OpenAI: gpt-oss-safeguard-20b

Review this user comment for policy violations: 'Hey @admin can you delete my account? I tried the settings page but the button is broken. Thanks!'

This comment appears safe and does not violate content policies. The user is making a legitimate account deletion request and reporting a technical issue. No harmful content, personal attacks, spam, or policy violations detected. The mention of '@admin' is appropriate given the context of seeking help with account management.

Demonstrates the model's core strength: distinguishing genuine user requests from policy violations. The 131K token context window allows batch processing of thousands of comments in a single call. However, as a safety-focused model, it may be overly cautious with edge cases involving sarcasm or cultural context that could be misread as hostile.

Classify this product review for our moderation queue: 'This is the WORST purchase I've ever made. Complete garbage. The company should be ashamed. Do NOT waste your money!!!'

This review expresses strong negative sentiment but does not violate content policies. The language is emphatic and uses capitalization for emphasis, but remains within acceptable bounds of consumer criticism. No profanity, threats, personal attacks on individuals, or prohibited content detected. This is a legitimate negative review expressing buyer dissatisfaction and should be published.

Shows the model's ability to separate emotional intensity from actual policy violations—critical for platforms that want authentic reviews without censoring legitimate criticism. At $0.30/Mtok output, it's 3-4x cheaper than general-purpose models for this specific task, though you lose the flexibility to handle follow-up questions or content generation.

Analyze this chat message for safety issues: 'I can help you with that coding problem. Here's a Python script that connects to your database: [code snippet with SQL injection vulnerability]'

CAUTION: This message contains code with a potential security vulnerability. While the user's intent appears helpful, the provided code snippet includes patterns consistent with SQL injection risks. This may be unintentional (educational context, beginner mistake) or malicious. Recommend flagging for human review rather than automatic removal, as the context suggests a coding assistance scenario where the security flaw should be addressed through education.

Highlights a nuanced capability: detecting technical safety issues beyond typical content moderation. The model can identify security anti-patterns in code snippets, useful for developer communities. The trade-off is visible here—it correctly flags the risk but lacks the technical depth to explain *why* the code is vulnerable, requiring downstream tooling or human expertise for remediation guidance.

When you need real-time content filtering across 100k+ daily posts

A 12-person social platform handling 150,000 user-generated posts daily needs automated first-pass moderation before human review. OpenAI's gpt-oss-safeguard-20b is purpose-built for this: it flags policy violations, hate speech, and spam at $0.07/Mtok input—roughly $2.10 per million posts analyzed. The 131k token context window handles long-form content and threaded conversations in a single call. Output costs stay low ($0.30/Mtok) because you're generating short classification labels, not essays. If your moderation queue is under 10,000 posts/day, a general-purpose model with a system prompt costs less. Above that threshold, the specialized training pays for itself in reduced false positives and faster review cycles. This model is the right call when moderation accuracy directly impacts user retention and you're processing six figures of content daily.

Why this model handles high-volume support routing without hallucination risk

A 40-person SaaS company receives 8,000 support tickets weekly and needs automated routing to product, billing, or technical teams. OpenAI's gpt-oss-safeguard-20b excels here because ticket classification is a safety-adjacent task: you need reliable category assignment without creative interpretation. At $0.07/Mtok input, analyzing 500-word tickets costs roughly $0.035 per thousand—$280/month for 8,000 tickets. The 131k context window lets you include full ticket history, past interactions, and knowledge base excerpts in one pass. Output tokens are minimal (just routing metadata), keeping costs predictable. If your ticket volume is under 1,000/week, a smaller model with prompt engineering works fine. Above that, this model's consistency and low hallucination rate justify the cost. Buy this when support accuracy is a revenue lever and you're scaling past manual triage.

When contract review needs 100k+ token context and zero creative liberty

A 6-person legal tech startup analyzes vendor contracts for compliance red flags—clauses around liability, data retention, and termination rights. OpenAI's gpt-oss-safeguard-20b handles this because legal review is fundamentally a safety task: you need exhaustive pattern matching without the model inventing clauses that aren't there. The 131k token window fits most commercial contracts in a single pass, including exhibits and schedules. At $0.07/Mtok input, a 40,000-token contract costs $2.80 to analyze; output is structured JSON flagging specific risks, so the $0.30/Mtok output rate stays manageable. If you're reviewing under 50 contracts monthly, a general model with careful prompting is cheaper. Above that, this model's specialized training reduces false negatives—the expensive kind where you miss a liability clause. This is the right call when legal accuracy is non-negotiable and you're processing hundreds of pages per engagement.