Nemotron 3.5 Content Safety
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。
Technical Specifications
- Provider
- NVIDIA
- Category
- open-source
- Release Date
- 2026-06
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- nemotron-3-5-content-safety
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($0.08/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.02/1M tokens
- Computer Use & Browser GUI Agent
- Text-only completion (No GUI action loop)
- MCP & Parallel Tool Calling
- Prompt-based JSON extraction only
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Nemotron 3.5 Content Safety Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $14.60/mo vs $20 flat plan).
Break-even point: ~1,852 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Nemotron 3.5 Content Safety:
Benchmark Scores & 6-Axis Capability Radar
- Chatbot Arena Elo
- 1200pts
Strengths
- 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
Limitations & 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
Editorial Assessment
Nemotron 3.5 Content Safety 是 NVIDIA 旗下极具代表性的模型，在性能、速度与 API 成本之间实现了卓越平衡。
Best for: 企业级 AI 自动化、代码研发、智能体（Agent）工作流以及 NVIDIA 生态深度集成
Compare Nemotron 3.5 Content Safety
Frequently Asked Questions about Nemotron 3.5 Content Safety
How much does the Nemotron 3.5 Content Safety API cost per 1M tokens?+
Nemotron 3.5 Content Safety by NVIDIA is priced at $0.08 per 1M input tokens and $0.16 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.02 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Nemotron 3.5 Content Safety pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Nemotron 3.5 Content Safety's context window?+
Nemotron 3.5 Content Safety features a 128K tokens context window, which equals approximately 96,000 English words or ~256 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 2,048 tokens.
Can I run Nemotron 3.5 Content Safety locally on my own GPU (Open Weights)?+
Yes, Nemotron 3.5 Content Safety (12B) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does NVIDIA train on data sent to the Nemotron 3.5 Content Safety API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Open-Weights Research & Commercial License. Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Nemotron 3.5 Content Safety perform on coding and reasoning benchmarks?+
Nemotron 3.5 Content Safety achieves a composite Intelligence Index of 39/100, scoring N/A% on SWE-bench Verified, N/A% on GPQA Diamond, N/A% on MMLU-Pro, and 1200 pts on Chatbot Arena Elo.
What is Nemotron 3.5 Content Safety best used for?+
Nemotron 3.5 Content Safety is best suited for: 企业级 AI 自动化、代码研发、智能体（Agent）工作流以及 NVIDIA 生态深度集成. Assessment: Nemotron 3.5 Content Safety 是 NVIDIA 旗下极具代表性的模型，在性能、速度与 API 成本之间实现了卓越平衡。
NVIDIA Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to NVIDIA's flagship reasoning tier:
Nemotron 3 Ultra 550B A55B
Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Content Safety
Nemotron 3 Super 120B A12B
Nemotron VoiceChat
Nemotron Cascade 2 30B A3B
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Nemotron 3.5 Content Safety:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
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.