OpenAI: GPT-3.5 Turbo (older v0613)

GPT-3.5 Turbo (older v0613) is a text model from OpenAI, distributed under the model id openai/gpt-3.5-turbo-0613. It belongs to the GPT-3.5 Turbo family, which OpenAI describes as its fastest model line, and this particular version is an older snapshot that remains available for workloads that depend on its specific behaviour. Its training data runs up to September 2021, so it has no knowledge of events after that point unless you supply the information yourself in the prompt.

The model accepts text as input and returns text as output, making it a general purpose language model for chat and traditional completion tasks. It can understand and generate natural language as well as code. The context window is 4,095 tokens, and the maximum output is 3,685 tokens, so both the prompt and the reply share a fairly tight budget compared with larger modern models. There is no image, audio or file input here; everything you send must be text.

Because it handles both prose and code, the model fits a range of everyday tasks: chat assistants and conversational interfaces, drafting and rewriting text, summarisation of short documents, classification and extraction over small inputs, and lightweight code generation or explanation. Its support for tools and structured outputs also makes it usable for simple function calling and for producing responses in a defined format, which helps when you need machine readable output rather than free text. The 4,095 token window means it suits prompts and answers that fit comfortably in a few pages of text, not long document analysis.

You call it through api14's OpenAI-compatible API by passing the model id openai/gpt-3.5-turbo-0613 in your request, so existing OpenAI SDKs and client libraries work with only a base URL and key change. The supported request parameters are frequency_penalty, logit_bias, logprobs, max_completion_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs and top_p. That covers sampling controls, determinism via seed, token level inspection through logprobs and top_logprobs, and tool or schema driven output through tools, tool_choice, response_format and structured_outputs.

The main constraint to plan around is the context length. At 4,095 tokens with a 3,685 token output ceiling, long conversations, large codebases or multi document prompts will need chunking, retrieval or trimming, and the September 2021 knowledge cutoff means recent information must come from your own context. If your task needs a much larger window, newer knowledge or multimodal input, a different model on api14 will likely fit better. For short, high volume text and code tasks where speed matters, this model remains a reasonable choice.