---
description: OpenAI's compact open-weight Mixture-of-Experts model with 20B total parameters. Optimized for cost-efficient deployment and agentic workflows.
title: OpenAI GPT-OSS 20B - GroqDocs
image: https://console.groq.com/og_cloudv5.jpg
---

# GPT OSS 20B

`openai/gpt-oss-20b`

[Try it in Playground](https://console.groq.com/playground?model=openai/gpt-oss-20b)

TOKEN SPEED

\~1000 tps

Powered bygroq

INPUT

Text

OUTPUT

Text

CAPABILITIES

[Tool Use](https://console.groq.com/docs/tool-use), [Browser Search](https://console.groq.com/docs/browser-search), [Code Execution](https://console.groq.com/docs/code-execution), [JSON Object Mode](https://console.groq.com/docs/structured-outputs#json-object-mode), [JSON Schema Mode](https://console.groq.com/docs/structured-outputs), [Reasoning](https://console.groq.com/docs/reasoning)

![OpenAI logo](https://console.groq.com/_next/static/media/openailogo.523c87a0.svg)OpenAI

[Model card](https://openai.com/index/gpt-oss-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

$0.075

13M / $1

Cached Input

$0.037

27M / $1

Output

$0.30

3.3M / $1

---

### LIMITS

CONTEXT WINDOW

131,072

---

MAX OUTPUT TOKENS

65,536

---

### 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](https://groq.com/blog/inside-the-lpu-deconstructing-groq-speed).

### [Key Technical Specifications](#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](#get-started-with-gptoss-20b)

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

curlJavaScriptPythonJSON

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)
```