- Text Classification
Perceptron Mk1 is Perceptron's highest‑quality proprietary vision‑language model focused on video understanding and embodied visual reasoning. It is notable for combining multimodal inputs (text, images, video)…
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gpt-oss-safeguard-20b
- Text Classification
gpt-oss-safeguard-20b is an OpenAI model name that appears to reference a 20-billion-parameter, safety-focused open-source-style GPT variant, but OpenAI has not publicly released authoritative technical details about it. Information about its architecture, training data, and exact capabilities is not officially documented.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is gpt-oss-safeguard-20b?
gpt-oss-safeguard-20b is a named OpenAI model that suggests a 20B-parameter GPT focused on open-source alignment or safety, but it is not formally documented by OpenAI. In practice, such a model name might be used in experimental or internal contexts for research, prototyping, or safety tooling, but no canonical public description exists. Without official documentation, its concrete production use cases, benchmarks, and deployment patterns are unknown. It is presumably related in spirit to the broader GPT family of large language models from OpenAI, but cannot be placed confidently within a specific, publicly described model lineage.
Model capabilities
5 Core Capabilities
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Conversational AI
Engages in multi-turn, context-aware conversations, following instructions and maintaining coherent dialogue across diverse general-purpose topics.
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Text Translation
Translates written content between multiple languages while preserving meaning and tone, supporting multilingual understanding and communication.
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Content Moderation
Supports detection of sensitive or harmful text content to help implement safety policies and reduce inappropriate or unsafe outputs.
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Visual Reasoning
Interprets and reasons about images, connecting visual details with textual instructions to answer questions or provide descriptions.
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Text Extraction
Reads and extracts textual information from images or documents, enabling downstream analysis, search, or transformation of the captured text.
Use cases
6 Most Valuable Use Cases
- Safety Policy Classification
- Content Moderation Support
- Legal Compliance Triage
- Risky Content Monitoring
- Trust and Safety Workflows
- Guardrail Inference Engine
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for gpt-oss-safeguard-20b–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 120 tps | 99.995% | $0.05 | $0.10 | 256K |
| OpenAI | Global | ~200ms | ~60 tps | 99.9% | ~$0.20 | ~$0.40 | ~128K |
| Anthropic | US East | ~220ms | ~55 tps | 99.9% | ~$0.22 | ~$0.44 | ~200K |
| Google Cloud | Global | ~210ms | ~50 tps | 99.9% | ~$0.24 | ~$0.48 | ~128K |
| Azure OpenAI | Global | ~230ms | ~45 tps | 99.9% | ~$0.26 | ~$0.52 | ~128K |
Performance benchmarks
Technical Specifications
| Metric | gpt-oss-safeguard-20b (OpenAI) | Llama-3.1-8B-Instruct (Meta) | Mistral-Nemo-12B-Instruct (Mistral AI) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~200ms |
| Context Window | 32K | 4K | 8K |
| Input Price ($/1M tokens) | ~$0.70 | ~$0.30 | ~$0.25 |
| Output Price ($/1M tokens) | ~$0.90 | ~$0.60 | ~$0.50 |
| Max Output Tokens | 4K | 1K | 2K |
| Throughput | ~80 tps | ~50 tps | ~60 tps |
| Uptime | ~99.9% | ~99.5% | ~99.5% |
30-day usage via LLM API
- 320M
- Prompt tokens processed (30 days)
- 5.8M
- API requests served (30 days)
- 410M
- Completion tokens generated (30 days)
- 99.8%
- Avg uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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Unified AI Routing
Automatically route each request to the optimal model across providers based on performance, latency, and cost—without changing your application code or client libraries.
One endpoint, every model -
Cost-Aware Orchestration
Control spend with smart model selection, budgets, and policies that downshift to cheaper options when quality allows—so you scale usage without surprise invoices.
Max performance, minimal spend -
Resilient Fallback Logic
Define automatic failover chains so timeouts, rate limits, or provider outages transparently roll to backup models—keeping your AI features online under real-world traffic.
Never ship single-provider -
End-to-End Observability
Get query-level traces, latency, cost, and error analytics across all providers in one place—so you can debug incidents and tune routing with real production data.
See every token, everywhere -
Task-Level Abstractions
Call high-level tasks like chat, RAG, or tools instead of raw models, letting LLM.API handle prompts, parameters, and provider quirks behind a stable interface.
Code to tasks, not models -
High-Throughput Batch Jobs
Run large-scale embeddings, classification, and content generation as efficient batch jobs with concurrency controls and retries—optimized to squeeze more work per dollar.
Bulk workloads, single call
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a guardrail model to classify and filter unsafe user-generated content.
- You need automated moderation of prompts and responses before passing them to larger models.
- Your use case involves batch-scoring large text corpora for safety or policy compliance.
- You need structured safety labels or risk scores to feed downstream business logic.
- Your use case involves building a safety gateway in front of multiple LLM providers.
- You need a dedicated safety model to separate moderation concerns from application logic.
Avoid if...
- You need a general-purpose chat or reasoning model rather than a safety specialist.
- Your workload requires high-quality code generation, debugging help, or complex software design.
- You need creative writing, content generation, or brainstorming beyond classification-style outputs.
- Your workload requires detailed domain reasoning, such as finance, law, or advanced science.
- You need multimodal understanding or generation, including images, audio, or video handling.
- Your workload requires tool use, function calling, or orchestrating multi-step agent workflows.
FAQ
Frequently Asked Questions
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What is gpt-oss-safeguard-20b?
gpt-oss-safeguard-20b is a 20-billion-parameter OpenAI model focused on safe, instruction-following text generation for general-purpose applications.
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What is gpt-oss-safeguard-20b best suited for?
It is best for building safety-conscious chatbots, assistants, and content pipelines that require strong refusal behavior and policy-aligned generations.
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What context window does gpt-oss-safeguard-20b support?
gpt-oss-safeguard-20b supports up to a 32,000-token context window for combined input and output.
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What modalities does gpt-oss-safeguard-20b support?
This model supports text input and text output only; it does not process images, audio, or video.
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How fast is gpt-oss-safeguard-20b when called through LLM.API?
Typical end-to-end latency is in the low-seconds range, depending on prompt length, output length, and your selected LLM.API region.
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How is gpt-oss-safeguard-20b priced on LLM.API?
Pricing is usage-based per input and output token, with exact rates shown in your LLM.API dashboard and billing documentation.
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How do I call gpt-oss-safeguard-20b via the LLM.API?
Set the model field to "gpt-oss-safeguard-20b" in your LLM.API completion or chat endpoint request and provide your LLM.API key.
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How does gpt-oss-safeguard-20b compare to similar 20B models?
Compared to generic 20B open-source models, it emphasizes stronger safety alignment and refusals, sometimes trading off creativity or permissiveness.
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Does gpt-oss-safeguard-20b support streaming responses over LLM.API?
Yes, you can enable token streaming by setting the appropriate streaming flag in your LLM.API request.
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What are the main limitations of gpt-oss-safeguard-20b?
It may refuse borderline content, occasionally over-censor benign requests, hallucinate facts, and lacks image, audio, or tool-native capabilities.
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