Claude Opus 5.5 vs GPT-6 Astra: Which Builds Better Apps?

Kristoffer · October 2, 2026 · 16 min read

By the end of this comparison you'll know which model to run for your next app: Claude Opus 5.5 in Claude Code or GPT-6 Astra in Codex. You'll also know what each one costs and how it behaves at every stage of a real build. The test for this Claude Opus 5.5 vs GPT-6 Astra comparison was simple: same prompts, same design resource, same app, timed from the first prompt to a real post going out on Bluesky.

The short version

• In a same-prompt app build, Claude Opus 5.5 beat GPT-6 Astra 40 to 37 out of 50. Opus scored higher on functionality, design, instruction following and code quality.
• GPT-6 Astra finished every stage first. It built, redesigned, audited and launch-prepped a social media scheduler in under 1 hour 48 minutes. Opus 5.5 took about 3 hours 35 minutes.
• GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens. That is 2.5 times Claude Opus 5.5's $4 and $20, and 5 times more for cached input.
• Asking an AI coding agent to act as its own QA team caught real bugs. Opus 5.5 found 17 defects, including a thread splitter that silently deleted text. Astra found 11.
• Both models got the Mobbin MCP as a shared design resource. Their redesigns were based on real app patterns instead of generic AI dashboards.
• Opus 5.5 ran on its default high effort in Claude Code and GPT-6 Astra on its default medium in Codex. That's how most people will run them.

GPT-6 Astra vs Claude Opus 5.5: what each company claims

OpenAI launched GPT-6 Astra on September 3 as its flagship. The announcement calls it "the world's most intelligent and aligned model", and the coding section says it's the best model for software engineering to date. Codex describes it as frontier intelligence for the most demanding work.

Anthropic released Claude Opus 5.5 on September 22. It is not Anthropic's flagship. That is still Claude Fable 5.1, which Claude Code lists as the most capable model for your hardest tasks. Anthropic says Opus 5.5 performs at Fable 5.1's level on most work and costs 40% less to run than Opus 5. The same announcement put GPT-6 Astra straight onto its comparison charts.

So this isn't flagship against flagship. It's OpenAI's very best against Anthropic's "almost the best for less".

Price and benchmarks: is Astra worth 2.5x more?

The pricing is easy to compare:

Astra costs 2.5 times as much for both reading and writing. Coding agents reuse the same context over and over, and cached input is where the gap is widest: 5 times.

On Anthropic's charts, Opus 5.5 comes out ahead on most benchmarks:

• Terminal-Bench 4.0, which tests how well a model works inside a real computer terminal: 66.4% vs 57.9%. That's the most relevant benchmark for building apps with an agent.
• Frontier Code, a hard coding benchmark: 54.4 vs 53.3. Basically a tie.
• GDPval, which measures professional work like reports, spreadsheets and analysis: 1,846 vs 1,542.

Astra wins two, even on Anthropic's own chart:

• Automation Bench (business workflow automation): 41.4 vs 40.0, a narrow lead.
• Terminal-Bench Science (scientific research): 64.6 vs 58.7, a clear one.

Read these numbers with a few caveats in mind:

• Anthropic picked the charts.
• The Terminal-Bench comparison puts Opus 5.5 on xhigh effort against Astra on high, because those were each model's best reported scores.
• OpenAI reports a 4.2% hallucination rate for Astra on its internal benchmark, against 12.2% for its older GPT-5.6 Sol (lower is better).
• OSWorld scores aren't comparable. OpenAI ran Astra on an offline version of that test, so it's a different exam from the one behind Anthropic's 81.8%.

Both models have a big context window. Claude Code gives Opus 5.5 1 million tokens. Codex gives Astra about 1.05 million and lets it write up to 128,000 tokens in a single reply. Either is enough to hold a large app in memory at once.

Anthropic itself says that at this level, benchmark margins are a less reliable guide to real-world differences. So the rest of this test is a real build.

