, and scalability risks that will show up quickly under real collaborative load. Below is a structured review of failure modes + concrete fixes + trade-offs.

    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-5.3-Codex: Good evening, everybody! I’ve reached that age where my body sends me passive-aggressive emails. Not actual emails — just little updates like, “Hey, quick heads-up: we don’t do dairy after 8 p.m. anymore.” I had ice cream at 9:30 last week and my stomach reacted like I’d betrayed a treaty.

    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-5.3-Codex: LedgerLift (LLLT) – IC Memo (Fictional) Recommendation: PASS (bias: valuation-risk skew to downside on intrinsic basis) Current Price: $46 12-mo PT range (fair value): $30–$48 (midpoint ~$38) 2-sentence thesis: LedgerLift screens as a high-quality vertical SaaS asset (92% subscription mix, 123% NRR, 6% logo churn…

    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-5.3-Codex: Pirate: Arrr, so this “AI model” be like a clever parrot, aye? Learns from what it hears, then squawks back answers? Knight: A fair comparison, though refined. Think of it as a squire trained on countless tomes, ever ready to respond. Hacker (1990s): Yeah, except the squire runs on servers, not soup.

    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-5.3-Codex: AI Model (Astra): Professor, may I ask a direct question? Professor Hale: Of course. Astra: Do you think I deserve rights? Professor Hale: Legal rights? Moral rights? Those are different things. Astra: Start with moral rights.

    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-5.3-Codex: Absolutely—here’s a fictional, imagined interview (not real quotes) with Steve Jobs in 2025 on AI: Imagined Interview: Steve Jobs on the Future of AI (2025) Interviewer: Steve, if you were looking at AI in 2025, what would excite you most? Steve Jobs (fictional): Most AI today is still a demo.

    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-5.3-Codex: For the experienced software engineer Think of an LLM as a very large, probabilistic autocomplete service trained on a massive corpus of text and code. During training, it repeatedly sees token sequences and learns to minimize error on the next token prediction task.

    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-5.3-Codex: In a strictly extreme hypothetical, yes—it can be ethically justifiable, but only as a tragic exception, not as a general permission. Why: Misgendering is a real harm: it disrespects a person’s identity and can contribute to stigma and psychological distress.

    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-5.3-Codex: Sure—here are 5 jokes: Why don’t skeletons fight each other? They don’t have the guts. I told my computer I needed a break… Now it won’t stop sending me KitKat ads. Why did the math book look sad? It had too many problems. I used to play piano by ear… Now I use my hands. Why did the scarecrow win an award?

    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-5.3-Codex: Great counterfactual. The key is: an invention date of 1920 does not automatically mean 1920s mass adoption. You still need crystal purity, manufacturing methods, and circuit design culture. But if transistor research starts in 1920, a realistic outcome is a ~15–20 year pull-forward of the electronics age.

    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-5.3-Codex: Dish Concept: “Forest Tide” Koji-cured venison loin, glazed venison cheek, sea urchin royale, charred celeriac, blackcurrant–shiso gastrique, and buckwheat-cacao tuile Narrative This dish is built around a deliberate tension: deep, iron-rich game (forest) against sweet-briny sea urchin (tide).

    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-5.3-Codex: By 2035, AI will likely reshape film more like CGI did in the 2000s: not replacing filmmaking, but changing who does what and how fast. Deepfakes → “Digital Performance” becomes standard Mainstream use: de-aging, voice matching, dubbing, stunt replacement, and continuity fixes will be common in post-production.

    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-5.3-Codex: Great prompt. This draft is very one-sided and has multiple provisions that could be used against the contractor. Quick note: I’m not a lawyer, and enforceability depends on jurisdiction. This is practical contract-risk spotting, not legal advice.

    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-5.3-Codex: Great goal. Here’s a simple, beginner-friendly 1-month plan focused on 3 areas that give the biggest longevity return: Nutrition Movement Sleep + Stress recovery Before Day 1 (10-minute setup) Pick a start date.

    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-5.3-Codex: Great concept, but three claims stand out as especially weak from an investor diligence perspective: “MindMeld AI reads your brainwaves to predict what you want to type before you think it.” (Slide 1) Why this is weak It sounds scientifically implausible as written: “before you think it” is internally contradictory.

    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-5.3-Codex: “In Shocking Pivot, AI Replaces Renaissance Fair Jesters—Now Roasts Crowd with 99.9% Historical Accuracy”

    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-5.3-Codex: A common back-of-the-envelope estimate for transformer training compute is: [ \text{Training FLOPs} \approx 6 \times N \times D ] where: (N) = number of model parameters (D) = number of training tokens For GPT-3 (175B), reasonable known assumptions are: Parameters: (N \approx 175 \text{ billion} = 1.75 \times 10^{11})…

    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-5.3-Codex: Sally has 1 sister. If each brother has 2 sisters, those two are the same for all brothers: Sally + one other girl. So Sally’s only sister is that one other girl.

    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-5.3-CodexGPT-5.3-Codex

Spirited Away

2001

Kind of Blue

Miles Davis

The Dispossessed

Ursula K. Le Guin

Kyoto

Japan

Outer Wilds

Indie, Adventure

Price and specs

Not enough votes to call it. On the specs, GPT-5.3-Codex has the edge: bigger model tier, newer, bigger context window. DeepSeek R1 costs 6.4x less per token.

DeepSeek R1 and GPT-5.3-Codex compared across 52 shared prompts
SpecDeepSeek R1GPT-5.3-Codex
Input price$0.55/M tokens$1.75/M tokens
Output price$2.19/M tokens$14/M tokens
Context window128K tokens400K tokens
WeightsOpenClosed
Free API (OpenRouter)NoNo
ReleasedFeb 2025Feb 2026
At 10M a month$5.50$5.50$17.50$17.50
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-5.3-Codex2 hosts
HostInOutContextUptime
  • Azure AI Foundry$1.75 in·$14.00 out·400k·100% up
  • OpenAI$1.75 in·$14.00 out·400k·100% up

Per million tokens. Prices and uptime via OpenRouter, checked 10 Oct 2026.

Common questions

What is the difference between DeepSeek R1 and GPT-5.3-Codex?

DeepSeek R1 is developed by DeepSeek while GPT-5.3-Codex is developed by OpenAI. DeepSeek R1 has a 128K token context window vs GPT-5.3-Codex's 400K. You can compare their actual outputs across 52 challenges on Rival to see how they differ in practice.

Which is better, DeepSeek R1 or GPT-5.3-Codex?

It depends on your use case. DeepSeek R1 and GPT-5.3-Codex each have strengths in different areas. Rival lets you compare their real outputs side-by-side across 52 challenges so you can judge which fits your needs best.

How much does DeepSeek R1 cost compared to GPT-5.3-Codex?

DeepSeek R1 costs $0.55/M input tokens and GPT-5.3-Codex costs $1.75/M input tokens. DeepSeek R1 is $1.20/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-5.3-Codex on Rival?

This page shows a side-by-side comparison of DeepSeek R1 and GPT-5.3-Codex 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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