This is an open-weights safety/moderation-specific model, not a general assistant. Pricing reflects its budget-tier positioning. Availability may be limited or subject to change as it appears to be a research/infrastructure model rather than a consumer product. Verify OpenAI's terms around usage and redistribution for the OSS weights.
Extremely low cost at $0.075/$0.3 per 1M tokens, making high-volume moderation economically viable
Specialized safety classification likely outperforms general models on policy violation detection
Open-weights 20B architecture allows deployment flexibility and fine-tuning for domain-specific safety rules
128K context window supports moderating long documents or conversation threads in a single pass
Weaknesses
Narrow specialization means it performs poorly on general coding, writing, or reasoning tasks compared to GPT-4o or Claude Sonnet 4.6
Not a general-purpose model — using it outside safety/moderation contexts will yield degraded results
Limited community documentation and benchmarks as an OSS safeguard model makes capability assessment uncertain
Real-world use cases
What people actually use gpt-oss-safeguard-20b for.
Classifying user-generated content for hate speech, harassment, or explicit material in a social platform pipeline
Screening LLM outputs for policy violations before serving responses in a production API
Flagging potentially harmful instructions or jailbreak attempts in multi-turn conversations at scale
How gpt-oss-safeguard-20b compares
The nearest models people weigh against it, and what actually separates them.
vs GPT-3.5 Turbo (older v0613) — Against GPT-3.5 Turbo (older v0613) (OpenAI), gpt-oss-safeguard-20b runs about 91% cheaper per token, takes 32x the context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
vs GPT-5 Mini — Against GPT-5 Mini (OpenAI), gpt-oss-safeguard-20b runs about 88% cheaper per token, gives up 3.1x on context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
vs GPT-5.1-Codex-Mini — Against GPT-5.1-Codex-Mini (OpenAI), gpt-oss-safeguard-20b runs about 88% cheaper per token, gives up 3.1x on context and answers slower. Which one wins depends on whether context depth or latency is your constraint.
Price History
gpt-oss-safeguard-20b pricing over time
↓7% since May 31
90 data points · tracked daily since May 31, 2026
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An older versioned snapshot of GPT-3.5 Turbo (v0613), OpenAI's once-dominant mid-tier language model optimized for fast chat completions and instruction following. This specific checkpoint is frozen in time, predating later capability improvements introduced in subsequent GPT-3.5 Turbo updates.
Verdict
A once-useful workhorse now completely overshadowed by cheaper, more capable successors.
Quality score
31%
Pricing
$1.00/1M in
$2.00/1M out
Speed
Very fast
5/5 speed
Context
4k tokens
This is a pinned legacy snapshot (v0613) and may eventually be deprecated by OpenAI. The 4,095-token context window is its most significant practical limitation. OpenAI's own GPT-4o mini offers drastically more context and better quality at a comparable price — strongly consider migrating.
LegacyBudgetFastShort ContextOpenAI
Best for
High-volume, cost-sensitive text tasks like classification, summarization, and simple Q&A where bleeding-edge quality is not required.
GPT-5 Mini is OpenAI's budget-tier distillation of GPT-5, designed for high-volume, cost-sensitive tasks that don't require full flagship reasoning depth. It supersedes GPT-4o with improved instruction following and a massively expanded 400K context window at a fraction of the cost.
Verdict
The new budget default for OpenAI API users: faster, cheaper, and smarter than GPT-4o with a context window that punches well above its price tier.
Quality score
66%
Pricing
$0.25/1M in
$2.00/1M out
Speed
Very fast
5/5 speed
Context
400k tokens
Output cost of $2/1M tokens is higher than some competing budget models (Gemini Flash at ~$0.60/1M output). At scale, output-heavy tasks may erode cost advantages — monitor token ratios carefully. Supersedes GPT-4o, which may be deprecated on a rolling basis.
BudgetFastLong ContextHigh VolumeOpenAI
Best for
High-volume production workloads — chatbots, summarization pipelines, and document Q&A — where cost efficiency matters more than peak reasoning.
GPT-5.1-Codex-Mini is OpenAI's budget-tier coding-specialized model built on the GPT-5.1 architecture, optimized for code generation, completion, and debugging at low cost. It offers a 400K context window, making it practical for large codebases without the price tag of flagship models.
Verdict
The sharpest budget coding model available if you need speed, volume, and a long context window without breaking your API budget.
Quality score
63%
Pricing
$0.25/1M in
$2.00/1M out
Speed
Very fast
5/5 speed
Context
400k tokens
At $2/1M output tokens, costs can accumulate in verbose code-generation tasks — monitor output token usage carefully in agentic loops. Not a general-purpose flagship replacement; best deployed alongside a stronger model for planning/reasoning layers.
CodingBudgetLong ContextFastCodex
Best for
High-volume code generation, autocomplete pipelines, and developer tooling where cost efficiency matters more than peak reasoning depth.
Pricing moves, ranking shifts, and capability updates.
New ModelMar 27, 2026
OpenAI: gpt-oss-safeguard-20b — added to UseRightAI
OpenAI: gpt-oss-safeguard-20b (OpenAI) is now indexed. A purpose-built safety classifier that's excellent at its narrow job and essentially useless outside it.
gpt-oss-safeguard-20b costs $0.07 per million input tokens and $0.2 per million output tokens on the API. A month of 10M input and 2M output tokens runs about $1.10 at list price, before any batch or caching discounts.
What is the context window of gpt-oss-safeguard-20b?
gpt-oss-safeguard-20b has a 128k tokens context window, with up to 16k tokens of output per response. That is the total of prompt plus response the model can hold in one request.
What is the knowledge cutoff of gpt-oss-safeguard-20b?
gpt-oss-safeguard-20b's training data runs through October 2024, and the model was released on October 29, 2025. For anything after that date it needs web search or documents in the prompt.
What is gpt-oss-safeguard-20b best for?
gpt-oss-safeguard-20b is best for automated content moderation pipelines and safety classification at scale.. It is a strong fit when that workflow matters more than the tradeoffs around budget pricing and fast speed.
When should I avoid gpt-oss-safeguard-20b?
You need a general-purpose assistant for coding, writing, analysis, or any task beyond content safety classification and policy enforcement.
What is a cheaper alternative to gpt-oss-safeguard-20b?
GPT-5.6 Terra (OpenAI) at $2.00/1M/1M input against gpt-oss-safeguard-20b's $0.07/1M/1M. Best OpenAI value — near-flagship capability at 60% off. Compare it first if gpt-oss-safeguard-20b's pricing is the thing stopping you.
What is a faster alternative to gpt-oss-safeguard-20b?
GPT-3.5 Turbo (older v0613) — very fast against gpt-oss-safeguard-20b's fast, with 4k tokens of context. Worth the swap when response time is what your users notice rather than the last few points of reasoning depth.
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