Nemotron 3.5 Content Safety vs Nemotron 3 Content Safety
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3.5 Content Safety by NVIDIA and Nemotron 3 Content Safety by NVIDIA 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 Content Safety holds a notable pricing advantage for token ingestion. On output generation, Nemotron 3 Content Safety is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 128K tokens. In latency-sensitive production environments, Nemotron 3 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.
원하는 AI 모델 선택 및 실시간 비교
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Nemotron 3.5 Content Safety
오픈 웨이트 모델NVIDIA의 Nemotron 3.5 Content Safety 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0.08/1M 토큰, 출력 $0.16/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.08 / 1M
$0.16 / 1M
128K tokens
N/A
Nemotron 3 Content Safety
오픈 웨이트 모델NVIDIA의 Nemotron 3 Content Safety 모델은 128K 토큰 컨텍스트 윈도우와 입력 $0.05/1M 토큰, 출력 $0.1/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.05 / 1M
$0.10 / 1M
128K tokens
N/A
한눈에 보는 핵심 차이점
- Nemotron 3 Content Safety is 1.6x cheaper for API usage (input + output tokens combined).
- Nemotron 3 Content Safety is faster at 190 tok/s vs 160 tok/s.
| Feature | Nemotron 3.5 Content Safety | Nemotron 3 Content Safety |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 39 | 37 |
Provider | NVIDIA | NVIDIA |
Input Price Cost per million input tokens | $0.08 / 1M | $0.05 / 1M |
Output Price Cost per million output tokens | $0.16 / 1M | $0.10 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 2K | 2K |
Throughput Speed Tokens generated per second | 160 tok/s | 190 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 1M | $0.01 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron 3.5 Content Safety · Strengths & Key Advantages
- NVIDIA의 첨단 Multimodal Safety & Compliance Classifier 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.08/1M, 출력 $0.16/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Nemotron 3 Content Safety · Strengths & Key Advantages
- NVIDIA의 첨단 Enterprise Safety Classifier Transformer 아키텍처와 최대 128K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.05/1M, 출력 $0.1/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Nemotron 3.5 Content Safety | Nemotron 3 Content Safety |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.02 | $0.01 |
Output Tokens / Task Tokens generated per task | 2K | 2K |
Reasoning Tokens / Task Thinking tokens burned per task | 1K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3.5 Content Safety | Nemotron 3 Content Safety |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-5-content-safety | nemotron-3-content-safety |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-06 | 2026-04 |
| Knowledge Cutoff | April 2026 | February 2026 |
| Architecture | Multimodal Safety & Compliance Classifier | Enterprise Safety Classifier Transformer |
| Parameters | 12B | 8B |
| 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 ($0.05/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.02/1M tokens | Cached input at $0.01/1M tokens |
| Computer Use / GUI Agent | Text-only completion (No GUI action loop) | Text-only completion (No GUI action loop) |
| MCP & Parallel Tool Calling | Prompt-based JSON extraction only | Prompt-based JSON extraction only |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| 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) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| 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 Nemotron 3 Content Safety: 핵심 요약 문답 (FAQ)
Which is more intelligent, Nemotron 3.5 Content Safety or Nemotron 3 Content Safety?
Nemotron 3.5 Content Safety is more intelligent. It scores 39 compared with 37 on the composite Intelligence Index.
Which model is faster?
Nemotron 3 Content Safety is faster, generating 190 tokens per second compared with 160 tokens per second.
Which model is cheaper to run?
Nemotron 3 Content Safety is cheaper at $0.03 per 1M tokens blended, compared with $0.05 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron 3 Content Safety has lower latency, with a time to first token of 0.16s compared with 0.22s.
종합 평가
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 Nemotron 3 Content Safety if your workflow prioritizes Enterprise AI trust & safety, public chatbot protection, and corporate compliance screening. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does Nemotron 3.5 Content Safety compare to Nemotron 3 Content Safety 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 Nemotron 3 Content Safety costs $0.05 input and $0.10 output. Overall, Nemotron 3 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 Nemotron 3 Content Safety?+
Both Nemotron 3.5 Content Safety and Nemotron 3 Content Safety offer an identical context window of 128K tokens (~96k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Nemotron 3.5 Content Safety or Nemotron 3 Content Safety?+
Nemotron 3.5 Content Safety scores at the frontier of coding benchmarks, while Nemotron 3 Content Safety scores competitively for software generation. For complex autonomous debugging and agentic engineering, Nemotron 3.5 Content Safety provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Nemotron 3.5 Content Safety and Nemotron 3 Content Safety?+
Nemotron 3.5 Content Safety features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nemotron 3 Content Safety focuses on standard fast inference.
How does inference speed (tokens per second) compare between Nemotron 3.5 Content Safety and Nemotron 3 Content Safety?+
Nemotron 3.5 Content Safety generates approximately 160 tokens per second, while Nemotron 3 Content Safety delivers 190 tokens per second. In latency-sensitive production environments, Nemotron 3 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 Nemotron 3 Content Safety to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron 3 Content Safety to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron 3.5 Content Safety. 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.
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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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs