A purpose-built safety classifier that's excellent at its narrow job and essentially useless outside it.
20
Coding
15
Writing
35
Research
0
Images
88
Value
60
Long Context
Use this when
Automated content moderation pipelines and safety classification at scale.
Skip this if
Strengths
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
Monthly cost estimate
See what OpenAI: gpt-oss-safeguard-20b actually costs at your usage level
Input tokens / month1M
10k50M
Output tokens / month500k
10k25M
Input cost
$0.075
Output cost
$0.150
Total / month
$0.225
Based on OpenAI: gpt-oss-safeguard-20b API pricing: $0.075/1M input · $0.3/1M output. Real costs vary by provider discounts and caching. Check the provider for exact current rates.
Price History
OpenAI: gpt-oss-safeguard-20b pricing over time
→0% since May 9
4 data points · tracked daily since May 9, 2026
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Recommendations are made independently based on real-world use and public benchmarks. See our disclosures for details.
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OpenAI: GPT-3.5 Turbo (older v0613)
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
Change history
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.
OpenAI: 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 OpenAI: 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 OpenAI: gpt-oss-safeguard-20b?
Google: Gemma 2 9B is the lower-cost option to compare first when you want a similar workflow fit with less token spend.
What is a faster alternative to OpenAI: gpt-oss-safeguard-20b?
OpenAI: GPT-3.5 Turbo (older v0613) is the better pick when response time matters more than maximum depth or premium quality.
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You need a general-purpose assistant for coding, writing, analysis, or any task beyond content safety classification and policy enforcement.
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
Very fast
Best for high-volume, cost-sensitive text tasks like classification, summarization, and simple q&a where bleeding-edge quality is not required.
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
Best for high-volume production workloads — chatbots, summarization pipelines, and document q&a — where cost efficiency matters more than peak reasoning.
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
Best for high-volume code generation, autocomplete pipelines, and developer tooling where cost efficiency matters more than peak reasoning depth.
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.
