gpt-oss-safeguard-20b

OpenAI chatreasoningtool_use

API ID: openai/gpt-oss-safeguard-20b

Input Price
$0.07
/1M tokens
Output Price
$0.30
/1M tokens

About gpt-oss-safeguard-20b

GPT-OSS is OpenAI's open-source model initiative, providing capable AI with open weights for research and deployment. The models demonstrate OpenAI's commitment to AI accessibility while maintaining strong performance. GPT-OSS variants include different sizes and specialized versions like safeguard models. The open weights enable self-hosting, fine-tuning, and customization. For developers seeking OpenAI-quality AI with deployment flexibility, GPT-OSS offers open-source capability from a frontier lab.

๐Ÿ’ฐ
Price Ranking
#350 lowest price among 599 Chat models

Model Specifications

Context Length
131k
Max Output
66k
Release Date
2025-10-29
Capabilities
chat reasoning tool_use
Input Modalities
text
Output Modalities
text

Best For

  • Complex reasoning, math problems, multi-step logic
  • Conversations, content writing, general assistance

Consider Alternatives For

  • Image understanding (needs vision capability)
  • Simple Q&A (cheaper models available)

๐Ÿ’ฐ Real-World Cost Examples

Estimated monthly costs for common use cases

Personal AI Assistant
$0.12
/month
50 conversations/day, ~500 tokens each
Customer Service Bot
$3.82
/month
1000 tickets/day, ~800 tokens each
Data Analysis Pipeline
$5.29
/month
500 analyses/day, ~2k tokens each

OpenAI Model Lineup

Compare all models from OpenAI to find the best fit

Model Input Output Context Capabilities
gpt-oss-safeguard-20b Current Free Free 131k chat reasoning tool_use
GPT-3.5 Turbo 16k (older v1106) Free Free 16k chat
GPT-4 32k Free Free 33k chat
GPT-4 32k (older v0314) Free Free 33k chat
GPT-3.5 Turbo (older v0301) Free Free 4k chat
GPT-3.5 Turbo 16k Free Free 16k chat

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๐Ÿš€ Quick Start

Get started with gpt-oss-safeguard-20b API

OpenAI Python SDK
from openai import OpenAI
client = OpenAI()

response = client.chat.completions.create(
    model="openai/gpt-oss-safeguard-20b",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)
print(response.choices[0].message.content)