Grok 4.5: Complete Specifications, Pricing, API Access & Use Cases (2026)

What is Grok 4.5?

Grok 4.5 is xAI’s frontier reasoning model, released on July 8, 2026, with a 500,000-token context window, text and image input support, configurable reasoning, and standard API pricing of $ 2 per million input tokens and $ 6 per million output tokens as of July 2026.

Current official materials use both xAI and SpaceXAI branding. This page uses xAI because it remains the provider name most commonly associated with the Grok model family, while acknowledging the SpaceXAI branding shown in the latest announcement and documentation.

The model is designed for coding, agentic tasks, technical problem-solving, and general knowledge work. It can use additional internal reasoning before producing an answer, with low, medium, and high reasoning settings available through the API. High reasoning is documented as the default for Grok 4.5.

Grok 4.5 is also designed to participate in tool-using workflows. Official documentation lists function calling, web search, X search, code execution, and structured outputs among its supported capabilities. These features make it relevant for applications that need to retrieve information, call external systems, execute controlled code, or return machine-readable results.

What Are Grok 4.5’s Key Specifications and Pricing?

The following table summarizes Grok 4.5 specifications and pricing verified or documented as of July 2026.

xAI’s model documentation confirms the context window, input and output modalities, cached-input rate, standard token prices, aliases, regional clusters, and published rate limits. It also states that requests exceeding 200,000 context tokens use a different pricing structure, so the standard \ $ 2/ \ $ 6 rates should not be assumed for every maximum-context request.

The Gate.AI model card lists Grok 4.5 with the gateway model ID x-ai/grok-4.5 . Gate.AI states that its displayed model prices stay aligned with provider pricing without platform markup, while cached input is billed using the provider’s cache-discount rate when caching is supported.

What Can Grok 4.5 Do That Makes It Useful in Production?

Coding and software-engineering workflows

Grok 4.5 is designed for code generation, debugging, refactoring, repository analysis, terminal tasks, and multi-step software-engineering work. xAI says the model was trained using datasets spanning coding, science, engineering, and mathematics, with additional reinforcement-learning work focused on technical and agentic tasks.

This can be useful when a workflow requires the model to inspect multiple files, identify dependencies, propose a change, and explain the result. Generated code still requires testing, dependency review, security scanning, and human approval before production deployment.

Tool-using agents

Function calling allows Grok 4.5 to request actions from external tools or services. Web search and X search can provide current information, while code execution can support calculations, data analysis, and technical validation.

These capabilities may fit research agents, developer assistants, data-processing pipelines, and internal automation. Tool access should remain permission-controlled because a model can select an incorrect function, provide invalid arguments, or misinterpret a tool result.

Long-document and repository analysis

The 500,000-token context window can accommodate extensive code, technical documentation, reports, contracts, research materials, or collections of related files.

A large context window increases the amount of material that can be supplied in one request, but it does not guarantee perfect recall or accurate interpretation. Retrieval, document segmentation, citations, and evaluation remain important for large-scale analysis.

Configurable reasoning

Developers can choose low, medium, or high reasoning effort. Lower settings may be appropriate for routine extraction or classification, while higher settings may fit complex debugging, planning, mathematical reasoning, or multi-step technical analysis.

Higher reasoning effort can increase latency and output-token usage. Production systems should test reasoning settings against task accuracy, response time, and total cost rather than using the maximum setting automatically.

Structured application output

Structured outputs can help applications request responses in a defined format instead of relying on unstructured prose. This is useful for extraction, routing, form completion, workflow state generation, and API-to-API communication.

Applications should still validate generated output against a schema and define retry or fallback behavior for missing, malformed, or unsupported values.

What Are Grok 4.5’s Supported Modalities?

Grok 4.5 should therefore be described as a ​ text-and-image-input reasoning model with text output , rather than as a single model that natively generates every media type. xAI’s model catalog separates Grok 4.5 from its image, video, and voice APIs.

Where Does Grok 4.5 Fall Short?

Grok 4.5 can generate inaccurate facts, flawed reasoning, insecure code, or unsupported conclusions. This is a general limitation of generative AI and is not unique to Grok 4.5. Important outputs should be verified against authoritative sources, automated tests, or qualified human review.

The model’s knowledge cutoff is not specified in the reviewed official documentation. Current information therefore depends on external search tools or supplied source material. Even when web or X search is enabled, retrieval results may be incomplete, outdated, misleading, or taken out of context.

Long-context processing creates cost and latency trade-offs. Although Grok 4.5 supports up to 500,000 tokens, xAI applies different pricing when request context exceeds 200,000 tokens. Very large prompts can also introduce irrelevant material that reduces answer focus.

