Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3.5 Content Safety by NVIDIA and GPT OSS Safeguard 120B by OpenAI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Nemotron 3.5 Content Safety holds a notable pricing advantage for token ingestion. On output generation, Nemotron 3.5 Content Safety is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT OSS Safeguard 120B provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Nemotron 3.5 Content Safety leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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Nemotron 3.5 Content Safety
开源权重模型NVIDIA 推出的 Nemotron 3.5 Content Safety 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.08/百万 Token，输出价格为 $0.16/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.08 / 1M
$0.16 / 1M
128K tokens
N/A
GPT OSS Safeguard 120B
开源权重模型OpenAI 推出的 GPT OSS Safeguard 120B 人工智能模型，具备 131K 上下文窗口，输入价格为 $0.15/百万 Token，输出价格为 $0.6/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.15 / 1M
$0.60 / 1M
131.1K tokens
41%
核心差异一览
- Nemotron 3.5 Content Safety is 2.7x cheaper for API usage (input + output tokens combined).
- GPT OSS Safeguard 120B has a larger context window (131K vs 128K tokens).
- Nemotron 3.5 Content Safety is faster at 160 tok/s vs 90 tok/s.
| Feature | Nemotron 3.5 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 39 | 52 |
Provider | NVIDIA | OpenAI |
Input Price Cost per million input tokens | $0.08 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.16 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 2K | 33K |
Throughput Speed Tokens generated per second | 160 tok/s | 90 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 41% |
GPQA Diamond Graduate-level reasoning | N/A | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.01 / 1M |
各项指标谁更胜一筹？
At-a-glance decision frameworkEvaluations ongoing
权威基准测试得分对比
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3.5 Content Safety · Strengths & Key Advantages
- 由 NVIDIA 研发的高精度 Multimodal Safety & Compliance Classifier 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.08/1M，输出 $0.16/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
GPT OSS Safeguard 120B · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 Sparse MoE safety-classifier model 架构，支持 131K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.15/1M，输出 $0.6/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron 3.5 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.02 | $0.13 |
Output Tokens / Task Tokens generated per task | 2K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 20K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3.5 Content Safety | GPT OSS Safeguard 120B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-5-content-safety | gpt-oss-safeguard-120b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2026-06 | 2025-10 |
| Knowledge Cutoff | April 2026 | June 2024 |
| Architecture | Multimodal Safety & Compliance Classifier | Sparse MoE safety-classifier model |
| Parameters | 12B | 117B (5.1B active MoE) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($0.08/1M in) across full 128K window | Flat rate ($0.15/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Automatic prefix caching at $0.015/1M (no extra cache-write fee) |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 73 GB VRAM (4-bit Quantized) · 252 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | OpenAI Preparedness Framework Audited & System Card Published |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text |
| Output Modalities | text | text |
Nemotron 3.5 Content Safety vs GPT OSS Safeguard 120B: 常见问题解答 (FAQ)
Which is more intelligent, Nemotron 3.5 Content Safety or GPT OSS Safeguard 120B?
GPT OSS Safeguard 120B is more intelligent. It scores 52 compared with 39 on the composite Intelligence Index.
Which model is faster?
Nemotron 3.5 Content Safety is faster, generating 160 tokens per second compared with 90 tokens per second.
Which model is cheaper to run?
Nemotron 3.5 Content Safety is cheaper at $0.05 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron 3.5 Content Safety has lower latency, with a time to first token of 0.22s compared with 0.52s.
Which model has a larger context window?
GPT OSS Safeguard 120B has a larger context window, supporting 131.1K tokens compared with 128K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Nemotron 3.5 Content Safety if your priority is Live streaming moderation, social media content review, and multimodal safety guardrails. Its performance in demanding tasks justifies its operational token cost. Choose GPT OSS Safeguard 120B if your workflow prioritizes Content moderation, policy screening and safety routing layers. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Nemotron 3.5 Content Safety compare to GPT OSS Safeguard 120B in API token pricing and cost per token?+
Nemotron 3.5 Content Safety costs $0.08 per 1M input tokens and $0.16 per 1M output tokens, whereas GPT OSS Safeguard 120B costs $0.15 input and $0.60 output. Overall, Nemotron 3.5 Content Safety is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Nemotron 3.5 Content Safety or GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety supports a context window of 128K tokens (~96k words), while GPT OSS Safeguard 120B supports 131K tokens (~98k words). Therefore, GPT OSS Safeguard 120B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nemotron 3.5 Content Safety or GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety scores at the frontier of coding benchmarks, while GPT OSS Safeguard 120B scores 41%. For complex autonomous debugging and agentic engineering, GPT OSS Safeguard 120B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Nemotron 3.5 Content Safety and GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT OSS Safeguard 120B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron 3.5 Content Safety and GPT OSS Safeguard 120B?+
Nemotron 3.5 Content Safety generates approximately 160 tokens per second, while GPT OSS Safeguard 120B delivers 90 tokens per second. In latency-sensitive production environments, Nemotron 3.5 Content Safety provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron 3.5 Content Safety and GPT OSS Safeguard 120B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron 3.5 Content Safety to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT OSS Safeguard 120B. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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- Nemotron 3 Content Safety vs GPT OSS Safeguard 120B: Benchmarks, Pricing & Speed Comparison对比
- Nemotron Content Safety Reasoning 4B vs GPT OSS Safeguard 120B: Benchmarks, Pricing & Speed Comparison对比
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs