Astra, Claude, and Range Anxiety: The Real Test of the Giant Models in the Field

Why the race between OpenAI and Anthropic stopped being about who builds a prettier landing page a long time ago, and became about who gets us to the destination without getting stuck on the way

In recent days, with the reveal of OpenAI's new Astra model, the internet has been flooded with early eulogies for Claude (and especially for Anthropic's Claude Code). As usual in the tech world, it is very easy to rush and order a headstone for a company that still leads in a large share of performance and deep-reasoning benchmarks.

But something fundamental has definitely changed.

For me, with Astra, OpenAI puts forward, for the first time, an alternative I can truly rely on across a wide range of complex tasks: analyzing logs, investigating deep code failures, and combining files and reports into a single execution task. It matches Claude's level, and in some cases even raises the bar.

To understand where we stand, it is worth going back to the analogy I like most: electric cars.

In the past, when we bought a car, we talked about engine size, the gearbox, and the driving experience. With electric cars, the conversation moved mostly to the ride experience: giant screens, apps, massage seats, and phone integration. We got a tablet on wheels that also happens to take us to work.

Then the reality of a long trip arrives, and suddenly you remember the small detail: the battery and range anxiety.

Because the pampering seat is wonderful, but it is a lot less fun to sit in it next to a slow charging station in the middle of Highway 6. Suddenly all you care about is how much of the range promised in the brochure is really left on the asphalt once you turn on the air conditioning and climb the road up to Jerusalem.

Imagine two cars that claim the same 500-kilometer range. One of them approaches the destination without blinking, and the other needs a stop much earlier. Raw battery size is only half the story. What really decides it is the energy efficiency of the whole system.

This is exactly how language models and AI-based development work.

If you pay a $200 monthly subscription (or consume an API worth thousands of dollars), the question is simple: how much real work gets finished for that money?

• •How many times did you have to correct the model?
• •How many times did you have to explain the context to it all over again?
• •How many times did it produce three "work plans" for you instead of simply doing what you asked?

This is exactly where the two main vectors come in: the ride experience and the driving range.

1. •The ride experience (UX and wrapper tools): the interface, the response speed, the natural connection to files and the work environment, and the ability to go from an idea to working code without having to manage every click.
2. •The driving range (token efficiency and execution): how many useful results come out of the monthly quota before it resets or runs out. A model that burns millions of tokens on tedious chains of thought and in the end delivers a mediocre result is an electric car that drains its battery while still parked.

Until now, Claude (and in particular agent solutions like Claude Code) gave the highest sense of confidence on heavy tasks. It knew how to make the climb up to Jerusalem. There was not always spare battery left at the end of the road, but at least we reached the destination.

With Astra, that gap narrows dramatically. OpenAI puts significant emphasis on token consumption efficiency and system-level execution capabilities. In complex agent and cyber tests (as we saw in OpenAI's publications around Astra's capabilities), the model manages to carry out autonomous chains of actions while using a significantly smaller amount of resources compared to previous generations.

It is also impossible to ignore the change in atmosphere around this release. We got used to Hollywood-style launch events from OpenAI, teaser videos, and inflated announcements that become a story in themselves before anyone has run a single line of code.

This time, the launch around Astra felt different: quieter, focused on performance, with almost no background noise. They throw a monstrous model into the field, release it to the community and to developers, and let the results speak for themselves.

It is hard not to wonder whether we are seeing the fingerprint of Noam Shazeer here, one of the authors of the original Transformer paper, who left Google and joined OpenAI. This is exactly the style that characterized the deep research departments: less talk, more pure engineering. Bring powerful systems, let people discover their capabilities, and the excitement will build on its own.

This is a necessary maturing of the market. The era when the name "ChatGPT" was enough to keep everyone subscribed is over. Today, products are measured by their daily return on investment (ROI) in real work.

Absolutely not.

Claude 3.5 Sonnet and Anthropic's latest updates still lead in some of the complex content and knowledge work. On the other hand, Astra proves superior in problem-solving efficiency, independent work with tools, and complex document tests. There is no knockout here. There is a healthy competition that pushes both sides forward.

Efficiency on paper is an excellent start, but the real bill is settled at the end of the workday in front of the screen. In the meantime, I keep both subscriptions and take daily test drives.

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