DeepSeek V4 Pro is a larger text model in the V4 series, focused on knowledge tasks, code development, and complex reasoning. The official model card discloses a native context of one million tokens and emphasizes efficiency with long materials and performance on engineering tasks. It is suitable for analyzing requirements, source code, evidence, and constraints together to deliver review findings, remediation plans, or document conclusions that can be checked; platform request limits are subject to this model's API documentation.
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="deepseek-v4-pro",
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 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 output from Chat Completions; the client is responsible for preserving message history.
Learn what deepseek-v4-pro can bring to your work.
Suitable for analyzing relevant source code, change diffs, requirements documentation, and test logs together, rather than merely explaining isolated functions. You can request the issue location, triggering conditions, fix approach, and test supplementation recommendations, so code reviews can focus on verifiable engineering evidence; final changes should still pass compilation, testing, and human review.
When working with reports, contract text, or knowledge-base excerpts, you can compare viewpoints, organize conditions, and identify items requiring clarification around the same question. Preserve chapter names and document numbers in the input, and require conclusions to correspond to original paragraphs, so deliverables can evolve from ordinary summaries into comparison tables, risk lists, or action recommendations that are easy to review.
In addition to natural-language explanations, you can ask the model to generate structured text or JSON according to agreed fields for ticket classification, risk extraction, or code review records. Fields can include business information such as issues, evidence, and recommendations; the application should validate format and content before writing them to business systems. When JSON Schema constraints or function tools are needed, integration testing should first be completed with this model; after the tool-calling workflow is validated, the application executes approved operations and fills back the results before requesting the model to continue analysis.
Start with specific tasks to find where the model can be effective.
Provide change diffs, related modules, and failed tests, and clearly specify concerns such as compatibility, exception handling, or data consistency. Have the model deliver a file-organized risk list, tests that need to be added, and candidate fix proposals, then have developers verify them. This makes it easier to obtain actionable review results than broadly asking, “Is there anything wrong with the code?”
Convert the materials to be compared into text, attach clause numbers, version dates, and points of concern, and request that common requirements, differing conditions, and questions to be confirmed be distinguished. Deliverables can be set as a clause comparison table and an issue list for procurement, project, or legal personnel to assess further; do not treat the model's explanation directly as a final legal conclusion.
Pro's native long context is helpful for combining multiple codebases and specifications, but inputs should still include file paths, chapters, and versions. Requiring answers to correspond to specific sources allows cross-file recommendations to return to actual engineering evidence and also makes subsequent review easier.
Choose based on task complexity, input materials, and expected results.
When a task involves cross-file relationships, multiple constraints, or a reasoning chain that requires repeated checking, consider V4 Pro first. For simple rewrites, short summaries, and text processing with clear rules, compare it with V4 Flash using the same input. Focus on errors and omissions, the amount of manual editing, and task completion; do not decide based only on a single demonstration or model name.
Keep the version of the materials submitted to deepseek-v4-pro and the actual response, distinguishing original facts, model suggestions, and operations 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.
Arrange the input first, then connect it to the corresponding application workflow.
Provide the relevant modules, change diffs, business invariants, and failure logs; do not send only a single error message.
Explicitly select deepseek-v4-pro in the Chat Completions request, organizing the context, materials, and output requirements for this run into messages. First use a task with a clearly defined scope to check the response, then include actual review or test feedback in the next round of messages.
Design the task directly from the inputs and acceptance priorities below.
Please review duplicate submissions and retry-on-failure along the data flow, identify specific paths that may compromise consistency, and provide the minimal fix and verification steps.
Reproduce each candidate issue one by one and check whether the recommendations cross business boundaries; the final acceptance criterion is tests and actual behavior, not the model's self-assessment.
Before formal use, understand the output quality and capability scope.
Answers to common questions about using deepseek-v4-pro.
V4 Pro is better suited as a candidate for complex reasoning, code analysis, and multi-document tasks. For routine text processing, you can also test Flash and compare omissions, errors, and the amount of manual editing using the same materials. deepseek-v4.1-flash is a compatible invocation name for Flash, not an alias for Pro.
This model should be used as a text model; screenshots are not suitable direct input. For PDF analysis, extract the main text first, then provide chapter and page number information; scanned pages also require text recognition. File fields cannot be used to infer that V4 Pro has native PDF or visual understanding capabilities.
Submit model=deepseek-v4-pro 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 user and assistant messages relevant to the current question in messages. Provide relevant modules, change diffs, business invariants, and failure logs; do not send only a single error message. When materials or constraints change, update them with the next request.
It can analyze code and generate modification suggestions. If function tool calling for this model has passed application integration testing, it can further be used to generate invocation requests; function execution in standard message calls is handled by the application and will not automatically run terminals or modify repositories based on a single request. Automated fixes should establish permission boundaries, validate tool parameters and execution results, and use compilation, testing, and human review to determine whether to accept the changes.
Choose a development approach or application suited to this model and follow its setup guide.
↗OpenAI Chat Completions API
SDK installation, authentication and examples
↗DeepSeek Harness Python SDK Setup Guide
Coding tool setup · requires a Coding Plan application key
↗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 modelThis is only a basic call example. See the full docs for more parameters and advanced usage.
View Docs