Grok 3 is a general-purpose conversational model launched by xAI. It excels at applying broad knowledge, mathematics, and programming capabilities to specific problems, and is also suited to organizing lengthy materials and following complex writing requirements. On this platform, you can use grok-3 for text Q&A, streaming responses, and multi-turn conversations, making it suitable for work that requires combining background knowledge, material details, and clear delivery formats.
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-3import 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-3",
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 invocation 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. Use stream for continuous Chat Completions output, and the client is responsible for preserving message history.
Learn what grok-3 can bring to your work.
Grok 3 is suitable not only for answering knowledge-based questions, but also for explaining mathematical approaches, technical concepts, and trade-offs in solutions using background conditions. Providing known conditions, goals, and evaluation criteria in the prompt is better suited to obtaining well-grounded analysis; you can request that conclusions, assumptions, and items to be verified be separated for easier subsequent review.
Long-form comprehension is an important feature of Grok 3, making it suitable for finding relevant passages in large volumes of text, summarizing themes, and comparing different sections. When processing reports or policy materials, it is recommended to retain headings, dates, and paragraph numbers so the model can extract supporting evidence based on questions rather than generating only a broad overview lacking details.
Grok 3 supports both coding tasks and written expression. It can draft implementations from requirement descriptions, explain program logic, and adjust explanatory style for specified readers. Continuous conversation is suitable for gradually adding environment details, errors, and constraints, advancing a one-time generation into an inspectable revision process, though generated code still requires actual testing.
Start with specific tasks and identify where the model can be effective.
Provide the report body, metrics of interest, and comparison dimensions, and have Grok 3 organize key conclusions, contradictory statements, and data that need to be supplemented. Deliverables can be organized as topic-based summaries and a list of questions, with the corresponding paragraph numbers included. This is suitable for pre-meeting preparation, material review, and cross-section information comparison.
Provide the relevant code, runtime environment, error logs, and expected behavior, and have Grok 3 explain possible causes, suggest modifications, and add testing ideas. First limit the modules that need changes, then narrow the scope of the issue through multiple rounds of feedback. This is suitable for script development, code reading, and turning requirements into implementation drafts.
Provide source materials for the topic, target readers, and the article structure, and have Grok 3 generate drafts of technical documentation, training content, or knowledge Q&A. Then add terminology standards, length, and tone requirements to further refine the wording. For fact-based paragraphs, retaining the source basis makes review and reuse easier than simply requesting a writing style.
Choose based on task complexity, input materials, and expected results.
When a task involves background knowledge, document comprehension, and written deliverables at the same time, Grok 3 is better suited to the role of a general-purpose assistant. Grok 3 mini is publicly positioned toward cost-efficient STEM reasoning, especially for problems that do not rely on extensive world knowledge. You can compare answer quality using the same set of real-world tasks, then choose based on available access points and usage limits.
Standard grok-3 calls are suitable for explaining, organizing, and writing around the materials provided. Official usage modes such as Think and DeepSearch each have their own independent workflows; when real-time information or external execution is needed, the application provides the corresponding materials and tools.
Arrange the inputs first, then connect them to the appropriate application workflow.
Provide the full report, target readers, questions of interest, and section identifiers, and specify the summary length.
Explicitly select grok-3 in the Chat Completions request, 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 include actual review or testing feedback in the next round of messages.
Design the task directly from the following inputs and acceptance priorities.
Organize costs, risks, and execution conditions; separate facts, the author's views, and your inferences, and include the original locations for key figures.
Verify figures and citations, and check whether opinions have been presented as facts; the summary should help readers find the original text rather than conceal uncertainties in the material.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using grok-3.
They cannot be directly equated. Grok 3 (Think) is a mode that emphasizes additional reasoning, while grok-3 is the chat invocation ID here. For math or logic tasks, you can request an explanation of the solution and verification of the results, but this does not mean Think is automatically enabled, nor does it guarantee the return of the complete internal reasoning process.
Submit model=grok-3 and messages to /v1/chat/completions. Read regular results from choices[].message.content; use stream to obtain incremental results for streaming calls. Use this platform's API Key, and set the complete base URL according to the SDK you use.
For the dedicated chat endpoint, set stream: true in the request body, read delta.content in choices from streaming events, and concatenate the text in sequence, ending after receiving [DONE]. For non-streaming responses, read message.content in choices; do not parse the two response types as the same format.
Have the application save the message history and include user and assistant messages relevant to the current question in messages. Provide the full report, target audience, issues of concern, and section identifiers, and specify the summary length. When materials or constraints change, update them in the next request.
You cannot assume that every answer is connected to the internet based on the model name alone. Grok 3's knowledge answers and DeepSearch are not the same feature. For recent events, prices, or new versions, you can provide dated materials; when using a session workflow with search tools, then assess timeliness based on the actual retrieval results.
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
↗This is only a basic call example. See the full docs for more parameters and advanced usage.
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