Deepseek

https://deepseek.com/

We support ALL Deepseek models, just set deepseek/ as a prefix when sending completion requests

API Key

# env variable
os.environ['DEEPSEEK_API_KEY']

Sample Usage

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""
response = completion(
    model="deepseek/deepseek-chat", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

Sample Usage - Streaming

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""
response = completion(
    model="deepseek/deepseek-chat", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
    stream=True
)

for chunk in response:
    print(chunk)

Supported Models - ALL Deepseek Models Supported!

We support ALL Deepseek models, just set deepseek/ as a prefix when sending completion requests

Model Name	Function Call
deepseek-chat	completion(model="deepseek/deepseek-chat", messages)
deepseek-coder	completion(model="deepseek/deepseek-coder", messages)
deepseek-flash	completion(model="deepseek/deepseek-flash", messages)
deepseek-v4-pro	completion(model="deepseek/deepseek-v4-pro", messages)

Reasoning Models

Model Name	Function Call
deepseek-reasoner	completion(model="deepseek/deepseek-reasoner", messages)

Thinking / Reasoning Mode

Enable thinking mode for DeepSeek reasoner models using thinking or reasoning_effort parameters:

thinking param

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""

resp = completion(
    model="deepseek/deepseek-reasoner",
    messages=[{"role": "user", "content": "What is 2+2?"}],
    thinking={"type": "enabled"},
)
print(resp.choices[0].message.reasoning_content)  # Model's reasoning
print(resp.choices[0].message.content)  # Final answer

reasoning_effort param

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""

resp = completion(
    model="deepseek/deepseek-reasoner",
    messages=[{"role": "user", "content": "What is 2+2?"}],
    reasoning_effort="medium",  # low, medium, high all map to thinking enabled
)
print(resp.choices[0].message.reasoning_content)  # Model's reasoning
print(resp.choices[0].message.content)  # Final answer

note

DeepSeek only supports {"type": "enabled"} - unlike Anthropic, it doesn't support budget_tokens. Any reasoning_effort value other than "none" enables thinking mode.

Basic Usage

SDK

from litellm import completion
import os

os.environ['DEEPSEEK_API_KEY'] = ""
resp = completion(
    model="deepseek/deepseek-reasoner",
    messages=[{"role": "user", "content": "Tell me a joke."}],
)

print(
    resp.choices[0].message.reasoning_content
)

PROXY

1. Setup config.yaml

model_list:
  - model_name: deepseek-reasoner
    litellm_params:
        model: deepseek/deepseek-reasoner
        api_key: os.environ/DEEPSEEK_API_KEY

2. Run proxy

litellm --config /path/to/config.yaml

3. Test it!

curl -L -X POST 'http://0.0.0.0:4000/v1/chat/completions' \
-H 'Content-Type: application/json' \
-H "Authorization: Bearer $LITELLM_API_KEY" \
-d '{
    "model": "deepseek-reasoner",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "text",
            "text": "Hi, how are you ?"
          }
        ]
      }
    ]
}'

Off-Peak Pricing

DeepSeek bills half its listed rate outside its peak hours. Peak hours are 01:00-04:00 and 06:00-10:00 UTC, Monday through Friday, excluding Chinese public holidays. Every other hour is off-peak, including weekends and Chinese public holidays in full. The rates below are USD per 1M tokens, from the DeepSeek pricing page, for deepseek-flash (DeepSeek-V4.1-Flash) and deepseek-v4-pro (DeepSeek-V4-Pro-0813)

Model	Rate	Input	Output	Cache hit
deepseek-flash	Peak	$0.30	$1.20	$0.006
deepseek-flash	Off-peak	$0.15	$0.60	$0.003
deepseek-v4-pro	Peak	$1.32	$3.96	$0.044
deepseek-v4-pro	Off-peak	$0.66	$1.98	$0.022

LiteLLM's cost tracking applies the off-peak rate automatically. The built-in cost map entries for these models carry the schedule above, and each request is priced from the UTC time and weekday it completes at, so tracked spend matches the DeepSeek invoice with no extra configuration. Chinese public holidays are not modeled, so on those days peak hours bill at the peak rate, a small overestimate. The legacy deepseek-v4-flash and deepseek-v4-flash-vision-exp names are served by DeepSeek-V4.1-Flash and billed at the Flash rate, off-peak included. To change the schedule or rates, or to set one on another deployment, see Off-Peak Pricing