Grok 4.5 is xAI's reasoning model for programming, agent tasks, and knowledge work, particularly suited to handling code, requirements, and technical documentation together. It covers engineering tasks in Rust, C/C++, and more, and can also analyze images and output text or structured results.
Point the Base URL to api.acedata.cloud, configure your platform API key and the model ID below, and use your compatible SDK or client.
api.acedata.cloudgrok-4.5import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ACEDATACLOUD_API_KEY"],
base_url="https://api.acedata.cloud/v1",
)
response = client.chat.completions.create(
model="grok-4.5",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)Choose an available protocol for this model. OpenAI SDK uses a Base URL ending in /v1; Anthropic SDK uses the root URL. See each guide for protocol-specific parameters, tools and response formats.
First, clarify this model's inputs and standard invocation method.
Native model features are for model selection; this platform's input limits, available parameters, and billing are subject to this model's API and pricing. Continuous output for Chat Completions uses stream, and the client is responsible for saving message history.
Learn what grok-4.5 can bring to your work.
Grok 4.5 is trained with an emphasis on multi-step software engineering, making it suitable for placing error messages, relevant source code, and expected behavior in the same task to identify causes and propose fixes. It is not only for code completion; it can also help address difficult Rust and C/C++ problems, implementation approaches, and test design, keeping responses focused on verifiable engineering goals.
For application building, Grok 4.5 can generate implementation code from natural-language requirements, with official demonstrations including interactive simulation applications. In practice, providing the feature scope, technology stack, and acceptance criteria together, then continuing to iterate based on runtime feedback, makes it easier to obtain a modifiable, testable initial draft than simply asking it to “build an application.”
Visual understanding enables Grok 4.5 to analyze images together with textual questions, for example by explaining layouts based on interface screenshots or organizing visible information. When results need to be consumed by a program, you can request answers organized by explicit fields and validate them in the application; if the endpoint supports JSON format configuration for this model, you can further use the corresponding format constraints. For external actions, use function definitions only when function-calling configuration is available; execution and result submission remain the application's responsibility.
Start with specific tasks to find where the model can make an impact.
Provide issue reproduction steps, relevant functions, error logs, and test results, and have the model produce root-cause hypotheses, modification suggestions, and regression-check items. For cross-file issues, first specify dependencies and interfaces that must not be changed, then add test feedback iteratively. This is suitable for producing fix proposals that developers can review, rather than skipping validation and deploying directly.
Provide technical-document excerpts, engineering constraints, and candidate solutions, and have Grok 4.5 organize comparison dimensions, reason through trade-offs, and generate a decision memo. You can ask it to distinguish source facts, inferences, and questions to be verified, delivering summaries, implementation steps, and verification checklists. It is suitable for R&D research, design reviews, and knowledge work that requires follow-up on details.
Provide interface screenshots, user action goals, and the frontend technology stack together to first obtain layout analysis and improvement suggestions, then request component code or an initial page draft. Deliverables can include written reviews, implementation code, and testing points; continue revising through subsequent screenshots and error messages to connect visual understanding with programming capabilities.
Choose based on task complexity, input materials, and expected results.
If the core needs are code debugging, technical reasoning, and knowledge organization, Grok 4.5 is worth evaluating as an engineering collaboration model. Grok 4.6 further emphasizes long-running agent tasks, as well as more complex visual and interactive projects. When choosing, compare results using the same requirements, code, and acceptance tests; there is no need to replace an existing workflow solely because a version is newer.
The official introduction to Grok 4.5 specifically covers Rust, C/C++, and application building. After providing the compilation environment, error logs, and unmodifiable interfaces, you can request code candidates and test designs; performance, memory, and runtime results must still be measured in the engineering environment.
Prepare the inputs first, then connect them to the relevant application workflow.
Provide a minimal reproduction, compilation parameters, dependency versions, and error logs, and describe performance or memory constraints.
Explicitly select grok-4.5 in the Chat Completions request, and organize the background, materials, and output requirements for this request into messages. First use a clearly scoped task to check the response, then include real review or test feedback in the next round of messages.
Design tasks directly from the following inputs and acceptance priorities.
Please identify the lifecycle or memory access issue below, compare two repair approaches, and provide a modification that preserves the existing interface along with a test that can reproduce the issue.
Compile and run the reproduction case to verify whether the failure is truly eliminated; when performance is involved, measure using the same hardware and inputs, and avoid asserting that it is faster based only on descriptions.
Before formal use, understand the output quality and scope of capabilities.
Answers to common questions about using grok-4.5.
Submit model=grok-4.5 and messages to /v1/chat/completions. Read regular results from choices[].message.content; use stream for incremental results in streaming calls. Use this platform's API Key, and set the complete base URL according to the SDK you use.
Have the application save the message history, and include the user and assistant messages relevant to the current question in messages. Provide a minimal reproduction, compiler parameters, dependency versions, and error logs, and describe performance or memory constraints. When materials or constraints change, update them with the next request.
For complex debugging and multi-step derivations, first improve the verifiability of responses through clear task goals, relevant materials, and acceptance criteria. If the current endpoint supports the reasoning_effort setting for grok-4.5, then compare results, latency, and Token usage across available settings; higher settings should not be treated as a quality guarantee. For simple extraction or rewriting tasks, limit the scope of the response to avoid unnecessary elaboration.
Fix a set of real inputs and acceptance requirements, and record answer omissions, citation accuracy, and the amount of manual editing. Applications that integrate tools should also check parameters, permissions, and result write-back; model selection should be based on delivery performance for complete tasks, not just the length of a single response.
The model generates analysis or call requests; actual terminal, search, and file operations are handled by the integrating application. Use only tools and parameters that have been validated for this model, check permissions and execution results, then provide the real output to the model for continued analysis.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples
↗Using Ace Data Cloud with Cherry Studio
Chat client setup · Base URL, API key and model
↗Using Ace Data Cloud with ChatBox
Chat client setup · Base URL, API key and model
↗Input
$0.40per 1M tokens
Official $2.00
Output
$1.20per 1M tokens
Official $6.00
You pay$13,584for 142,800 credits · $0.0951 / credits46% OFF
You're billed in credits. Pick a top-up tier to see its per-credit rate — the larger the top-up, the cheaper each credit.
This is only a basic call example. See the full docs for more parameters and advanced usage.
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