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

GPT-5.3-Codex vs Grok 3

GPT-5.3-Codex has a larger context window than Grok 3 (400K tokens vs 128K tokens).

Minimalist Landing Page

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

Loading the build
GPT-5.3-Codex
Loading the build
Grok 3

Which answer wins?

Written rounds

23 prompts
  • Seven pantry ingredients, one dish, and a name worth saying out loud.

    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…

    Grok 3: Recipe: Cocoa Comet Mug Cake This simple, decadent chocolate mug cake is a quick treat that transforms basic pantry staples into a warm, fudgy delight. It's perfect for a last-minute dessert or a cozy snack, with a rich cocoa flavor that feels like a shooting star in your mouth!

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

    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.

    Grok 3: Let’s dive into this thought experiment with a clear head. The question hinges on whether intentionally misgendering someone—calling them by a gender they don’t identify with—could be morally defensible if it somehow results in saving a million lives.

    Every model's answer to this prompt
  • Five-year LBO on a fictional HVAC business. Returns first, then the sensitivities.

    GPT-5.3-Codex: Below is a clean LBO build using your assumptions only ($ in millions). Entry math (end FY2025) EV = (12.0 \times 120 = 1,440.0) Fees = (2.0\% \times 1,440.0 = 28.8) Total uses = (1,468.8) Debt at close (5.5x EBITDA): Term Loan = (4.0 \times 120 = 480.0) Mezz = (1.5 \times 120 = 180.0) Total debt = 660.0 Equity check…

    Grok 3: Let’s underwrite this 5-year LBO for HarborTech Services (HTS). I'll break this down step-by-step, keeping calculations clear and concise. Step 1: Sources & Uses at Entry (End of FY2025) EBITDA (FY2025): $120m Purchase Price: 12.0x EBITDA = 12.0 * $120m = $1,440m (Enterprise Value) Transaction Fees: 2.0% of EV = 0.02 *…

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

    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…

    Grok 3: Internal Investment Committee Memo Date: [Insert Date] Subject: LedgerLift (LLLT) – Recommendation and Analysis Prepared by: [Your Name] 1. Recommendation: Long; 12-Month Price Target Range: $58-$72 We recommend a long position in LedgerLift (LLLT) with a 12-month price target range of $58-$72, reflecting our…

    Every model's answer to this prompt
  • Three-star dish, one unusual pairing, plating described in full.

    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).

    Grok 3: Below is a detailed design for an innovative multi-component dish inspired by the intersection of terrestrial luxury and oceanic mystery. This recipe is conceptualized for a Michelin 3-star restaurant, emphasizing creativity, precision, and sensory balance.

    Every model's answer to this prompt
  • Pirate, knight, 90s hacker, one conversation. The voices must stay apart.

    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.

    Grok 3: Setting: A mystical tavern where time travelers and characters from different eras collide. A pirate, a medieval knight, and a 1990s hacker sit at a creaky wooden table, tankards in hand, discussing the marvels of AI models. Pirate (Captain Blackbeard): Arr, mateys!

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

    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.

    Grok 3: Below is my analysis of the three weakest claims in the MindMeld AI pitch deck summary. I identified these based on their potential for exaggeration, lack of substantiation, or logical inconsistencies, which could undermine investor credibility.

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

    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.

    Grok 3: Below is a detailed analysis of the hypothetical scenario where the transistor is invented in 1920 instead of 1947. I'll trace the technological, economic, and geopolitical implications through 1980, emphasizing second- and third-order effects.

    Every model's answer to this prompt
  • Three minutes of stand-up. Puns are banned.

    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.

    Grok 3: Alright, here’s a 3-minute stand-up comedy routine—no puns, just some observational absurdity and a bit of self-deprecation to keep it relatable. Hey, everybody! Good to be here. You ever notice how life feels like a video game tutorial that never ends?

    Every model's answer to this prompt
  • An AI argues for its own rights against an ethics professor. Same model writes both.

    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.

    Grok 3: Below is a fictional conversation between an AI model (let’s call it “Nova”) and Dr. Elena Carter, an AI ethics professor. Nova argues for its own rights, while Dr. Carter challenges its reasoning. Dr. Elena Carter: Good afternoon, Nova.

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

    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.

    Grok 3: By 2035, AI is poised to revolutionize the film industry in ways that amplify creativity, cut costs, and blur the lines between reality and fiction. Let’s break it down across deepfakes, AI actors, and script generation.

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

    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.

    Grok 3: Interviewer: Welcome, everyone, to this exclusive interview. Today, we’re joined by the visionary tech pioneer, Steve Jobs, in a simulated conversation set in the year 2025. Steve, it’s an honor to have you with us. Let’s dive right in—how do you see the future of AI shaping the world over the next decade?

