grok-4 is xAI's Grok 4 reasoning conversational model, suitable for tasks that require breaking down conditions, analyzing code, comparing approaches, and continuing follow-up questions. It is better suited to analysis based on clearly defined materials than to treating a single response as an automated execution program or real-time information query.
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-4import 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",
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 input and standard calling method.
Native model characteristics are for model selection; this platform's input limits, available parameters, and billing are subject to this model's API and pricing. Chat Completions uses stream for continuous output, and the client is responsible for storing message history.
Learn what grok-4 can bring to your work.
Grok 4 focuses on reasoning-based conversation. For problems with multiple conditions, you can ask it to first organize the known conditions, then compare hypotheses, explain the reasoning, and provide a conclusion. It is suitable for mathematical discussions, technical solution trade-offs, and business rule analysis; clearly stating the conditions and acceptance criteria is more helpful for checking answers than simply asking it to “think deeply.”
After submitting code snippets, error messages, and expected behavior, you can ask grok-4 to help identify issues, propose modifications, and explain their impact. Continuing to provide test results helps gradually narrow the scope of investigation. It delivers code and analysis suggestions; compilation, testing, and deployment must still be completed in the actual development environment.
For technical and business rule questions, provide hard constraints, existing data, and acceptable trade-offs together. Ask each response to explain how the conditions affect the recommended approach, then supplement with real materials to continue the discussion, which helps avoid receiving only generic advice.
Start from specific tasks and find where the model can play a role.
Input relevant functions, error stacks, runtime environment, and attempted fixes, and let grok-4 organize possible causes, suggest troubleshooting order, and generate modification drafts. Deliverables can include problem explanations, patch suggestions, and test checklists; then bring actual test results back into the conversation to continue validation rather than directly accepting the initial judgment.
Provide business objectives, constraints, and candidate solutions, and ask the model to compare them using consistent criteria while identifying which conclusions depend on additional assumptions. Suitable for creating technical selection documents, rule conflict lists, or review outlines. Important data should be provided along with the question so that discussions focus on facts in the materials and verifiable decision-making bases.
Input questions, your own solution process, and where you got stuck, and let grok-4 explain key concepts, check steps, and provide targeted practice suggestions. Through continuous follow-up questions, you can move from intuitive explanations to formulas or code expressions. The deliverable is an explanation and practice draft; before formal use, calculation results and boundary conditions should still be checked.
Select based on task complexity, input materials, and expected results.
If an application has already established processes around grok-4's response style, prompts, and test sets, it can continue using it for reasoning, code analysis, and content tasks. Grok 4.7 is a different version and its long context or reasoning configuration should not be directly applied to Grok 4. Whether to upgrade should be based on comparisons of results from the same set of real-world tasks.
Keep the versions of materials submitted to grok-4 and the actual responses, distinguishing between original facts, model suggestions, and actions already completed by the application. Before structured results enter the system, check required fields, value types, and business rules to avoid turning missing information directly into confirmed records.
Arrange inputs first, then connect them to the corresponding application workflow.
Prepare candidate solutions, hard constraints, acceptable trade-offs, and existing measurement data.
Explicitly select grok-4 in Chat Completions requests, and organize the background, materials, and output requirements for this request into messages. First use a task with a clearly defined scope to check the response, then put actual review or testing feedback into the next round of messages.
Design tasks directly from the following inputs and acceptance criteria.
Please check each solution one by one to determine whether it meets the constraints, list the evidence, failure conditions, and experiments that still need to be completed in a table, and then explain the recommendation order.
Review every constraint and number, and continue gathering information for judgments with insufficient evidence; complex reasoning still requires validation with real-world data.
Before formal use, understand the scope of output quality and capabilities.
Answers to common questions about using grok-4.
No. grok-4 is the invocation ID for Grok 4, while Grok 4.7 is a different version. Evaluate their performance for your tasks separately; do not treat Grok 4.7's context length, reasoning configuration, or new features as specifications of Grok 4, and do not judge migration benefits based solely on version names.
Provide the code, error messages, expected results, and runtime environment, and specify which parts cannot be changed. You can first ask it to identify the cause, then generate a repair draft and test checklist. After actual testing, bring failed cases back into the same conversation for further analysis instead of confirming the fix based only on a textual explanation.
Submit model=grok-4 and messages to /v1/chat/completions. Read standard results from choices[].message.content; for streaming calls, obtain incremental results through stream. Use this platform's API Key and configure the complete base URL according to the SDK you use.
Simply sending a question does not mean a real-time search is performed. Recent events should be analyzed together with search tool results or the latest materials you provide. For answers requiring timeliness, request that source dates be noted and keep factual statements separate from model inferences for easier subsequent verification.
Not necessarily. Function calls in the chat interface specify the function and parameters to use; the application still needs to validate, execute, and return the results. When using a conversational tool workflow, also check permissions and execution status; for operations involving writing, publishing, or deletion, retain explicit authorization and failure handling.
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
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This is only a basic call example. See the full docs for more parameters and advanced usage.
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