Remember when AI models could barely handle a paragraph? Those days are long gone. Let’s break down how context windows in large language models (LLMs) have exploded in size over the past few years.
Way back in the stone age of 2019, GPT-2 had a puny 1024 token context window. You could maybe fit a short article in there if you were lucky. GPT-3 doubled that, but it was still pretty limiting.
Things started getting interesting with GPT-3.5 in late 2022. Its 4096 token window let us work with longer texts and have more substantial conversations. A few months later, they bumped it up to 16K tokens. Progress!
GPT-4 launched with 8K tokens, finally letting us tackle small coding projects in one go. But the real game-changer was Claude from Anthropic. This bad boy could handle 100,000 tokens at once. Suddenly, we could summarize entire books or create full-fledged games within a single context. The catch? Claude wasn’t the sharpest tool in the shed at first.
Late 2023 saw OpenAI fire back with GPT-4 Turbo and its 128K token window. This quickly became the new standard, with even smaller open-source models adopting similar capacities. Anthropic upped the ante again with Claude 2.1’s 200K tokens, but honestly, most tasks don’t need that much context.
Google decided to flex with Gemini 1.5 Pro, boasting a million-token context. Great for crunching entire codebases or textbooks, but overkill for day-to-day use. They even have a 2-million token version, which is just showing off at this point.
The latest headline-grabber? A company called Magic claims they’ve built a model with a 100 million token context. That’s nuts. For perspective, GPT-2 (yes, the whole model) had 107 million parameters total. We’re approaching a reality where you could theoretically generate new language models from text prompts. Wild stuff, even if there’s zero practical use for it right now.
Here’s the thing: massive context windows are cool, but they come with serious drawbacks. The bigger the input, the slower and more expensive these models become to run. Even if we build billion-token context models, using them would cost a fortune.
It’s worth noting that all these models use the Transformer architecture. There are alternatives like Mamba that handle long contexts better, and hybrids like Jamba that combine different approaches. We might see billion-token windows soon, but do we really need them?
Many tasks that seem to require huge context can actually be solved with clever techniques like retrieval-augmented generation (RAG) or by using agent frameworks to break problems into manageable chunks.
My prediction? For most LLMs, 128K tokens will remain the sweet spot. It’s more than enough for most applications, and it strikes a balance between capability and efficiency. We’ll always have specialized models for niche use cases, but don’t expect your average chatbot to start digesting entire libraries anytime soon.
The real innovations will come from smarter ways of using context, not just making it bigger. Keep an eye on techniques that make models more efficient with the context they have, rather than just expanding it endlessly.
Want to dive deeper into the world of AI language models? Check out my post on small language models. They’re proving that bigger isn’t always better in the AI world.
