GPT-6 Released: What Can 2 Million Token Context Actually Do

On April 14th, OpenAI officially released GPT-6.

The most eye-catching spec is the 2-million-token context window. What’s that mean? Roughly enough to read Dream of the Red Chamber with room to spare, or cram in dozens of source files.

Honestly, my first reaction was: is this really necessary?

With GPT-4’s 32k context, I rarely hit the limit. Write an article, edit code—plenty of headroom. 2 million tokens? Using a sledgehammer to crack a nut?

But thinking it through, it’s not that simple.

Context length has a threshold effect. Below the threshold, you don’t notice; once crossed, entirely new use cases open up.

For example: previously, reading a 100-page PDF required extracting key sections or splitting conversations. Now throw the whole thing in and let it find highlights.

Even more powerful is coding. A medium-to-large project’s core codebase typically runs hundreds of thousands to millions of tokens. Previously AI glimpsed fragments; now it can genuinely read the entire text. What does this mean? It can offer suggestions based on understanding the whole project architecture, not blind guesses from fragments.

I tested several scenarios:

First, reading legal contracts. A typical M&A agreement might be 300,000-400,000 words. Previously, AI contract review required segmenting. Now dump the whole thing in and flag risky clauses directly.

Second, code refactoring. I took an 80,000-line open-source project, stuffed it all into context, then asked: What architectural problems does this project have? It actually gave valuable observations.

Third: multi-document comparative analysis. I put five papers on the same topic in simultaneously. This lateral reading was previously nearly impossible; now it’s trivial.

Of course, 2 million tokens isn’t without cost.

First, price. Longer context means higher computational costs. While OpenAI hasn’t announced pricing, imagine using the full 2 million won’t be cheap.

Second, latency. Processing this much information takes time. For real-time interaction scenarios, this could be problematic.

But overall, GPT-6’s release marks a new phase for LLMs. Previous competition focused on how smart; now shifting to how much information can it handle.

This is especially significant for enterprise applications. Imagine: customer service remembering your entire company history; legal advisors reading all relevant contracts at once.

These scenarios previously only existed in sci-fi; now they’re within reach.

Well… my wallet might take another hit.