Groq Circle
Groq Circle
Log In

GPT OSS 20B

Preview
openai/gpt-oss-20b
Try it in Playground
TOKEN SPEED
~1000 TPS
Powered bygroq
INPUT
Text
OUTPUT
Text
CAPABILITIES
Tool Use, Browser Search, Code Execution, JSON Object Mode, Reasoning
OpenAI logoOpenAI
Model card

OpenAI's compact open-weight Mixture-of-Experts (MoE) model with 20B total parameters. Optimized for cost-efficient deployment and agentic workflows, it supports long-context reasoning, tool use, and function calling in a small memory footprint.

PRICING

Input
$0.10
10M / $1
Output
$0.50
2.0M / $1

LIMITS

CONTEXT WINDOW
131,072
MAX OUTPUT TOKENS
32,768

QUANTIZATION

This uses Groq's TruePoint Numerics, which reduces precision only in areas that don't affect accuracy, preserving quality while delivering significant speedup over traditional approaches. Learn more here.

Key Technical Specifications

Model Architecture

Built on a Mixture-of-Experts (MoE) architecture with 20B total parameters (3.6B active per forward pass). Features 24 layers with 32 MoE experts using Top-4 routing per token. Equipped with Grouped Query Attention (8 K/V heads, 64 Q heads) with rotary embeddings and RMSNorm pre-layer normalization.

Performance Metrics

The GPT-OSS 20B model demonstrates exceptional performance across key benchmarks:
  • MMLU (General Reasoning): 85.3%
  • SWE-Bench Verified (Coding): 60.7%
  • AIME 2025 (Math with tools): 98.7%
  • MMMLU (Multilingual): 75.7% average

Use Cases

Low-Latency Agentic Applications
Ideal for cost-efficient deployment in agentic workflows with advanced tool calling capabilities including web browsing, Python execution, and function calling.
Affordable Reasoning & Coding
Provides strong performance in coding, reasoning, and multilingual tasks while maintaining a small memory footprint for budget-conscious deployments.
Tool-Augmented Applications
Excels at applications requiring browser integration, Python code execution, and structured function calling with variable reasoning modes.
Long-Context Processing
Supports up to 131K context length for processing large documents and maintaining conversation history in complex workflows.

Best Practices

  • Utilize variable reasoning modes (low, medium, high) to balance performance and latency based on your specific use case requirements.
  • Provide clear, detailed tool and function definitions with explicit parameters, expected outputs, and constraints for optimal tool use performance.
  • Structure complex tasks into clear steps to leverage the model's agentic reasoning capabilities effectively.
  • Use the full 128K context window for complex, multi-step workflows and comprehensive documentation analysis.
  • Leverage the model's multilingual capabilities by clearly specifying the target language and cultural context when needed.

Get Started with GPT-OSS 20B

Experience openai/gpt-oss-20b on Groq:

shell
pip install groq
Python
from groq import Groq
client = Groq()
completion = client.chat.completions.create(
    model="openai/gpt-oss-20b",
    messages=[
        {
            "role": "user",
            "content": "Explain why fast inference is critical for reasoning models"
        }
    ]
)
print(completion.choices[0].message.content)

Was this page helpful?