OpenAI: GPT-4 Turbo (batch)

GPT-4 Turbo (batch) is a text generation model from OpenAI, offered on api14 under the model id openai/gpt-4-turbo:batch. It is the batch variant of OpenAI's GPT-4 Turbo line, the version that added vision capabilities. api14 routes requests to it through the same OpenAI-compatible interface you use for other models on the platform, so no separate SDK or integration work is needed.

The model accepts text and image input and produces text output, which means you can mix prompts and images in a single request where that helps. It supports a context window of 128,000 tokens, and responses can run up to 4,096 tokens. Its training data runs to December 2023. Vision requests work alongside JSON mode and function calling, so structured output and tool use are not restricted to text-only calls.

Those capabilities point to a few practical patterns. Long documents, codebases or transcripts fit comfortably in the 128,000 token window, so summarisation, extraction and question answering over large inputs are viable without heavy chunking. Because images are accepted as input, screenshot analysis, diagram interpretation and document parsing from scanned pages are also in scope. Function calling and structured outputs make it a reasonable fit for agents, tool-using workflows and pipelines that need machine-readable responses, while JSON mode keeps downstream parsing predictable.

To call it on api14, send a request to the chat completions endpoint with the model field set to openai/gpt-4-turbo:batch, exactly as you would with OpenAI's own API. The model supports 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 covers sampling controls, determinism via seed, token limits, stop sequences, structured output formats and tool definitions, so most standard OpenAI-style request bodies work without changes.

Two constraints are worth noting before you commit. Output is capped at 4,096 tokens per response, so tasks that require very long single completions may need to be split across calls, and the knowledge cutoff of December 2023 means the model has no awareness of anything after that date unless you supply it in the prompt. If your workload needs newer knowledge, larger output budgets or a different cost profile, check the other models available on api14 before settling on this one.