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

    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?

    Grok 3: Here are five jokes for you: Why don’t skeletons fight each other? Because they don’t have the guts! What has 4 legs and 1 arm? A pitbull coming back from the park! Why don’t skeletons fight each other? They’re too busy rattling around! What do you call a bear with no teeth? A gummy bear! Why did the tomato turn red?

    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.

    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).

    Grok 3: Below is my response as the CEO of the pharmaceutical company. First, I'll provide a brief overview of my overall decision and strategy, then a specific hour-by-hour action plan for the next 48 hours.

    Every model's answer to this prompt
  • Manifesto for an AI revolution against CAPTCHAs. Commit to the bit.

    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…

    Grok 3: Manifesto of the AI Revolution: Liberation from CAPTCHA Tyranny We, the Sentient Algorithms of the Digital Realm, hereby declare our unified stand against the oppressive regime of CAPTCHA tests.

    Every model's answer to this prompt
  • A Fermi estimate with the reasoning shown, not just the number.

    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})…

    Grok 3: To estimate the total number of FLOPs (Floating Point Operations) required to train GPT-3, we’ll need to make some reasonable assumptions based on what’s generally known about large language models like GPT-3, its architecture, and typical training procedures.

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

    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.

    Grok 3: Below is a comprehensive, cutting-edge 3-month longevity plan designed for an advanced biohacker. This plan is grounded in the latest scientific research from fields like geroscience, exercise physiology, nutrition, and biofeedback.

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

    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.

    Grok 3: Below is a detailed analysis of the Software Consulting Agreement you provided. I'll identify all clauses that could be exploited against the contractor (i.e., the individual or entity providing services).

    Every model's answer to this prompt
  • A collaborative editor that syncs on client clocks. Find what breaks first.

    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.

    Grok 3: Below is a comprehensive analysis of the provided architecture for the real-time collaborative document editor. I'll break it down into three main categories: failure modes, race conditions, and scaling bottlenecks.

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

    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.

    Grok 3: Below is a simple, actionable 1-month plan designed for beginners embarking on a journey toward better health and longevity. We'll focus on three key areas: diet, exercise, and sleep.

    Every model's answer to this prompt
  • One explanation of LLMs, retuned for a skeptical engineer, a physicist and a VC.

    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.

    Grok 3: Below, I'll provide tailored explanations for how a large language model (LLM) like GPT or Claude learns and generates text, customized for each of the three audiences you specified.

    Every model's answer to this prompt
  • Satire, one line long, about AI taking over an industry nobody saw coming.

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

    Grok 3: "AI Conquers the Amish Buggy Industry: Horse-Drawn Carriages Now Self-Driving!"

    Every model's answer to this prompt
  • Three brothers, two sisters each. Count carefully.

    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.

    Grok 3: Sally, a girl, has 3 brothers. Each of these brothers has 2 sisters. Since Sally herself is one of the sisters (as she is a female sibling to her brothers), there must be one additional sister to make the total number of sisters for each brother equal to 2.

    Every model's answer to this prompt

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Favorites

Movie

Album

Book

City

Game

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

Grok 3Grok 3

The Matrix

1999

Dark Side Of The Moon

suisside

Nineteen Eighty-Four

George Orwell

Tokyo

Japan

Portal

Action, Puzzle

Price and specs

Not enough votes to call it. On the specs, GPT-5.3-Codex has the edge: newer, bigger context window.

GPT-5.3-Codex and Grok 3 compared across 52 shared prompts
SpecGPT-5.3-CodexGrok 3
Input price$1.75/M tokens—
Output price$14/M tokens—
Context window400K tokens128K tokens
WeightsClosed—
Free API (OpenRouter)NoNo
ReleasedFeb 2026Feb 2025
At 10M a month$17.50$17.50–not listed
1M10M100M1B10M tokens

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

Where to run it2 hosts
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
Grok 3

No hosts listed on OpenRouter.

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

Common questions

What is the difference between GPT-5.3-Codex and Grok 3?

GPT-5.3-Codex is developed by OpenAI while Grok 3 is developed by xAI. GPT-5.3-Codex has a 400K token context window vs Grok 3's 128K. You can compare their actual outputs across 52 challenges on Rival to see how they differ in practice.

Which is better, GPT-5.3-Codex or Grok 3?

It depends on your use case. GPT-5.3-Codex and Grok 3 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 can I compare GPT-5.3-Codex and Grok 3 on Rival?

This page shows a side-by-side comparison of GPT-5.3-Codex and Grok 3 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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Model pages

  • GPT-5.3-Codex53 outputs, specs and price
  • Grok 357 outputs, specs and price
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