SpaceXAI: Grok 4.5

SpaceXAI: Grok 4.5 is a text generation model from xAI, listed on api14 under the model id x-ai/grok-4.5. It sits in the text category and is positioned by its provider as a model with frontier performance on coding, knowledge work, and STEM tasks. The provider release date is 2026-07-08. If you are evaluating models for a production workload, this is the entry to consider when your tasks lean toward code, technical reasoning, or knowledge-intensive work rather than casual chat alone.

The model accepts text, image, and file inputs, and it produces text output. Its context window is 500,000 tokens, with a maximum output of 450,000 tokens. That combination of a large input window and a large output ceiling means you can supply long documents, codebases, or mixed text and image payloads in a single request and still leave substantial room for the model to generate a detailed answer. Because input modalities include images and files, the model can be used in workflows where the source material is not plain text alone.

Those capabilities point to a few practical uses. Coding assistance is a natural fit: reviewing large files, explaining unfamiliar code, generating patches, or answering questions about a repository that you pass in as context. Knowledge work follows from the same properties, such as summarising long reports, extracting structured information from documents, or answering questions grounded in material you supply. STEM tasks, including multi-step technical reasoning, also fit, and the reasoning-related parameters let you control how much internal reasoning effort the model applies before it answers. Structured outputs and response format controls make it usable in pipelines where the result must conform to a schema rather than free prose.

To call it through api14, use the OpenAI-compatible chat completions endpoint with the model id x-ai/grok-4.5. The supported request parameters are include_reasoning, logprobs, max_tokens, reasoning, reasoning_effort, response_format, seed, structured_outputs, temperature, tool_choice, tools, top_logprobs, and top_p. In practice this means you can cap generation with max_tokens, tune sampling with temperature and top_p, request deterministic behaviour with seed, inspect token-level probabilities with logprobs and top_logprobs, and constrain the reply with response_format or structured_outputs. Tool use is available through tools and tool_choice, and the reasoning controls let you enable or adjust internal reasoning and decide whether it is surfaced via include_reasoning.

One thing to keep in mind is that output is text only, so if you need generated images or audio, this model is not the right choice. The large context window and output ceiling also mean requests can be heavy, so for short, simple tasks a smaller or faster model may be more appropriate. Within its text, image, and file input scope, though, Grok 4.5 is a reasonable default when the work is technical or document-heavy and you want the option of explicit reasoning and structured output.