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GPT OSS Safeguard 20B

GPT OSS Safeguard 20B is part of OpenAI's gpt-oss-safeguard family, introduced as a research preview of open-weight reasoning models built specifically for safety classification work. Rather than baking a fixed policy into the weights, the model interprets a developer-provided safety policy at inference time, using chain-of-thought reasoning to classify user messages, completions, and full conversations against whatever rules the operator supplies, which makes it easier to iterate on policies without retraining.

As the smaller of the two release sizes, GPT OSS Safeguard 20B is a fine-tuned derivative of OpenAI's earlier gpt-oss open models and ships under the same permissive Apache 2.0 license, with weights distributed through Hugging Face. The transparent chain-of-thought lets developers audit how a classification decision was reached, offering a practical middle ground for teams that need customizable, reviewable content moderation without building and labeling their own classifier from scratch.

Merge Gatewayopenai/gpt-oss-safeguard-20bgpt-oss

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Model key
openai/gpt-oss-safeguard-20b
Release date
Oct 29, 2025
Last updated
Oct 29, 2025
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.07
Output token cost
$0.20

Limits

Output tokens
4,096 tokens
Context window
4,096 tokens

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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CoverageAnalysis

CNBC's October 29, 2025 report covers the same gpt-oss-safeguard launch, confirming the explicit names gpt-oss-safeguard-120b and gpt-oss-safeguard-20b as fine-tuned versions of OpenAI's gpt-oss models released earlier in August 2025. The article notes these are open-weight models whose parameters are publicly availabl The reporting adds partnership and testing context not present in the primary announcement: OpenAI developed gpt-oss-safeguard in collaboration with Robust Open Online Safety Tools (ROOST), an organization focused on safety infrastructure for AI, and Discord and SafetyKit also helped test the models. Example use cases

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