SpaceXAI: Grok 4.20 Multi-Agent

SpaceXAI: Grok 4.20 Multi-Agent is a text model from xAI. It is a variant of the company's Grok 4.20 model, built specifically for collaborative, agent-based workflows. Where a conventional chat model produces a single stream of output, this one is designed around multiple agents operating in parallel, which is the defining characteristic of the variant and the main reason a developer would choose it over a standard Grok release.

On the input side, the model accepts text, images and files, and it returns text only. The context window is very large at 2,000,000 tokens, and the maximum output is 1,800,000 tokens, so both what you send in and what you get back can be substantial. The knowledge cutoff is 1 September 2025, which matters when your task depends on recent information and you are not supplying that information yourself. Reasoning behaviour is exposed rather than hidden: the supported parameters include reasoning, reasoning_effort and include_reasoning, so you can turn internal reasoning on or off and, when it is on, ask for it to be surfaced.

Those capabilities point to a fairly specific set of jobs. Deep research is the obvious one: gathering material across many sources and synthesising it into a single answer, with the parallel agents handling different threads at once. The same shape suits tasks that combine retrieval with tool use, where the model coordinates several steps and needs to keep the intermediate results straight, and it suits multi-step analysis over long inputs such as large documents, codebases or report sets that would not fit in a smaller window. Because images and files are accepted alongside text, document-heavy work that mixes scanned pages, screenshots and written queries is also a reasonable fit.

You call it through api14's OpenAI-compatible API using the model id "x-ai/grok-4.20-multi-agent". Because the interface follows the OpenAI format, you can point an existing OpenAI client at api14 and change only the model id. Alongside the standard sampling controls (temperature, top_p, max_tokens, seed, response_format), it supports structured_outputs, reasoning, reasoning_effort, include_reasoning, logprobs and top_logprobs. That means you can constrain output to a schema, control how much reasoning effort is spent, and inspect token-level probabilities when you need to debug or score generations.

Two limits are worth weighing before you commit. Output is text only, so if you need the model to produce images or audio, this is not the right choice; that should be a different model. And with a knowledge cutoff of 1 September 2025, anything that depends on events after that date has to come from the context you provide, whether as text, images or files. If your task is a single short prompt that does not benefit from parallel agents or a very large context, a simpler and faster text model will usually be the better fit.