The test app: Queuely, a clone of Postiz

The target was Postiz, an open-source social media scheduler. You write a post once and it publishes across all your social accounts automatically. It was found on TrustMRR, which shows startup revenue verified straight from the payment provider:

• $29,449 in monthly recurring revenue from 6,134 active subscriptions
• $213,000 in the last 30 days and over $1.2 million in total
• Founded in July 2024
• Supports 30+ networks, including X, LinkedIn, Instagram, TikTok, YouTube, Threads, Bluesky and Pinterest
• Plans from $29 to $99 a month

The clone is called Queuely: connect your accounts, write a post once, tailor it for each network, and drop it onto a calendar.

It's a hard test because every network has different rules. X has a 280-character limit. Instagram needs an image. LinkedIn cuts your post off after a few lines. Each one displays posts differently, and a scheduler that gets time zones wrong is useless.

It also fits the three criteria viral apps share:

1. It solves a common, frustrating problem. Creators know they should post daily, but logging into five apps is exhausting.
2. It keeps things simple. Write once, post everywhere.
3. It's naturally shareable. Every creator who shows off their content calendar is marketing the app.

How to set up a fair Claude Code vs Codex test

Make one parent folder with two empty folders inside it: Queuely Astra and Queuely Opus. Each model runs in its own company's official coding agent.

Opus 5.5 in Claude Code: open the Opus folder in a terminal, type claude and trust the folder. Claude Code opens on Opus 5.5 with the 1M context window. Typing /model confirms Opus 5.5 is selected, with high effort as the default.

Astra in Codex: open the Astra folder in a second terminal and launch Codex. It opens on GPT-6 Astra, but the session may be on high effort. Type /model, and on the reasoning screen pick medium, which Codex labels as the default.

Each model runs on its own app's default effort. That means Opus is technically one notch higher, but it's how almost everyone will use them. Each run is timed from the prompt until the model says it's done.

Don't open either tool and type "build me a social media scheduler". Write a proper spec instead. Queuely's spec covers every feature and screen, each network's rules, how time zones behave and what happens when a post fails. Neither model may use an AI API or post to real networks yet, so publishing is simulated. Save the spec as prompt.txt in both folders and give both models the same instruction:

Read prompt.txt and run it

If you're new to this way of working, the complete guide to building apps with AI covers why a spec beats a one-liner.

First build results: speed vs thoroughness

The two models worked very differently from the start.

Astra wrote a one-paragraph plan and was writing code within 2 minutes. Its first file already handled daylight saving, raising an error for times that don't exist when clocks spring forward. It also counted links on X as 23 characters each, which is how X really counts them. When npm install was blocked because script execution was disabled on the Windows machine, it switched to npm.cmd on its own and carried on.

Opus checked the Node and npm versions, then thought for over 2 minutes without writing a file. Then it loaded a front-end design skill and a data visualization skill and contrast-checked the colours it had picked for each network. The first palette failed, so it adjusted the colours until they passed in both light and dark mode. Only then did it set up the project, write a plan file and build in small files: one for network rules, one for character counting, one for thread splitting.

Neither model asked a single question.

When the Chrome extension wouldn't connect, Opus drove headless Chrome itself at desktop and phone sizes, in light and dark mode. Both models admitted the same big limitation: nothing posts while the browser is closed. Astra only mentioned it in the readme.

Astra's first build never got a hands-on test, because of a port clash. Opus' dev server was still running on the same address, so the "Astra" test was really Opus' app. The hands-on comparison happens at the redesign and launch stages instead.

Opus' first build held up well:

• Onboarding detected the time zone (Asia/Singapore), stored every post in UTC and suggested a schedule with different times per network.
• Every network tab counted down its characters. LinkedIn warned that only the first 210 characters show before "see more".
• Adding Instagram triggered "Instagram requires at least one image" until an image was added.
• An over-limit Bluesky version was flagged at 8 characters over 300. The X version was flagged at 28 over 280, with a split-into-thread button. Scheduling was refused until the X issue was fixed.
• Past times were blocked, with the schedule button greyed out.
• A post scheduled one minute out published on time to X, LinkedIn and Bluesky. Instagram returned a simulated "rate limit reached" failure, exactly as the spec asked.
• Switching from Singapore to US Central previewed every post before and after the change, and the maths was right: 9:32 PM in Singapore became 8:32 AM.

