OpenAI: GPT-3.5 Turbo 16k

GPT-3.5 Turbo 16k is a text model from OpenAI, released by the provider on 28 August 2023. It sits in the GPT-3.5 family and is the long context variant of the standard GPT-3.5 Turbo, offering roughly four times the context length of that model. It is a general purpose language model rather than a specialised one, and it is aimed at developers who need more room in a single request than the base GPT-3.5 Turbo provides.

The model accepts text as input and returns text as output. Its context window is 16,385 tokens, and the maximum output is 4,096 tokens. The knowledge cutoff is 30 September 2021, so anything after that date is outside its training data. The larger context is the main differentiator here: OpenAI describes it as supporting approximately 20 pages of text in a single request, which makes it suitable for prompts that carry a lot of source material rather than a short instruction.

That capacity shapes the practical use cases. Summarising long documents, answering questions over a pasted corpus, extracting structured fields from lengthy reports, and multi turn conversations that need to retain a large amount of prior dialogue all fit within the 16,385 token window. Because the model supports tools and structured outputs, it can also be used for function calling style workflows where the model selects a tool and returns arguments, and for responses that must conform to a JSON schema via response_format. Logprobs and top_logprobs are available, which helps when you need to inspect the model's token level confidence rather than just the final text.

You call it through api14's OpenAI-compatible API using the model id "openai/gpt-3.5-turbo-16k". Because the endpoint follows the OpenAI chat completions shape, existing OpenAI SDKs and clients work with only a base URL and key change. The supported request parameters are frequency_penalty, logit_bias, logprobs, max_completion_tokens, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs and top_p. Seed is useful when you want repeatable sampling, and stop sequences let you bound generation.

Two constraints are worth weighing before you commit. The knowledge cutoff of September 2021 means the model has no awareness of later events, libraries or APIs unless you supply that context yourself, and the 4,096 token output ceiling limits how much it can produce in one response even though the input window is large. If your task needs more recent knowledge, a longer output, or image input, this model does not cover it, and a different model on api14 would be the better fit.