How to adjust the model's thinking level with reasoning_effort

The reasoning_effort parameter controls how much effort the model puts into reasoning and planning its response.

Higher effort → more thoughtful, step-by-step, or structured answers
Lower effort → faster, simpler replies

Allowed values:

Value	Behavior
`"low"`	Quick, simple replies
`"medium"`	Balanced depth and speed (good default choice)
`"high"`	More detailed reasoning and structured answers

💡 Tips:

Use "low" when you want short, direct responses.
Use "high" for tasks like explanations, problem solving, reasoning.
"medium" works well for most everyday interactions.

Note:

Setting higher reasoning effort may increase response time slightly, since the model is thinking more.

First, install the SDK:

$ pip install -Uqq sarvamai

Then use the following Python code:

1 from sarvamai import SarvamAI
2 
3 # Initialize the SarvamAI client with your API key
4 client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
5 
6 # Example 1: Using default reasoning_effort (not specified) — balanced response
7 response = client.chat.completions(
8     messages=[
9         {"role": "system", "content": "You are a helpful assistant."},
10         {"role": "user", "content": "Summarize the story of the Ramayana."}
11     ]
12     # reasoning_effort not specified → defaults internally (balanced)
13 )
14 
15 print(response.choices[0].message.content)

1 from sarvamai import SarvamAI
2 
3 client = SarvamAI(api_subscription_key="YOUR_SARVAM_API_KEY")
4 
5 # Example 2: Using reasoning_effort = "high" — more detailed, thoughtful response
6 response = client.chat.completions(
7     messages=[
8         {"role": "system", "content": "You are a helpful assistant."},
9         {"role": "user", "content": "Summarize the story of the Ramayana."}
10     ],
11     reasoning_effort="high"
12 )
13 
14 # Receive assistant's reply as output
15 print(response.choices[0].message.content)