DeepSeek model
R1
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....
JSON modeLong contextReasoningStreamingTool calling
Best use cases
- Chinese reasoning
- math
- analysis
- code explanation
OpenAI-compatible code example
Keep the OpenAI SDK style, set base_url to NextModel, and use the catalog model ID deepseek-r1.
Python
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://api.nextmodel.app/v1"
)
resp = client.chat.completions.create(
model="deepseek-r1",
messages=[{"role": "user", "content": "Hello from NextModel"}]
)
print(resp.choices[0].message.content)Similar alternatives
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Routingconfigured
Tool callingJSON modeLong contextReasoning
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Tool callingJSON modeLong contextReasoning
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
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Routingconfigured
Tool callingJSON modeLong contextReasoning
OpenRouter if availableOpenRouter public Models API live metadata; public price comes from the registry pricing rule
FAQ
DeepSeek: R1 API questions
What is DeepSeek R1 best for?
It is best compared for Chinese reasoning, math-heavy prompts, and workloads that need reasoning at controlled cost.