Grok 4.5 does not natively generate images, audio, or video through its text-model endpoint. Applications requiring those outputs must use separate models or APIs.

Tool-using workflows introduce operational risk. A model may call the wrong tool, supply incorrect parameters, repeat actions, or act on a mistaken assumption. External tools should use least-privilege permissions, approval gates, request validation, spending limits, and auditable logs.

For legal, medical, financial, employment, security, or other high-impact decisions, Grok 4.5 should assist rather than replace qualified professionals. Sensitive conclusions should never be accepted solely because they were produced with a high reasoning setting.

What Is Grok 4.5 Best Used For?

The phrase "best used for" is scenario-dependent. Model selection should be based on representative tests using the organization’s actual prompts, tools, latency targets, safety controls, and budget.

How Does Grok 4.5 Compare to GPT-5.5 and Claude Sonnet 5?

Grok 4.5, GPT-5.5 , and Claude Sonnet 5 address overlapping demand for coding, reasoning, tool use, and agentic workflows. The comparison avoids below declaring an overall winner because provider specifications, prices, and access conditions can change.

A useful comparison should measure task completion rate, factual accuracy, code quality, latency, token usage, tool-call reliability, and failure recovery on the same workload. Published benchmark results can provide context, but they should not replace application-specific testing.

Teams evaluating migration within the Grok family may also consult the Grok 4.3 model profile to compare context length, pricing, and generation behavior across versions.

How Do I Access Grok 4.5 Through Gate.AI?

As per the Gate.AI model card, Grok 4.5 is available under the model ID:

x-ai/grok- 4.5

Gate.AI documents an OpenAI-compatible API interface with the base URL:

https://api.gate.ai/openai/v1​​

Developers create a Gate.AI API key, replace the OpenAI base URL, and specify the selected model ID in the request. Gate.AI also documents optional automatic routing, API-key management, request logs, budget controls, prompt-caching visibility, usage insights, and organization permissions.

Python Example

Python import os

import OpenAI from openai

api_key = os.environ.get("GATEAI_API_KEY")
if not api_key:
raise RuntimeError("Set the GATEAI_API_KEY environment variable.")

client = OpenAI(
api_key=api_key,
base_url="https://api.gate.ai/openai/v1",
)

response = client.chat.completions.create(
model="x-ai/grok-4.5",
messages=[
{
"role": "user",
"content": (
"Review this JavaScript function, identify the bug, "
"and provide a corrected implementation: "
"function median(a){a.sort();return a[a.length/2]}"
),
}
],
)

print(response.choices[0].message.content) ```

curl Example

``` Bash curl https://api.gate.ai/openai/v1/chat/completions \

-H "Authorization: Bearer $GATEAI_API_KEY" \
-H "Content-Type: application/json" \
-d ‘{
"model": "x-ai/grok-4.5",
"messages": [
{
"role": "user",
"content": "Review this JavaScript function, identify the bug, and provide a corrected implementation: function median(a){a.sort();return a[a.length/2]}"
}
]
} ```

Gate.AI states that its listed model prices stay synchronized with provider prices without platform markup. Its pricing documentation also says cached tokens use the provider’s cache-discount rate when supported, while failed, timed-out, or invalid failover attempts are not billed.

Developers can alternatively access Grok 4.5 directly through the xAI Responses API using the provider model ID grok-4.5 . The official xAI example sends requests to the /v1/responses endpoint using bearer-token authentication.

FAQs

What is the Grok 4.5 context window?
Grok 4.5 supports a maximum documented context window of 500,000 tokens as of July 2026. xAI states that requests exceeding 200,000 context tokens use different pricing, so maximum-context workloads may not be billed at the standard input rate.

How much does the Grok 4.5 API cost?
Standard pricing is \ $ 2 per million input tokens, \ $ 0.50 per million cached input tokens, and \ $ 6 per million output tokens as of July 2026. Different rates apply above 200,000 context tokens, and tool-related charges may be billed separately.

How can developers access Grok 4.5?
Developers can use the xAI API with model ID grok-4.5 . As per the Gate.AI model card, the gateway model ID is x-ai/grok-4.5 , accessed through Gate.AI’s OpenAI-compatible API endpoint.

What tasks is Grok 4.5 suitable for?
Grok 4.5 may fit coding, repository analysis, technical troubleshooting, long-document review, structured extraction, research assistance, and tool-using agents. Its outputs still require testing or human review, especially for production code and high-impact decisions.