OpenAI: GPT-4 Turbo

GPT-4 Turbo is a text generation model from OpenAI, listed on api14 under the model id openai/gpt-4-turbo. It is the latest GPT-4 Turbo variant and adds vision capabilities to the GPT-4 Turbo line. OpenAI describes it as supporting vision requests alongside JSON mode and function calling, and its training data runs up to December 2023.

The model accepts text and images as input and returns text. It has a context window of 128,000 tokens, which allows long documents, extended conversations and multiple images to be handled in a single request. The maximum output is 4,096 tokens, so responses are bounded even when the input is large. Its knowledge cutoff is 2023-12-31, meaning it has no awareness of events after that date unless you supply the information in the prompt.

Because it reads images as well as text, the model suits tasks such as describing or extracting information from screenshots, diagrams, charts and scanned documents, answering questions about an uploaded image, or combining visual context with a written instruction. The large context window makes it a reasonable fit for summarising long reports, reviewing sizable code files or contracts, and maintaining long multi turn conversations. Tool calling and structured outputs let you build workflows where the model selects functions or returns data in a defined shape, which is useful for agents, extraction pipelines and integrations that need machine readable results.

You call it through api14's OpenAI-compatible API using the model id openai/gpt-4-turbo, so 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_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_logprobs and top_p. That covers sampling controls, deterministic-ish behaviour via seed, token limits, stop sequences, probability inspection through logprobs and top_logprobs, structured output formatting, and function or tool calling with tool_choice.

Two constraints are worth noting when choosing this model. Output is capped at 4,096 tokens, so tasks that need very long single responses may require chunking or a model with a higher output limit. The knowledge cutoff of 2023-12-31 means questions about newer events depend on context you provide. If your workload is text only and does not benefit from image input, a text focused model may be a simpler fit, but for mixed text and image requests within a 128,000 token context, this model covers the common cases.