GPT-6 Astra vs. Claude Opus 5.5: Which Model to Use

When OpenAI released GPT-6 Astra in early September, the company pitched it as a model that can do "anything you can do on a computer."

That same month, Anthropic answered with Claude Opus 5.5, a model it says performs at the level of its own top-end Claude Fable 5.1. (We made the comparison between GPT-Astra and Claude Fable 5.1 earlier.)

The two models are now popular default choices for serious agentic work. They've been out for a couple of months, and we've had serious time to practice with both. In this article, I'll compare GPT-6 Astra and Claude Opus 5.5 on benchmarks, features, and pricing. To go the extra mile, I'll also put both through the same hands-on coding test, and I'll make a specific recommendation about which one I like better.

What Is GPT-6 Astra?

GPT-6 Astra is OpenAI's flagship model announced in September. It's available in ChatGPT Plus, Pro, Business, and Enterprise, in Codex, through the OpenAI API, and on Amazon Bedrock. 

OpenAI built Astra around computer use. It can operate a desktop end-to-end, including apps that don't have an API, and it's trained to complete tasks with fewer tokens and fewer retries.

OpenAI also calls GPT-6 Astra its most aligned model so far, and it's the first OpenAI model to reach the "Critical" cybersecurity capability threshold under the company's Preparedness Framework.

What Is Claude Opus 5.5?

Claude Opus 5.5 is Anthropic's newest Opus model, also released this last September. It's available in the Claude apps, Claude Code, the Claude Platform API (as claude-opus-5-5), Amazon Bedrock, Google Cloud Vertex AI, and Microsoft Foundry.

Anthropic positions Opus 5.5 as performing at the level of Claude Fable 5.1 on most work, while being 20% cheaper per token than Opus 5 and roughly 40% cheaper on typical workloads, since it also uses fewer tokens. Anthropic says output generation is more than 30% faster than Opus 5. 

Anthropic's launch highlights lean heavily toward long-running engineering work: an early tester completed a 680,000-line code migration in under a day, and in an internal C-to-Rust translation of HAProxy, Opus 5.5 finished in 9.5 hours versus 12 for Fable 5.1.

GPT-6 Astra vs. Claude Opus 5.5: Head-to-Head Comparison

The cleanest side-by-side data we have comes from Anthropic's Opus 5.5 launch post, which lists GPT-6 Astra alongside its own models. Keep in mind that the Astra figures there are OpenAI's reported scores or public leaderboard results, not runs Anthropic did itself.

Coding and software engineering

This is where Opus 5.5 makes its strongest case. On Terminal-Bench 4.0, which tests agents working in a real terminal, Opus 5.5 scores 66.4% against Astra's 57.9%, an 8.5-point lead. 

FrontierCode is much closer: 54.4% for Opus 5.5 versus 53.3% for Astra, essentially a tie. 

But Astra has its own coding strengths. OpenAI reports 74.1% on DeepSWE v1.1, which it calls a new record, and says Astra in the Codex harness completes tasks faster than its predecessor. Anthropic doesn't publish a DeepSWE score for Opus 5.5.

Knowledge work and reasoning

On GDPval-AA v2.1, which grades professional tasks across 44 occupations, Opus 5.5 scores 1846 Elo against Astra's 1542. That's a large margin. Opus 5.5 also leads on Humanity's Last Exam with tools, at 67.7% versus 57.2%.

Astra pulls ahead on scientific terminal work, scoring 64.6% on Terminal-Bench-Science versus 58.7% for Opus 5.5. OpenAI also reports very strong math and science results for Astra, including 97.6% on FrontierMath Tier 4 v2 and 96.0% on GPQA Diamond. Anthropic doesn't report those benchmarks for Opus 5.5, so if math-heavy reasoning is your core use case, Astra has the stronger published evidence.

Computer use and business automation

Computer use is Astra's headline feature. OpenAI reports 72.6% on OSWorld 2.0 and 92.7% on ScreenSpot-Pro, and it ships enterprise controls for this: admins can restrict websites and desktop apps, require approval before consequential actions, and have potentially unsafe tool calls reviewed automatically.

Anthropic reports 81.8% on OSWorld 2.1 (partial credit) for Opus 5.5. That's a different version of the benchmark with different scoring, so you can't compare it directly with Astra's 72.6%. On AutomationBench, Zapier's business-workflow benchmark, the two are close, with Astra slightly ahead at 41.4% versus 40.0%.

Context window and output

Both models handle very long inputs. Astra has a 1.05M-token context window, and Opus 5.5 has a 1M-token window. Both allow up to 128K output tokens, and Opus 5.5 can go up to 300K output tokens through the Message Batches API (in beta).

Safety and availability

Both labs gate their most sensitive capabilities. Astra is rated "Critical" for cybersecurity, so advanced exploit work requires OpenAI's trusted-access program. Opus 5.5 reroutes most cybersecurity tasks to Claude Opus 4.8 and uses the same biology safeguards as Fable 5.1, with verification programs for vetted organizations. 

Pricing: what you actually pay

On list price, Opus 5.5 is 60% cheaper than Astra on both input and output, and 80% cheaper on cache reads. A request with 100K input tokens and 10K output tokens costs about $0.60 on Opus 5.5 and $1.50 on Astra.

