GPT OSS

Revolutionary open-source language models by OpenAI, democratizing access to advanced AI with GPT-OSS-120B and GPT-OSS-20B models.

Try GPT OSS Interactive Demo

What is GPT OSS?

GPT OSS represents a groundbreaking shift in AI accessibility. Released on August 5, 2025, this open-source GPT model family consists of two powerful variants designed for reasoning and developer tasks.

GPT-OSS-120B

117 billion parameters with 5.1B active per token

GPT-OSS-20B

21 billion parameters with 3.6B active per token

120B

Parameters in GPT-OSS-120B

Apache 2.0
License
Aug 2025
Released

Features of GPT OSS

Discover the revolutionary capabilities that make GPT OSS the future of open-source language models

Efficiency & Accessibility

GPT OSS models are optimized for efficiency. GPT-OSS-120B fits on a single H100 GPU, while GPT-OSS-20B runs on consumer hardware with just 16GB memory.

Advanced Reasoning

Powerful reasoning capabilities comparable to OpenAI's o4-mini and o3-mini models, with adjustable reasoning effort levels for optimal performance.

Safety & Compliance

Comprehensive safety measures tested under OpenAI's Preparedness Framework with a $500,000 Red Teaming Challenge for community input.

Technical Specifications

  • Mixture-of-Experts (MoE) architecture
  • 4-bit quantization (MXFP4) support
  • 128k context length capacity
  • o200k_harmony tokenizer

Development Features

  • Tool use and function calling
  • Chain-of-thought reasoning
  • Structured Outputs API compatibility
  • Instruction following optimization

How to Use GPT OSS

Get started with GPT OSS models in minutes. Follow our comprehensive guide for seamless integration.

01

Download Model

Access GPT OSS models from Hugging Face or official repositories

02

Setup Environment

Configure your hardware requirements and dependencies

03

Integration

Integrate using frameworks like vLLM, Ollama, or llama.cpp

04

Deploy

Deploy to production with cloud or on-premises solutions

Quick Start Example


# Install required packages
pip install transformers torch

# Load GPT OSS model
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "openai/gpt-oss-20b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Generate text
prompt = "Explain the benefits of open-source AI:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
                    

Performance Metrics

Compare GPT OSS models against leading language models with detailed benchmarks

Model Specifications

GPT-OSS-120B 117B Parameters
GPT-OSS-20B 21B Parameters
GPT-4 ~175B Parameters
LLaMA-70B 70B Parameters

Benchmark Scores

Reasoning Tasks 85%
Math & Code 88%
Health & Science 90%
Safety Rating 92%
16GB
Min Memory (20B)
256
Tokens/sec
128K
Context Length
4-bit
MXFP4 Quant

Frequently Asked Questions

Find answers to common questions about GPT OSS models and their implementation