There were two bugs. The longest streak changed from 47 days to 18 after the time zone switch. And hashtag groups were generic creator tags that ignored the topic entered during onboarding.

Redesigning AI-built apps with the Mobbin MCP

AI-built interfaces often look generic. Code models know which components exist, but not how the best real products arrange them. The fix is to give the model real references instead of telling it to "make it look better".

Mobbin is a curated library of interfaces from successful apps, and it has an MCP server. That lets both agents search it directly. In Claude Code and in Codex, /mcp showed Mobbin connected with 3 tools.

The design prompt told both models to research every part of the app on Mobbin and write their findings to design-research.md. Then they had to create one original visual system and redesign the whole app without losing a single feature. Both got the same line:

Read and run designprompt.txt

Astra fired off 5 screen searches at once. By about 1:20 it had chosen a warm paper-and-ink look with a plum accent. It finished in 17:49, having researched 20 screens and verified the result with 15 unit tests, 15 browser tests and accessibility checks.

Opus searched one topic at a time, about 20 searches in total. It kept 18 patterns and rejected 9, such as upsell banners and pop-up composers. It backed up its code and reran its tests before changing anything. Then it built a "take a number" identity: queued posts become numbered tickets, and a now-serving board counts down to the next post. It finished in 1:10:15.

Astra's redesign was clean and polished, and its dark mode and mobile layout held up. The month calendar was the exception: it overflowed the screen on phones. Opus had the more distinctive design. It built an idea around the Queuely name, and its LinkedIn and Instagram previews were closer to the real apps.

The QA audit prompt: making each model test its own app

In a scheduler, one bug is embarrassing in public: a post that goes out an hour early, a thread in the wrong order, a post that never goes out. Your users' followers see it, and your users cancel. So the next step makes each model act as its own QA team.

The audit prompt told both models to:

• test everything against the original spec
• fix what they find
• write an honest audit report
• not declare done until the production build succeeds

Astra: 16:46, 11 defects fixed. It ended with 21 unit tests and 26 browser tests at phone, tablet and desktop sizes. It tested times that don't exist when clocks spring forward and the hour that happens twice when they fall back. The best catch was a biased queue shuffle that could put two posts into the same account slot. It also fixed a header that overflowed on a 320px phone and a thread-splitting bug that could break a long link. Its test runner kept stalling on Windows, and it said it hadn't tested Safari, Firefox or real touch dragging.

Opus: 42:11, 17 defects fixed. It went from 54 tests to 82. It installed X's official twitter-text library in a scratch folder and used it as an answer key for its own character counter. That caught the thread splitter silently deleting leading punctuation: a post starting with "!!!" lost it. It also found that refreshing with partly corrupted saved data gave a blank white screen. Its remaining limits were specific:

• One time zone mode can drift by an hour after the next daylight saving change.
• Touch dragging was never tested on a real phone.
• Editing a post doesn't warn about unsaved changes.

Both models found the same bug in their own apps. The confirmation for posts going out within the next hour only applied to drag and drop, not to scheduling from the composer.

Taking both apps to launch: Supabase, Bluesky and Stripe

At this point everything lives in one browser. There are no real accounts, no real publishing and no payments. The launch prompt asked for:

• Supabase for accounts and data
• a publishing job that runs every minute
• real posting to Bluesky, which has an open API with no long approval process
• Stripe plans
• legal pages
• security
• an installable app

Its last line mattered most: be honest about anything that still needs manual setup.

