Claude Opus 4.6 vs OpenAI Codex 5.3: Which is Higher?

The rivalry between Anthropic and OpenAI has intensified, from competing Tremendous Bowl adverts to launching new coding fashions on the identical day. Anthropic’s Claude Opus 4.6 and OpenAI’s Codex 5.3 at the moment are dwell. Each present sturdy benchmarks, however which one really stands out? I’ll put them to the take a look at and evaluate their efficiency on the identical process. Let’s see which one comes out on high.

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 “high of the chart” of their launch notes, with particular high-tier efficiency indicated of their system playing cards.

Claude 4.6 Opus (Anthropic):

• Focus: Distinctive at deep reasoning and long-context retrieval (1M tokens). It excels at Terminal-Bench 2.0, suggesting it’s at the moment the strongest mannequin for agentic planning and sophisticated system-level duties.
• New Options: Introduces “Adaptive Pondering” and “Context Compaction” to handle long-running duties with out dropping focus.

Right here’s our detailed evaluate on Claude Opus 4.6.

GPT-5.3-Codex (OpenAI):

• Focus: Specialised for the complete software program lifecycle and visible laptop use. It exhibits an enormous leap in OSWorld-Verified, making it extremely efficient at navigating UI/UX to finish duties.
• New Options: Optimized for pace (25% quicker than 5.2) and “Interactive Collaboration,” permitting customers to steer the mannequin in real-time whereas it executes.

Right here’s our detailed weblog on Codex 5.3.

How you can Entry?

• For Opus 4.6: I’ve used my Claude Professional account price $17 per thirty days.
• For Codex 5.3: I’ve 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 Duties

Now that we’re carried out with all the idea, let’s evaluate the efficiency of those fashions. You could find my immediate, mannequin responses and my tackle the identical:

Process 1: Twitter‑model Clone (net app)

Immediate:

You might be an skilled full‑stack engineer and product designer. Your process is to construct a easy Twitter‑model clone (net app) utilizing dummy frontend knowledge.

Use: Subsequent.js (App Router) + React + TypeScript + Tailwind CSS. No authentication, no actual backend; simply mocked in‑reminiscence knowledge within the frontend.

Core Necessities:

• Left Sidebar: Brand, most important nav (Residence, Discover, Notifications, Messages, Bookmarks, Lists, Profile, Extra), main “Put up” button.
• Heart Feed: Timeline with tweets, composer on the high (profile avatar + “What is occurring?” enter), every tweet with avatar, identify, deal with, time, textual content, elective picture, and actions (Reply, Retweet, Like, View/Share).
• Proper Sidebar: Search bar, “Tendencies for you” field (subjects with tweet counts), “Who to observe” card (3 dummy profiles).
• Prime Navigation Bar: Mounted with “Residence” and a pair of tabs: “For you” and “Following”.
• Cell Habits: On small screens, present a backside nav bar with icons as an alternative of the left sidebar.

Dummy Knowledge:

• Create TypeScript sorts for Tweet, Consumer, Pattern.
• Seed app with:
  • 15 dummy tweets (quick/lengthy textual content, some with photographs, various like/retweet/reply counts).
  • 5 dummy developments (identify, class, tweet rely).
  • 5 dummy customers for “Who to observe”.

Habits:

• Put up Composer: Sort a tweet and immediately add it to the highest of the “For you” feed.
• Like Button: Toggle appreciated/unliked state and replace like rely.
• Tabs: “For you” exhibits all tweets, “Following” exhibits tweets from 2–3 particular customers.
• Search Bar: Filter developments by identify because the person sorts.

File and Element Construction:

• app/format.tsx: World format.
• app/web page.tsx: Principal feed web page.
• elements/Sidebar.tsx: Left sidebar.
• elements/Feed.tsx: Heart feed.
• elements/Tweet.tsx: Particular person tweet playing cards.
• elements/TweetComposer.tsx: Composer.
• elements/RightSidebar.tsx: Tendencies + who-to-follow.
• elements/BottomNav.tsx: Cell backside navigation.
• knowledge/knowledge.ts: Dummy knowledge and TypeScript sorts.

Use Tailwind CSS to match Twitter’s design: darkish textual content on mild background, rounded playing cards, refined dividers.

Output:

• Present a brief overview (5–7 bullet factors) of the structure and knowledge circulate.
• Output all information with feedback on the high for file paths and full, copy-paste-ready code.
• Match imports with file paths used.

Constraints:

• No backend, database, or exterior API—every thing should run with npm run dev.
• Use an ordinary create-next-app + Tailwind setup.
• Hold all content material dummy (no actual usernames or copyrighted content material).

How you can Run:

After making a Subsequent.js + Tailwind mission, run the app with the precise instructions offered.

Output:

My Take:

The Twitter clone constructed by Claude was noticeably higher. Codex did handle to create a sidebar panel, however it had lacking photographs and felt incomplete, whereas Claude’s model regarded much more polished and production-ready.

Process 2: Making a Blackjack Recreation

Immediate:

Recreation Overview:

Construct a easy, honest 1v1 Blackjack sport the place a human participant competes towards a pc vendor, following customary on line casino guidelines. The pc ought to observe mounted vendor guidelines and never cheat or peek at hidden info.

