A context window is not storage — it is working memory. Every token in the window (your system prompt, the conversation history, any documents you paste in, and the model's own output so far) competes for the same fixed-size budget. When people say Claude has a 200K context window or Gemini supports 1M tokens, those numbers include everything: input and output combined. A common mistake is treating the context window like a database you can stuff full of documents and expect the model to search perfectly. In reality, models process context through attention mechanisms, and attention has both computational and qualitative limits.
The "lost in the middle" problem is real and well-documented. Research from Stanford and elsewhere showed that when you place critical information in the middle of a very long context, models are measurably worse at using it compared to information at the beginning or end. This is not a theoretical concern — it directly affects how you should structure your prompts. If you are feeding a model 50 pages of documentation, put the most important sections first and last, not buried on page 25. Some teams work around this by chunking documents and using RAG to retrieve only the relevant pieces rather than dumping everything into context.
Context window sizes have grown dramatically. GPT-3 launched in 2020 with 4K tokens (roughly 3,000 words). By 2024, Claude offered 200K tokens, and Gemini 1.5 Pro pushed to 1M tokens. Google's Gemini 2.5 models maintain that million-token window. But bigger windows come with real trade-offs. Latency increases because the model must attend to more tokens. Cost goes up because most API providers charge per token processed. And as mentioned, quality on retrieval tasks does not scale linearly with context size — a 1M-token window is not 5x better at finding a needle than a 200K-token window.
For developers working with APIs, context management is a core engineering problem. Long conversations accumulate tokens fast. A back-and-forth chat might consume 500–1,000 tokens per exchange, which means a 4K-token model runs out of room in just a few turns. Production systems handle this with sliding windows (dropping the oldest messages), summarization (compressing prior conversation into a shorter summary), or hybrid approaches using RAG to offload reference material into a vector database and only pull in relevant chunks on demand. Getting this right is often the difference between a demo that works and a product that scales.
One nuance that trips up newcomers: the context window limit is on tokens, not characters or words. Tokenization varies by model and language. English text averages about 1 token per 4 characters, but code can be denser (variable names and syntax eat tokens fast), and non-Latin scripts like Chinese or Hindi often use more tokens per word. The same document might consume 10K tokens in English and 15K in Japanese. Most providers offer tokenizer tools or libraries — Anthropic has a token counter in the API response headers, and OpenAI publishes tiktoken — so you can measure exactly rather than guessing.