Token prices aren't the whole story, though. OpenAI trained Astra to finish tasks in fewer tokens, and Opus 5.5 at max effort can generate a lot of reasoning output, which narrows the gap on some workloads. 

Hands-On Test: Building a Gear-Driven Clock

Now for the fun part: I gave both models the same build task, with the same prompt, in the same coding agent.

The task: a single-file web page showing an analog clock that's driven entirely by a visible gear train. The page reads the time once at load; after that, only the gears are allowed to move the hands.

I picked this test because a reader can check the results by eye. Do the teeth mesh? Do neighboring gears turn in opposite directions?

The setup

Both models ran in OpenCode on the same Mac, one after the other, with default settings and a single attempt each. I used the standard versions of each model (gpt-6-astra via OpenRouter and claude-opus-5-5 via the Anthropic API), not the Pro or fast-mode variants. Here's the prompt, pasted verbatim:

Build a single-file web page, index.html, with inline CSS and JavaScript. Use canvas or SVG. No external libraries, no network requests, no build step.

The page shows an analog clock driven entirely by a visible mechanical gear train.

Requirements:

One driving gear (the "motor") turns at a constant rate. Everything else moves only because gears mesh: the second, minute, and hour hands must be driven through the gear train, with the minute hand turning exactly 1/60 as fast as the second hand and the hour hand exactly 1/12 as fast as the minute hand. Do not compute hand angles from Date() each frame; read the current local time once at load to set the starting position, then let the gears drive the hands.
Show at least six gears with visible teeth. Gears that mesh must have matching tooth size, teeth that visibly interlock without overlapping, and opposite directions of rotation. Each pair's speed ratio must equal the inverse of its tooth-count ratio. Compound gears (two gears fixed on one axle) are allowed and should look like they share an axle.
Hovering over any gear shows its tooth count and its current speed in RPM.
Controls: a speed slider from 1x to 3600x real time, a pause/resume button, and a "reset to now" button.
After running at any speed for several minutes and then returning to 1x, the clock must still show the correct time for the simulated elapsed time (no drift from floating-point accumulation).
It should look like a well-made piece of clockwork, readable at both desktop and phone widths.

When you're done, briefly explain your gear train: the tooth counts and how they produce the 60:1 and 12:1 ratios.Powered by

The results

Here are both clocks running side by side at 10x speed. At this speed, the first big wheel in each clock turns once every six seconds and the minute hands creep forward.

What I noticed watching them

They read "analog clock" differently. Astra built what clockmakers call a regulator: seconds, minutes, and hours each get their own dial, mounted on separate wheels spread across the panel. Opus built a traditional clock face, with the hour and minute hands sharing the center, a small seconds dial above, and the gear train showing through a cut-away face.

Astra's gears are easier to follow, and Opus's time is easier to read. With Astra, every wheel is out in the open, and you can trace the motion from the motor all the way to the hours dial. But to tell the time, you have to check three dials. With Opus, you can read the time at a glance, but some of the gears sit behind the dial ring, so you can't see every one of them mesh. 

The first big wheel looks too fast, but it isn't. When I first sped the clocks up, the first large wheel in each one looked like it was racing, especially Astra's. In both designs, that wheel carries the second hand, so it turns once a minute in real time and once a second at 60x. At real speed, you can see it moving exactly in step with the second hand.

Some of Astra's wheels overlap. In a couple of places, Astra draws one large wheel on top of another, such as where the hours wheel crosses the green wheel beside it. That isn't wrong, since real clock movements stack wheels in layers, but it makes it harder to see which teeth are actually touching.

Both kept the right time. I fast-forwarded both clocks by 10 hours, which takes about 10 seconds at the top speed, and both landed on 20:10:30, right where they should be.

Which one I liked better

Both clocks work. If I wanted something to watch, I'd pick Astra's: the whole mechanism is on display, and it looks like a piece of engineering. That said, what Opus put together is closer to what the prompt asked for: a single analog clock face.

When to Choose GPT-6 Astra vs Claude Opus 5.5

The clock build is just one fun project. I didn't have a chance to test every use case. Here are useful guidelines:

Choose Claude Opus 5.5 if...

• You're building or running coding agents, especially long-running migrations, refactors, and terminal-based work.
• Your workload is professional knowledge work, such as research, analysis, and documents. I'm saying this since Opus 5.5 leads on GDPval-AA.
• Cost matters at scale, or you regularly send prompts longer than 272K tokens.
• You need the model on Google Cloud or Microsoft Foundry.

Choose GPT-6 Astra if...

• Your main use case is computer use: driving desktop apps and browsers, filling forms, and QA across tools without APIs.
• You need the strongest published math and science reasoning. I'm saying this because Astra leads on Terminal-Bench-Science and reports top FrontierMath and GPQA scores.
• Your team already works in ChatGPT Enterprise and Codex, and you want Astra's admin controls and enterprise plugins.

Final Thoughts

On paper, Claude Opus 5.5 is the better value. It leads GPT-6 Astra on most of the benchmarks where both have scores, including a clear lead on agentic coding and knowledge work, and it does so at 40% of Astra's list price.

But Astra isn't outclassed. It's the stronger choice for computer use, it posts the better published results on math and science, and its token efficiency means the real cost gap is smaller than the price sheet suggests.