OpenAI: GPT-3.5 Turbo Instruct

GPT-3.5 Turbo Instruct is a text model from OpenAI, released on 28 September 2023. It is a variant of GPT-3.5 Turbo tuned for instructional prompts, and it omits the chat-related optimizations found in OpenAI's conversational models. Its training data runs up to September 2021, which is the stated knowledge cutoff.

The model takes text as input and returns text as output, so it fits plain completion style work rather than multimodal tasks. It has a context length of 4095 tokens and a maximum output of 3685 tokens. Supported request parameters include temperature, top_p, max_tokens, stop, frequency_penalty, presence_penalty, logit_bias, logprobs, top_logprobs, seed, response_format, and structured_outputs.

Because the model is instruction tuned rather than chat optimized, the natural fit is single-turn prompt to completion work. Typical uses include drafting and rewriting text, summarization, classification, extraction into a defined structure, and generation pipelines where you send one instruction and consume one response. Structured outputs and response_format support are useful when downstream code needs predictable shapes, and logprobs with top_logprobs help when you want to inspect token level confidence. The seed parameter helps when you want more reproducible sampling across runs.

You call the model through api14's OpenAI-compatible API using the model id "openai/gpt-3.5-turbo-instruct". Requests follow the OpenAI chat completions style schema, so existing OpenAI SDKs and clients work with only a base URL and key change. Send your instruction as the user message content and set any of the supported parameters listed above. Note that because this model is instruction tuned rather than chat optimized, prompts written as a clear single instruction tend to suit it better than long multi-turn conversations.

The main constraints to plan around are the context length of 4095 tokens and the knowledge cutoff of September 2021, so anything requiring recent information must be supplied in the prompt itself. Long documents, large code files, or extended multi-turn dialogues may exceed the window or work less naturally here. If you need a conversational back and forth, a chat-optimized model is usually a better match, and if you need a much larger context, another model in the catalog will fit better.