Reasoning

Reasoning models excel at complex problem-solving tasks that require step-by-step analysis, logical deduction, and structured thinking and solution validation. With Groq inference speed, these types of models can deliver instant reasoning capabilities critical for real-time applications.

Why Speed Matters for Reasoning

Reasoning models are capable of complex decision making with explicit reasoning chains that are part of the token output and used for decision-making, which make low-latency and fast inference essential. Complex problems often require multiple chains of reasoning tokens where each step build on previous results. Low latency compounds benefits across reasoning chains and shaves off minutes of reasoning to a response in seconds.

Supported Model

Reasoning Format

Groq API supports explicit reasoning formats through the reasoning_format parameter, giving you fine-grained control over how the model's reasoning process is presented. This is particularly valuable for valid JSON outputs, debugging, and understanding the model's decision-making process.

Note: The format defaults to raw or parsed when JSON mode or tool use are enabled as those modes do not support raw. If reasoning is explicitly set to raw with JSON mode or tool use enabled, we will return a 400 error.

Options for Reasoning Format

Quick Start

1from groq import Groq
2
3client = Groq()
4completion = client.chat.completions.create(
5    model="deepseek-r1-distill-llama-70b",
6    messages=[
7        {
8            "role": "user",
9            "content": "How many r's are in the word strawberry?"
10        }
11    ],
12    temperature=0.6,
13    max_completion_tokens=1024,
14    top_p=0.95,
15    stream=True,
16    reasoning_format="raw"
17)
18
19for chunk in completion:
20    print(chunk.choices[0].delta.content or "", end="")

Quick Start with Tool use

curl https://api.groq.com//openai/v1/chat/completions -s \
  -H "authorization: bearer $GROQ_API_KEY" \
  -d '{
    "model": "deepseek-r1-distill-llama-70b",
    "messages": [
        {
            "role": "user",
            "content": "What is the weather like in Paris today?"
        }
    ],
    "tools": [
        {
            "type": "function",
            "function": {
                "name": "get_weather",
                "description": "Get current temperature for a given location.",
                "parameters": {
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "City and country e.g. Bogotá, Colombia"
                        }
                    },
                    "required": [
                        "location"
                    ],
                    "additionalProperties": false
                },
                "strict": true
            }
        }
    ]}'

Recommended Configuration Parameters

Optimizing Performance

Temperature and Token Management

The model performs best with temperature settings between 0.5-0.7, with lower values (closer to 0.5) producing more consistent mathematical proofs and higher values allowing for more creative problem-solving approaches. Monitor and adjust your token usage based on the complexity of your reasoning tasks - while the default max_completion_tokens is 1024, complex proofs may require higher limits.

Prompt Engineering

To ensure accurate, step-by-step reasoning while maintaining high performance:

• DeepSeek-R1 works best when all instructions are included directly in user messages rather than system prompts.
• Structure your prompts to request explicit validation steps and intermediate calculations.
• Avoid few-shot prompting and go for zero-shot prompting only.