Tech & Construction:

• Use HTML, CSS, and JavaScript solely.
• Single-page app with three information: index.html, model.css, script.js.
• No exterior libraries.

Recreation Guidelines (Commonplace Blackjack):

• Deck: 52 playing cards, 4 fits, values:
  • Quantity playing cards: face worth.
  • J, Q, Ok: worth 10.
  • Aces: worth 1 or 11, whichever is extra favorable with out busting.
• Preliminary Deal:
  • Participant: 2 playing cards face up.
  • Vendor: 2 playing cards, one face up, one face down.
• Participant Flip:
  • Choices: “Hit” (take card) or “Stand” (finish flip).
  • If the participant goes over 21, they bust and lose instantly.
• Vendor Flip (Mounted Logic):
  • Reveal the hidden card.
  • Vendor should hit till 17 or extra, and should stand at 17 or above (select “hit on comfortable 17” or “stand on all 17s” and state it clearly within the UI).
  • Vendor doesn’t see future playing cards or override guidelines.
• End result:
  • If the vendor busts and the participant doesn’t, the participant wins.
  • If neither busts, the upper complete wins.
  • Equal totals = “Push” (tie).

Equity / No Bias Necessities:

• Use a correctly shuffled deck initially of every spherical (e.g., Fisher-Yates shuffle).
• The vendor should not change conduct based mostly on hidden info.
• Don’t rearrange the deck mid-round.
• Hold all sport logic in script.js for audibility.
• Show a message like: “Vendor follows mounted guidelines (hits till 17, stands at 17+). No rigging.”

UI Necessities:

• Format:
  • Prime: Vendor part – present vendor’s playing cards and complete.
  • Center: Standing textual content (e.g., “Your flip – Hit or Stand?”, “Vendor is drawing…”, “You win!”, “Vendor wins”, “Push”).
  • Backside: Participant part – present participant’s playing cards, complete, and buttons for Hit, Stand, and New Spherical.
  • Present playing cards as easy rectangles with rank and swimsuit (textual content solely, no photographs).
  • Show win/loss/tie counters.

Interactions & Move:

• When the web page masses, present a “Begin Recreation” button, then deal preliminary playing cards.
• Allow Hit/Stand buttons solely throughout the participant’s flip.
• After the participant stands or busts, run the vendor’s computerized flip step-by-step (with small timeouts).
• At spherical finish, present the result message and replace counters.
• “New Spherical” button resets palms and reshuffles the deck.

Code Group:

• Capabilities in script.js:
  • createDeck(): Returns a recent 52-card deck.
  • shuffleDeck(deck): Shuffles the deck (Fisher-Yates).
  • dealInitialHands(): Offers 2 playing cards every.
  • calculateHandTotal(hand): Handles Aces as 1 or 11 optimally.
  • playerHit(), playerStand(), dealerTurn(), checkOutcome().
• Observe variables for playerHand, dealerHand, deck, and win/loss/tie counters.

Output Format:

• Briefly clarify in 5–7 bullet factors how equity and no bias are ensured.
• Output the complete content material for:
  • index.html
  • model.css
  • script.js
• Make sure the code is copy-paste prepared and constant (no lacking features or variables).
• Add a “How you can run” part: instruct to put the three information in a folder and open index.html in a browser.

Output:

My Take:

The hole turned much more apparent within the Blackjack sport. Codex 5.3 produced a really boring, static output. In distinction, Claude Opus 4.6 was approach forward. It delivered a correct inexperienced on line casino mat, a way more engaging UI, and an total participating net expertise. 

Claude Opus 4.6 vs OpenAI Codex 5.3: Ultimate Verdict

Opinions on whether or not Codex 5.3 or Opus 4.6 is best stay divided within the tech group. Codex 5.3 is favored for its pace, reliability in producing bug-free code, and effectiveness in advanced engineering duties, significantly for backend fixes and autonomous execution. Alternatively, Opus 4.6 excels in deeper reasoning, agentic capabilities, and dealing with long-context issues, providing extra engaging UI designs. Nonetheless, it might probably face challenges with iterations and token effectivity.

After my hands-on expertise with each fashions, for this battle, Codex 5.3 vs Claude Opus 4.6, I’m going with Claude Opus 4.6 🏆. 

The general efficiency, ease of use, and polished UI made it stand out within the duties I examined, though Codex 5.3 had its deserves in pace and performance.

Don’t simply take my phrase for it. Put each fashions to the take a look at your self and see which one works greatest for you! Let me know your ideas.

I’m a Knowledge Science Trainee at Analytics Vidhya, passionately engaged on the event of superior AI options comparable to Generative AI functions, Giant Language Fashions, and cutting-edge AI instruments that push the boundaries of know-how. My function additionally entails creating participating instructional content material for Analytics Vidhya’s YouTube channels, creating complete programs that cowl the complete spectrum of machine studying to generative AI, and authoring technical blogs that join foundational ideas with the newest improvements in AI. Via this, I purpose to contribute to constructing clever methods and share data that conjures up and empowers the AI group.