SpaceXAI: Grok 4.3

SpaceXAI: Grok 4.3 is a reasoning model from xAI. It sits in the text category and is built around reasoning first behaviour, which shapes how it responds to prompts and how it fits into a pipeline. If you have used other Grok models from the same provider, the general shape of the API and the style of output will be familiar, but the reasoning focus is the defining characteristic here.

The model takes text, images and files as input and returns text. That combination matters for tasks where the source material is not purely prose, such as screenshots, scanned documents or mixed media. The context window is 1,000,000 tokens, and the maximum output is 900,000 tokens, so long inputs and long generations are both viable within a single request. Supported parameters include temperature, top_p, max_tokens, seed, logprobs and top_logprobs, plus response_format and structured_outputs for constraining the shape of the reply. Reasoning behaviour is configurable through the reasoning, reasoning_effort and include_reasoning parameters, and tool use is available through tools and tool_choice.

Those capabilities point to a few practical patterns. Agentic workflows are a natural fit, since the model can hold a large working context, call tools and reason across multiple steps before producing a final answer. Instruction-following tasks benefit from structured_outputs and response_format when you need machine-readable results rather than free text. Long document or file analysis works because the context window is large enough to hold substantial source material alongside the instructions. Image input extends this to cases where the relevant information is in a diagram, screenshot or photographed page. Reasoning effort controls let you trade depth of deliberation against latency depending on the task.

You call the model through api14's OpenAI-compatible API using the model id "x-ai/grok-4.3". Because the interface follows the OpenAI chat completions shape, existing clients and SDKs generally work with a change of base URL and model id. The request parameters listed above are supported, so you can set temperature, top_p, max_tokens, seed, logprobs, top_logprobs, response_format, structured_outputs, tools, tool_choice, reasoning, reasoning_effort and include_reasoning as needed. Note that the model outputs text only, so any image or file content belongs in the input.

One thing to keep in mind is that this is a reasoning model, which means responses can take longer than a straightforward text model when reasoning effort is set high. If your task is simple classification or short generation and latency matters more than deliberation, a lighter model may be a better fit. Similarly, if you need output modalities other than text, this model will not serve that need. For everything else, the combination of a large context window, image and file input, tool calling and structured output makes it a reasonable default for demanding text workloads.