SpaceXAI: Grok 4.20

SpaceXAI: Grok 4.20 is a text model from xAI, listed on api14 under the model id x-ai/grok-4.20. It is a reasoning model, which means it is built to work through problems step by step rather than only producing an immediate answer. The provider describes it as combining a low hallucination rate with strict prompt adherence, and it is positioned around speed and agentic tool calling. Its knowledge cutoff is 2025-09-01, so anything after that date is outside what the model learned during training.

The model accepts text, images and files as input, and it returns text. Its context window is 2,000,000 tokens, with a maximum output of 1,800,000 tokens. That combination of a very large context and a very large output ceiling means you can supply long documents, large codebases, image inputs and file attachments in a single request, and still leave room for a substantial generated response. Because it is a reasoning model, it can also expose its reasoning, which is useful when you need to inspect how an answer was reached rather than only reading the final text.

Those capabilities point to a fairly specific set of use cases. Long context work such as reviewing entire repositories, contracts, research bundles or multi file datasets fits naturally, since you do not have to chunk aggressively. Image and file inputs make it suitable for document understanding, where a request mixes a scanned page or screenshot with instructions. Agentic tool calling and structured outputs suit pipelines that need the model to call functions, return machine readable results, or drive multi step workflows. Reasoning output helps in debugging, evaluation and any task where the intermediate steps matter.

You call it through api14's OpenAI-compatible API using the model id x-ai/grok-4.20, so existing OpenAI SDK clients and HTTP patterns work with minimal changes. The supported request parameters are include_reasoning, logprobs, max_tokens, reasoning, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_logprobs and top_p. In practice, tools and tool_choice cover function calling, response_format and structured_outputs cover JSON and schema constrained replies, reasoning and include_reasoning control the reasoning behaviour, and seed, temperature, top_p, logprobs and top_logprobs give you control over sampling and token level inspection.

There is no stated limitation in the facts beyond the knowledge cutoff and the text only output modality. If your task requires audio or video output, or knowledge after 2025-09-01 without retrieval, another model or an added retrieval layer may fit better. For text generation, long context analysis, document and image understanding, and tool driven agent workflows, Grok 4.20 is a reasonable default to evaluate.