Kimi K3 is Moonshot AI's open-weight native multimodal model, focused on long-horizon programming, complex reasoning, and knowledge work. The official model card discloses a native context window of one million tokens, supporting the use of text and visual cues together for engineering analysis and content creation. On this platform, messages can be organized through standard Chat Completions; terminals, research retrieval, and file processing are provided by application workflows.
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.
import 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="kimi-k3",
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. Use stream for continuous Chat Completions output, and the client is responsible for saving message history.
Learn what kimi-k3 can bring to your work.
K3 is suited to analyzing requirements, related code, error logs, and test conditions together, rather than merely generating isolated functions. You can ask it to first identify the scope of impact, then propose fixes and validation steps. Retaining file paths, dependencies, and non-modifiable conditions in the input helps produce engineering deliverables that are easier to review.
Combined with screenshots and written requirements, K3 can analyze page layouts, component hierarchies, and interaction intent, then generate interface code or redesign suggestions. It is suitable for starting frontend prototypes from visual references; you should also provide the technology stack, design guidelines, and responsive requirements to avoid asking the model to guess behaviors not shown in the screenshots.
Another focus of K3 is knowledge work: combining materials, data, and multi-round feedback to produce research reports, analytical explanations, or document drafts. Applications can provide real search and calculation results and ask the model to organize sources, conclusions, and items requiring confirmation separately; when further processing is needed, arrange the next steps based on these deliverables.
Start with specific tasks to find where the model can be effective.
Provide the failure symptoms, relevant files, stack traces, and existing tests, and let K3 map the call chain, propose root-cause hypotheses, and deliver modification suggestions, patch drafts, and a regression test checklist. It is suitable for tasks that require linking multiple modules; feeding back actual test results can help it revise its initial assessment rather than stopping at a one-time guess.
Submit page screenshots, component specifications, and the target framework, and ask K3 to produce layout descriptions, component code, and interaction checklists. Then use browser screenshots to provide feedback on discrepancies and iteratively adjust spacing, hierarchy, and responsive behavior. The deliverable is prototype code that can be developed further; visual similarity alone should not be treated as passing functional and accessibility acceptance.
Keep the versions of materials submitted to kimi-k3 and the actual responses, distinguishing 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 definitive records.
Choose based on task complexity, input materials, and expected results.
When a task requires multi-step analysis, contextual code understanding, or repeated use of tool results, K3 is a better fit. K2.6 and K2.5 can serve as alternatives for general conversation and content tasks. Do not directly reuse the Thinking toggle configuration from the K2 series; for short rewrites and simple extraction, compare delivery quality, response wait time, and actual usage before deciding.
Use /v1/chat/completions and explicitly set model=kimi-k3. Existing OpenAI-compatible applications can retain their message and result handling; when integrating, configure the platform address, API Key, and exact model.
Prepare the inputs first, then connect them to the appropriate application workflow.
Prepare the code structure, design screenshots, existing components, and acceptance requirements, and label image and text requirements separately.
Explicitly select kimi-k3 in the Chat Completions request, and organize the background, materials, and output requirements for this run into messages. First use a task with a clearly defined scope to check the response, then include real review or testing feedback in the next round of messages.
Design tasks directly from the following inputs and acceptance priorities.
Please analyze the differences between the screenshot and the existing page, propose a modification order by component, generate an implementation that preserves the API contract, and list acceptance items for desktop and mobile.
After building the application, review the page item by item and verify the interface and interactions; tool execution and browser acceptance are completed by the integrated application environment.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using kimi-k3.
K3 keeps reasoning continuously enabled. Setting reasoning_effort: max is recommended, and it is used this way when omitted. Do not copy the K2.6 thinking switch into K3 requests. If the task is only brief rewriting or classification, consider a model better suited to lightweight tasks rather than relying on disabling reasoning.
You can submit screenshots as image_url image blocks together with framework, style specifications, and interaction requirements for interface analysis and code generation. It outputs text code and suggestions; it does not automatically run the web page as a result. Verification should be completed through actual rendering, screenshot comparison, and interaction testing.
Submit model=kimi-k3 and messages to /v1/chat/completions. Read regular results from choices[].message.content; for streaming calls, obtain incremental results through stream. Use this platform's API Key and set the full base URL according to the SDK you use.
You can explicitly require JSON output in the prompt and specify field names, types, required fields, and allowed values, with examples; the application should perform JSON parsing, structural validation, and business validation, and set retries and exception handling for missing fields, type errors, or truncated results. If using response_format to configure the format, first verify the actual effect of the selected mode in kimi-k3 requests, and do not treat the json_schema field as a guarantee of strict Schema compliance.
Have the application save the message history and include the user and assistant messages relevant to the current question in messages. Prepare the code structure, design screenshots, existing components, and acceptance requirements, and label image and text requirements separately. When materials or constraints change, update them with the next request.
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
$1.14per 1M tokens
Official $2.86
Output
$5.71per 1M tokens
Official $14.29
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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