Grok 4.5

What is Grok 4.5?

Grok 4.5 is a reasoning model from xAI focused on software engineering, agentic workflows, technical analysis, and other demanding knowledge-work tasks. It is available through the xAI API and is also used in products and integrations including Grok Build and Cursor.

Rather than targeting only short conversational responses, Grok 4.5 is intended for multi-step work. Examples include examining a large codebase, debugging an unfamiliar system, planning a software migration, analyzing technical documents, or using external tools to complete a workflow. xAI describes its training and reinforcement-learning focus as covering coding, science, engineering, mathematics, software engineering, and agentic tasks.

Grok 4.5 should be understood as a text-generating model with image understanding, not as the complete set of image, video, or voice capabilities available elsewhere in the wider Grok product ecosystem.

Where Grok 4.5 fits in xAI’s lineup

Grok 4.5 belongs to xAI’s Grok 4 model family. The supplied model documentation identifies Grok 4.5 as a current API model for coding and agentic work, while xAI’s broader assistant documentation now refers to Grok 4.7 as the current assistant/model generation. That distinction matters: Grok 4.5 remains a specific model identifier with documented API behavior, even as the consumer-facing Grok lineup continues to change.

The canonical API identifier is grok-4.5. xAI also documents grok-4.5-latest and grok-build-latest as aliases. Availability and exact behavior can vary by integration, so applications that require a stable model reference should use the canonical identifier where appropriate.

Inputs, outputs, and context window

Grok 4.5 accepts text and image inputs and returns text. Its context window is 500,000 tokens, according to the supplied model specification. A context window is the amount of input and conversation material the model can consider in one request; in practical terms, this makes the model suitable for large codebases, long technical documents, extended conversations, and multi-step agent workflows.

The 500,000-token context limit should not be confused with the maximum response length. xAI’s current Grok 4.5 documentation does not specify a maximum output-token limit. Applications should therefore avoid assuming that the full context window can be returned as a single response.

Reasoning and coding capabilities

Reasoning is central to Grok 4.5. The model supports low, medium, and high reasoning-effort settings, with high as the default. Reasoning cannot be disabled. Some shared interfaces accept an xhigh value, but xAI documents that this is treated as high for Grok 4.5 rather than providing a distinct higher-effort mode.

Higher reasoning effort can be useful when the task involves several dependent steps, ambiguous requirements, difficult debugging, or extensive technical analysis. It can also increase the amount of computation and the time or cost associated with a request. Lower settings may be preferable for simpler transformations or workflows where response speed matters more than extended deliberation.

For coding, the model is intended for tasks such as:

• Writing and explaining application code
• Debugging errors and tracing likely causes
• Reviewing or transforming existing code
• Analyzing large repositories and technical documents
• Planning migrations and implementation steps
• Generating structured results for software workflows

The supplied research describes xAI’s coding and software-engineering emphasis, but it does not provide benchmark scores in the model documentation. Any evaluation claims beyond the documented capabilities should therefore be treated as editorial judgments rather than verified vendor results.

Tools, function calling, and agentic workflows

Grok 4.5 supports function calling, which allows an application to give the model a defined set of external operations. The model can select a function and provide arguments, while the application remains responsible for executing the operation and returning the result. This pattern can connect Grok 4.5 to databases, business systems, code tools, file processors, or other services.

The model also supports structured outputs. This lets developers request responses that conform to a defined schema, which is useful for extracting fields from documents, classifying records, producing workflow instructions, or passing model results safely between software components. Structured outputs are not the same as an unrestricted guarantee that every response will be valid JSON in every interface; applications should follow the relevant xAI API documentation and validate returned data.

Streaming is supported for text responses, allowing an application to display output as it is generated rather than waiting for the complete response. The model is also documented for file and document-oriented workflows. These features make it a candidate for agentic applications, where the model interprets a task, reasons about intermediate results, calls tools, and produces a final response.

Grok 4.5 pricing and API details

For standard-context requests below 200,000 prompt tokens, the documented price is $2 per million input tokens, $0.30 per million cached input tokens, and $6 per million output tokens. Requests using 200,000 tokens or more receive higher long-context rates: $4 per million input tokens, $0.60 per million cached input tokens, and $12 per million output tokens.

The long-context rates apply to requests at or above the documented 200,000-token threshold, so sending very large prompts can materially change the cost. Cached-input pricing can reduce the cost of repeated prompt material when the API’s caching conditions are met, but it does not make the output free. Batch API support is not available for Grok 4.5, and the model is listed for the global API in the us-east-1 and us-west-2 regions.

Strengths and limitations

Main strengths

• Large context: The 500,000-token window is useful for extensive code, documentation, and multi-step tasks.
• Technical specialization: The model is specifically positioned for coding, engineering, mathematics, and agentic software work.
• Configurable reasoning: Low, medium, and high effort settings provide a practical speed-versus-deliberation choice.
• Tool-oriented design: Function calling, structured outputs, streaming, and file workflows support integration into applications.
• Image understanding: Image input allows technical diagrams, screenshots, and other visual material to be considered alongside text.
• Competitive standard-context pricing: The standard input and output rates are lower than the model’s long-context rates, making prompt size an important cost variable.

Important limitations

• Grok 4.5 produces text and does not provide native image, video, audio, speech, music, or embedding output.
• Audio and video input are not documented as supported for this model.
• Reasoning cannot be turned off, which may be unnecessary for simple, latency-sensitive tasks.
• There is no documented maximum output-token limit in the current model information.
• Batch API support is unavailable.
• Fine-tuning is not documented as available for this model.
• The model’s knowledge-cutoff date is not publicly specified in the current documentation. Current information should be supplied through supported search tools or external context rather than assumed to be part of the base model knowledge.
• Long-context requests use higher pricing, and complex reasoning can increase practical latency and token consumption.

When to choose Grok 4.5

Choose Grok 4.5 when the task benefits from sustained reasoning, substantial context, and integration with external tools. It is a sensible option for a coding assistant that must inspect large amounts of source material, a technical research workflow that needs structured extraction, or an agent that must perform several dependent operations.

It is also a strong fit when image understanding is useful but the final result can remain text. For example, an application could provide a screenshot of an error, relevant source files, and a structured response schema for proposed fixes. The model’s function-calling support can then connect that reasoning process to testing, file retrieval, or project-management systems.

Another model type may be more appropriate when the priority is very low latency, minimal cost for simple prompts, or a native media output. Specialized image, video, speech, transcription, music, or embedding models are better suited to those jobs. A batch-capable model or service is preferable for large offline queues because Grok 4.5 does not support the Batch API. For routine classification or short transformations, a model that does not require always-on reasoning may provide a better speed-and-cost trade-off.

Bottom line

Grok 4.5 is best viewed as a technical reasoning model rather than a general media-generation model. Its combination of a 500,000-token context window, image input, configurable reasoning, coding focus, function calling, structured outputs, and streaming makes it well suited to software engineering and agentic workflows. The main considerations are the lack of native non-text output, the absence of Batch API support and a documented output limit, the higher cost of very long prompts, and the fact that reasoning is always enabled.