What is the maximum context window for OpenAI’s models?

OpenAI’s models have varying context window sizes depending on the specific version. As of late 2023, the largest publicly available context window is 128,000 tokens, offered by GPT-4 Turbo. Earlier models like GPT-3.5 Turbo support up to 16,000 tokens, while the standard GPT-4 model initially provided 8,000 tokens, with a 32,000-token variant released later. Tokens roughly equate to chunks of text—for example, 1,000 tokens represent about 750 words. A 128,000-token window allows processing approximately 300 pages of text in a single request, making it suitable for tasks requiring analysis of large documents or long conversations. Developers specify the context window via API parameters, ensuring the model processes only the allowed token range.

The context window matters because it determines how much information the model can consider in a single interaction. For instance, a chatbot using GPT-3.5 Turbo with a 4,000-token window might struggle to maintain coherence in lengthy conversations, whereas GPT-4 Turbo’s 128,000-token capacity enables it to reference hours of dialogue or entire technical manuals. However, larger windows come with trade-offs. Processing more tokens increases computational costs and latency. Developers must balance context length with efficiency—using a 32,000-token window for code analysis might be overkill if the task only requires parsing a 500-line script. OpenAI’s API pricing also scales with token usage, so larger windows directly impact cost.

When working with these models, developers must manage context effectively. For example, if an application processes a 100,000-word document, GPT-4 Turbo can handle it in one request, while older models would require chunking the text and stitching results. Tools like the max_tokens parameter help control output length, but inputs exceeding the model’s context limit are automatically truncated. It’s also important to note that performance may degrade slightly with extremely long contexts, as models prioritize recent information. Testing with real-world data—like legal documents or multi-turn support tickets—helps determine the optimal window size. Always check OpenAI’s documentation for updates, as context windows may expand with future model iterations.