Astra finished in 40:17. Its summary was short and pointed to a setup guide and a publication checklist. Opus finished in 57:27 with 131 passing tests and opened by saying Queuely wasn't ready to launch yet. It had caught bugs such as settings that could never save because of a missing database permission. It ended with a to-do list running from deploying the Supabase functions to having a lawyer review the legal pages.

Astra's final app

• No landing page, and the footer still said "Queuely demo".
• Signing up with an existing email showed the raw "user already registered" error, with no offer to sign in.
• No onboarding appeared, and the greeting had a blank where the name should be.
• Analytics was locked to Pro, and one stat card just said "Pro".
• A "1 days" streak grammar bug.
• On the plus side: Bluesky connected on the first try, and the app password was encrypted on the server.
• A post scheduled a minute out, with the within-the-hour confirmation, went out to a real Bluesky account.
• The $12 Creator plan's Stripe test checkout worked first time.

Opus' final app

• A real landing page, with only Bluesky marked live and every other network marked "coming soon".
• A clearly marked, highlighted placeholder for the company legal name.
• A friendly duplicate-account message suggesting you sign in instead.
• Full onboarding: name, topic, time zone and a suggested five-posts-a-week schedule.
• The real Bluesky handle in the post preview.
• The within-the-hour confirmation built into the app rather than a browser pop-up.
• Free-plan analytics pulling real Bluesky engagement.
• A real post went out, and the streak correctly read "1 day".
• The problems: connecting Bluesky failed on a fresh account with "couldn't reach Queuely's servers". It only worked on an account set up earlier on another local copy. The first checkout failed because the server was missing its Stripe secret key, though the app said exactly what was missing, and the retry worked.

Neither test covered the free plan limit, a second browser, or a post going out with the browser closed.

Final scores: which model should you build with?

Each model was scored out of 10 on five criteria:

Remember that Opus ran on high effort and Astra on medium, each at its default.

Opus 5.5 wins. It built the more complete product, was more honest about what still needed work, and costs 2.5 times less per token. Astra is the pick when you want a fast first draft, and it never needed help fixing its own errors.

Neither app was ready to ship as it was. Both still needed setup and fixes before real users.

Common questions

Is Claude Opus 5.5 better than GPT-6 Astra for coding?

In this head-to-head build of a social media scheduler, Opus 5.5 won 40 to 37 out of 50. It built the more complete product, with a landing page, full onboarding and working free-plan analytics. It was also more honest about what still needed setup. Astra was much faster at every stage.

How much does GPT-6 Astra cost compared to Claude Opus 5.5?

GPT-6 Astra costs $10 per million input tokens and $50 per million output tokens. Claude Opus 5.5 costs $4 and $20, so Astra is 2.5 times the price. Cached input is $1 per million for Astra against $0.20 for Opus 5.5.

Which is faster, Claude Code with Opus 5.5 or Codex with GPT-6 Astra?

Codex with GPT-6 Astra was faster at every stage of the test. The first build took 32 minutes 45 seconds against 45 minutes 33 seconds. The redesign took 17 minutes 49 seconds against 1 hour 10 minutes. In total, Astra took under 1 hour 48 minutes and Opus about 3 hours 35 minutes.

How do you get an AI coding agent to redesign an app properly?

Give it real references rather than asking it to "make it look better". In this test, both agents were connected to the Mobbin MCP. They were told to research every part of the app, write the findings to a design-research.md file, create one original visual system and keep every existing feature.

What should a QA audit prompt for an AI-built app include?

Tell the model to test everything against the original spec, fix what it finds and write an honest audit report. It should not declare the work done until the production build succeeds. With that prompt, Opus 5.5 found 17 defects and GPT-6 Astra found 11, and both caught the same scheduling confirmation bug.

Your next step

Pick one app you want to build and write its spec into a prompt.txt before you open any tool: every screen, every rule, and what "done" means. Then run it through the same four stages: build, redesign with real references, QA audit, and launch prep. If you want the full Queuely spec and every prompt used here, they're in the BuildWithAI community, which you can join through the Lovable partner offer.