OpenAI: GPT-3.5 Turbo (batch)

OpenAI: GPT-3.5 Turbo (batch) is a text model from OpenAI, offered through api14 under the model id openai/gpt-3.5-turbo:batch. It belongs to the GPT-3.5 Turbo family, which OpenAI describes as its fastest model. The provider release date is 2023-05-28, and the training data covers up to September 2021, so the model's built-in knowledge stops at that point.

The model works with text input and produces text output. It understands and generates natural language or code, and it is optimized for chat and traditional completion tasks. The context window is 16,385 tokens, and the maximum output is 4,096 tokens. Because it handles both prose and code, it fits workloads that mix conversational turns with structured text generation.

Practical uses follow directly from those traits. Chat assistants, conversational interfaces and multi-turn dialogue are a natural fit given the chat optimization. Traditional completion tasks, such as drafting, summarizing or rewriting text, also work. Since the model can generate code, it can help with code-oriented prompts as well. The 16,385 token context gives room for longer conversations or larger prompts, while the 4,096 token output cap suits responses that do not need to be extremely long.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-3.5-turbo:batch. The request parameters it supports are frequency_penalty, logit_bias, logprobs, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs and top_p. That set covers sampling controls, token limits, stop sequences, deterministic seeding, structured output and tool or function calling, so you can shape both plain text responses and tool-driven flows without leaving the standard interface.

Keep the knowledge cutoff in mind: anything after September 2021 is outside the model's training data, so time-sensitive questions may need retrieval or prompt-supplied context. The 4,096 token output limit also matters for tasks that require long single responses. For those situations, or when a task depends on more recent information, another model may fit better.