The rivalry between Anthropic and OpenAI has intensified, from competing Super Bowl ads to launching new coding models on the same day. Anthropic’s Claude Opus 4.6 and OpenAI’s Codex 5.3 are now live. Both show strong benchmarks, but which one truly stands out? I’ll put them to the test and compare their performance on the same task. Let’s see which one comes out on top.
OpenAI Codex 5.3 vs Claude Opus 4.6: Benchmarks
Claude 4.6 Opus scores for SWE-Bench and Cybersecurity are described as “industry-leading” or “top of the chart” in their release notes, with specific high-tier performance indicated in their system cards.
Benchmark
Claude 4.6 Opus
GPT-5.3-Codex
Notes
Terminal-Bench 2.0
81.4%
77.3%
Agentic terminal skills and system tasks.
SWE-Bench Pro
~57%*
56.8%
Real-world software engineering (multi-language).
GDPval-AA
Leading (+144 Elo)
70.9% (High)
Professional knowledge work value.
OSWorld-Verified
72.7%
64.7%
Visual desktop environment usage.
Humanity’s Last Exam
First Place
N/A
Complex multidisciplinary reasoning.
Context Window
1 Million Tokens
128k (Output)
Claude supports 1M input / 128k output limit.
Cybersecurity (CTF)
~78%*
77.6%
Identifying and patching vulnerabilities.
Claude 4.6 Opus (Anthropic):
- Focus: Exceptional at deep reasoning and long-context retrieval (1M tokens). It excels at Terminal-Bench 2.0, suggesting it is currently the strongest model for agentic planning and complex system-level tasks.
- New Features: Introduces “Adaptive Thinking” and “Context Compaction” to manage long-running tasks without losing focus.
Here’s our detailed review on Claude Opus 4.6.
GPT-5.3-Codex (OpenAI):
- Focus: Specialized for the full software lifecycle and visual computer use. It shows a massive leap in OSWorld-Verified, making it highly effective at navigating UI/UX to complete tasks.
- New Features: Optimized for speed (25% faster than 5.2) and “Interactive Collaboration,” allowing users to steer the model in real-time while it executes.
Here’s our detailed blog on Codex 5.3.
How to Access?
- For Opus 4.6: I have used my Claude Pro account worth $17 per month.
- For Codex 5.3: I have used the macOS app of codex and my ChatGPT plus account (₹1,999/month) for logging-in.
Claude Opus 4.6 vs OpenAI Codex 5.3 Tasks
Now that we are done with all the basis, let’s compare the performance of these models. You can find my prompt, model responses and my take on the same:
Task 1: Twitter‑style Clone (web app)
Prompt:
You are an expert full‑stack engineer and product designer. Your task is to build a simple Twitter‑style clone (web app) using dummy frontend data.
Use: Next.js (App Router) + React + TypeScript + Tailwind CSS. No authentication, no real backend; just mocked in‑memory data in the frontend.
Core Requirements:
- Left Sidebar: Logo, main nav (Home, Explore, Notifications, Messages, Bookmarks, Lists, Profile, More), primary “Post” button.
- Center Feed: Timeline with tweets, composer at the top (profile avatar + “What is happening?” input), each tweet with avatar, name, handle, time, text, optional image, and actions (Reply, Retweet, Like, View/Share).
- Right Sidebar: Search bar, “Trends for you” box (topics with tweet counts), “Who to follow” card (3 dummy profiles).
- Top Navigation Bar: Fixed with “Home” and 2 tabs: “For you” and “Following”.
- Mobile Behavior: On small screens, show a bottom nav bar with icons instead of the left sidebar.
Dummy Data:
- Create TypeScript types for Tweet, User, Trend.
- Seed app with:
- 15 dummy tweets (short/long text, some with images, varying like/retweet/reply counts).
- 5 dummy trends (name, category, tweet count).
- 5 dummy users for “Who to follow”.
Behavior:
- Post Composer: Type a tweet and instantly add it to the top of the “For you” feed.
- Like Button: Toggle liked/unliked state and update like count.
- Tabs: “For you” shows all tweets, “Following” shows tweets from 2–3 specific users.
- Search Bar: Filter trends by name as the user types.
File and Component Structure:
- app/layout.tsx: Global layout.
- app/page.tsx: Main feed page.
- components/Sidebar.tsx: Left sidebar.
- components/Feed.tsx: Center feed.
- components/Tweet.tsx: Individual tweet cards.
- components/TweetComposer.tsx: Composer.
- components/RightSidebar.tsx: Trends + who-to-follow.
- components/BottomNav.tsx: Mobile bottom navigation.
- data/data.ts: Dummy data and TypeScript types.
Use Tailwind CSS to match Twitter’s design: dark text on light background, rounded cards, subtle dividers.
Output:
- Provide a short overview (5–7 bullet points) of the architecture and data flow.
- Output all files with comments at the top for file paths and full, copy-paste-ready code.
- Match imports with file paths used.
Constraints:
- No backend, database, or external API—everything must run with npm run dev.
- Use a standard create-next-app + Tailwind setup.
- Keep all content dummy (no real usernames or copyrighted content).
