DeepSeek R1 is a text conversation model centered on reasoning, suitable for mathematical problem-solving, logical analysis, code debugging, and complex solution justification. Its value lies not only in generating answers, but also in organizing analysis around known conditions, explaining key steps, and supporting follow-up questions.
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.clouddeepseek-r1import 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-r1",
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 output from Chat Completions, and the client is responsible for preserving message history.
Learn what deepseek-r1 can bring to your work.
R1 is suitable for mathematical and logical problems requiring multi-step solutions. When entering a problem, you can also specify known conditions, variable definitions, and the conclusion you want to verify, so the response develops around these constraints. For problems with branches or implicit assumptions, asking it to discuss each case separately makes verification easier than requesting only a final numerical value.
When facing code errors, R1 can explain execution logic, analyze relationships between relevant functions, and organize possible causes of failure. Providing code snippets, error messages, expected behavior, and actual results together is more suitable for forming a verifiable troubleshooting path. Its suggested modifications still need to be tested in a real runtime environment.
The official recommendation for the original R1 is to place task instructions in the user prompt. Mathematical and logical applications can request the final answer, key assumptions, and verification methods at the same time, then continue asking follow-up questions around these deliverables without relying on displaying complete internal reasoning.
Start with specific tasks to identify where the model can be effective.
Provide the full problem statement, your own solution, and the step where you got stuck, then ask R1 to explain the derivation, identify conditions that need to be added, and provide an alternative solution approach. Deliverables can include step-by-step answers, error identification, or a verification checklist. When calculation results are involved, verify them again with a calculator or program to avoid treating textual explanations as computational proof.
Provide minimal reproducible code, error logs, and relevant configuration, and let R1 organize fault hypotheses, suggest an inspection order, and explain behaviors that modifications may affect. It is suitable for producing debugging notes, repair drafts, and testing suggestions, rather than replacing actual execution. When multiple modules are involved, clearly specify call relationships and the runtime environment.
Write candidate solutions, business requirements, and constraints as text, and ask R1 to compare the assumptions, trade-offs, and potential conflicts of each option. You can request a decision table, assumptions to validate, and recommendations for next-step experiments. For questions that depend on the latest information, first provide verified facts so the analysis is grounded in clear data.
Choose based on task complexity, input materials, and expected results.
When a task focuses on mathematical derivation, logical judgment, or code failure analysis, R1's reasoning capabilities are better aligned with the need. If the task is only short Q&A, rewriting, or routine copywriting, there is no need to prioritize it solely for its reasoning capabilities. When selecting a model, use your own representative tasks to compare answer correctness, whether explanations are useful, and the interaction experience, rather than looking only at response length.
deepseek-r1 and deepseek-r1-0528 are different invocation names and should not be swapped arbitrarily when reproducing experiments; you also cannot judge specific capability differences based on their names alone. When maintaining an application, record the complete ID used, prompts, and test cases; when considering a switch to the dated version, focus on retesting boundary-case math problems, code debugging, and existing output formats.
Arrange the input first, then connect it to the appropriate application workflow.
Prepare the problem, variable ranges, and an existing solution, and write out any implicit conditions as well.
Explicitly select deepseek-r1 in a 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 actual verification or testing feedback in the next message.
Design the task directly from the following inputs and acceptance priorities.
Please check every step of the solution below, identify the first invalid derivation; provide a correction process, and verify the final conclusion using boundary values.
Use substitution to check the final answer; focus on whether missing conditions are identified and counterexamples are explained, rather than only the length of the analysis text.
Before formal use, understand the output quality and scope of capabilities.
Answers to common questions when using deepseek-r1.
It is better suited for math problems requiring multi-step analysis, logic problems, code debugging, and technical solution validation. It is recommended to provide the conditions, goals, and attempts already made, and request explanations of key judgments. If the task is only casual conversation or simple rewriting, a general-purpose conversational model can also meet the need.
Provide minimal reproducible code, complete error messages, the runtime environment, and expected results, and explain which checks have already been performed. You can ask it to first list fault hypotheses, then provide verification steps and a draft modification. Before applying suggestions to a project, run tests to confirm that no new behavioral changes have been introduced.
Submit model=deepseek-r1 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.
Have the application save the message history, and include user and assistant messages relevant to the current question in messages. Prepare the problem, variable ranges, and an existing solution, and write out implicit conditions as well. When materials or constraints change, update them with the next request.
You should not make an application's functionality depend on complete internal reasoning. You can ask R1 to provide solution steps, key assumptions, and verification methods to check answers; the client should primarily handle the response text. You can retain key assumptions and verification steps without requiring the application to depend on specific reasoning events.
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 modelInput
$0.36per 1M tokens
Official $0.55
Output
$1.44per 1M tokens
Official $2.19
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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