How to Run:
After creating a Next.js + Tailwind project, run the app with the exact commands provided.
Output:
My Take:
The Twitter clone built by Claude was noticeably better. Codex did manage to create a sidebar panel, but it had missing images and felt incomplete, whereas Claude’s version looked far more polished and production-ready.
Task 2: Creating a Blackjack Game
Prompt:
Game Overview:
Build a simple, fair 1v1 Blackjack game where a human player competes against a computer dealer, following standard casino rules. The computer should follow fixed dealer rules and not cheat or peek at hidden information.
Tech & Structure:
- Use HTML, CSS, and JavaScript only.
- Single-page app with three files: index.html, style.css, script.js.
- No external libraries.
Game Rules (Standard Blackjack):
- Deck: 52 cards, 4 suits, values:
- Number cards: face value.
- J, Q, K: value 10.
- Aces: value 1 or 11, whichever is more favorable without busting.
- Initial Deal:
- Player: 2 cards face up.
- Dealer: 2 cards, one face up, one face down.
- Player Turn:
- Options: “Hit” (take card) or “Stand” (end turn).
- If the player goes over 21, they bust and lose immediately.
- Dealer Turn (Fixed Logic):
- Reveal the hidden card.
- Dealer must hit until 17 or more, and must stand at 17 or above (choose “hit on soft 17” or “stand on all 17s” and state it clearly in the UI).
- Dealer does not see future cards or override rules.
- Outcome:
- If the dealer busts and the player does not, the player wins.
- If neither busts, the higher total wins.
- Equal totals = “Push” (tie).
Fairness / No Bias Requirements:
- Use a properly shuffled deck at the start of each round (e.g., Fisher-Yates shuffle).
- The dealer must not change behavior based on hidden information.
- Do not rearrange the deck mid-round.
- Keep all game logic in script.js for audibility.
- Display a message like: “Dealer follows fixed rules (hits until 17, stands at 17+). No rigging.”
UI Requirements:
- Layout:
- Top: Dealer section – show dealer’s cards and total.
- Middle: Status text (e.g., “Your turn – Hit or Stand?”, “Dealer is drawing…”, “You win!”, “Dealer wins”, “Push”).
- Bottom: Player section – show player’s cards, total, and buttons for Hit, Stand, and New Round.
- Show cards as simple rectangles with rank and suit (text only, no images).
- Display win/loss/tie counters.
Interactions & Flow:
- When the page loads, show a “Start Game” button, then deal initial cards.
- Enable Hit/Stand buttons only during the player’s turn.
- After the player stands or busts, run the dealer’s automatic turn step-by-step (with small timeouts).
- At round end, show the outcome message and update counters.
- “New Round” button resets hands and reshuffles the deck.
Code Organization:
- Functions in script.js:
- createDeck(): Returns a fresh 52-card deck.
- shuffleDeck(deck): Shuffles the deck (Fisher-Yates).
- dealInitialHands(): Deals 2 cards each.
- calculateHandTotal(hand): Handles Aces as 1 or 11 optimally.
- playerHit(), playerStand(), dealerTurn(), checkOutcome().
- Track variables for playerHand, dealerHand, deck, and win/loss/tie counters.
Output Format:
- Briefly explain in 5–7 bullet points how fairness and no bias are ensured.
- Output the full content for:
- index.html
- style.css
- script.js
- Ensure the code is copy-paste ready and consistent (no missing functions or variables).
- Add a “How to run” section: instruct to place the three files in a folder and open index.html in a browser.
Output:
My Take:
The gap became even more obvious in the Blackjack game. Codex 5.3 produced a very boring, static output. In contrast, Claude Opus 4.6 was way ahead. It delivered a proper green casino mat, a much more attractive UI, and an overall engaging web experience.
Claude Opus 4.6 vs OpenAI Codex 5.3: Final Verdict
Opinions on whether Codex 5.3 or Opus 4.6 is better remain divided in the tech community. Codex 5.3 is favored for its speed, reliability in producing bug-free code, and effectiveness in complex engineering tasks, particularly for backend fixes and autonomous execution. On the other hand, Opus 4.6 excels in deeper reasoning, agentic capabilities, and handling long-context problems, offering more attractive UI designs. However, it can face challenges with iterations and token efficiency.
After my hands-on experience with both models, for this battle, Codex 5.3 vs Claude Opus 4.6, I’m going with Claude Opus 4.6 🏆.
The overall performance, ease of use, and polished UI made it stand out in the tasks I tested, even though Codex 5.3 had its merits in speed and functionality.
Don’t just take my word for it. Put both models to the test yourself and see which one works best for you! Let me know your thoughts.
I am a Data Science Trainee at Analytics Vidhya, passionately working on the development of advanced AI solutions such as Generative AI applications, Large Language Models, and cutting-edge AI tools that push the boundaries of technology. My role also involves creating engaging educational content for Analytics Vidhya’s YouTube channels, developing comprehensive courses that cover the full spectrum of machine learning to generative AI, and authoring technical blogs that connect foundational concepts with the latest innovations in AI. Through this, I aim to contribute to building intelligent systems and share knowledge that inspires and empowers the AI community.
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