Nemotron Content Safety Reasoning 4B vs Llama 3.1 Nemotron Safety Guard 8B v3
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron Content Safety Reasoning 4B by NVIDIA and Llama 3.1 Nemotron Safety Guard 8B v3 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, both models offer identical input token rates. On output generation, Llama 3.1 Nemotron Safety Guard 8B v3 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, Llama 3.1 Nemotron Safety Guard 8B v3 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 Content Safety Reasoning 4B
开源权重模型NVIDIA 推出的 Nemotron Content Safety Reasoning 4B 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.05/百万 Token，输出价格为 $0.15/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.05 / 1M
$0.15 / 1M
128K tokens
N/A
Llama 3.1 Nemotron Safety Guard 8B v3
开源权重模型NVIDIA 推出的 Llama 3.1 Nemotron Safety Guard 8B v3 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.05/百万 Token，输出价格为 $0.1/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.05 / 1M
$0.10 / 1M
128K tokens
N/A
核心差异一览
- Llama 3.1 Nemotron Safety Guard 8B v3 is 1.3x cheaper for API usage (input + output tokens combined).
- Llama 3.1 Nemotron Safety Guard 8B v3 is faster at 180 tok/s vs 150 tok/s.
| Feature | Nemotron Content Safety Reasoning 4B | Llama 3.1 Nemotron Safety Guard 8B v3 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 36 |
Provider | NVIDIA | NVIDIA |
Input Price Cost per million input tokens | $0.05 / 1M | $0.05 / 1M |
Output Price Cost per million output tokens | $0.15 / 1M | $0.10 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.03 / 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 | 4K | 2K |
Throughput Speed Tokens generated per second | 150 tok/s | 180 tok/s |
SWE-bench Verified Real-world software engineering | N/A | N/A |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 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.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
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 Content Safety Reasoning 4B · Strengths & Key Advantages
- 由 NVIDIA 研发的高精度 Reasoning-Augmented Safety Classifier 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.05/1M，输出 $0.15/1M），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
Llama 3.1 Nemotron Safety Guard 8B v3 · Strengths & Key Advantages
- 由 NVIDIA 研发的高精度 Specialized Guardrail Classifier Transformer 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.05/1M，输出 $0.1/1M），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 Content Safety Reasoning 4B | Llama 3.1 Nemotron Safety Guard 8B v3 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.02 | $0.01 |
Output Tokens / Task Tokens generated per task | 4K | 2K |
Reasoning Tokens / Task Thinking tokens burned per task | 2K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron Content Safety Reasoning 4B | Llama 3.1 Nemotron Safety Guard 8B v3 |
|---|---|---|
| Official API Model ID (SDK) | nemotron-content-safety-reasoning-4b | llama-3-1-nemotron-safety-guard-8b-v3 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-01 | 2025-10 |
| Knowledge Cutoff | November 2025 | August 2025 |
| Architecture | Reasoning-Augmented Safety Classifier | Specialized Guardrail Classifier Transformer |
| Parameters | 4B | 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 ($0.05/1M in) across full 128K window | Flat rate ($0.05/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.01/1M 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) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 9 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 | text |
| Output Modalities | text | text |
Nemotron Content Safety Reasoning 4B vs Llama 3.1 Nemotron Safety Guard 8B v3: 常见问题解答 (FAQ)
Which is more intelligent, Nemotron Content Safety Reasoning 4B or Llama 3.1 Nemotron Safety Guard 8B v3?
Nemotron Content Safety Reasoning 4B is more intelligent. It scores 38 compared with 36 on the composite Intelligence Index.
Which model is faster?
Llama 3.1 Nemotron Safety Guard 8B v3 is faster, generating 180 tokens per second compared with 150 tokens per second.
Which model has lower latency?
Llama 3.1 Nemotron Safety Guard 8B v3 has lower latency, with a time to first token of 0.18s compared with 0.24s.
综合结论
Verdict & Model Selection Recommendation: Choose Nemotron Content Safety Reasoning 4B if your priority is Auditable content moderation, compliance verification, and high-stakes enterprise governance. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.1 Nemotron Safety Guard 8B v3 if your workflow prioritizes API gateway safety filtering, compliance auditing, and prompt injection defense. 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 Content Safety Reasoning 4B compare to Llama 3.1 Nemotron Safety Guard 8B v3 in API token pricing and cost per token?+
Nemotron Content Safety Reasoning 4B costs $0.05 per 1M input tokens and $0.15 per 1M output tokens, whereas Llama 3.1 Nemotron Safety Guard 8B v3 costs $0.05 input and $0.10 output. Overall, Llama 3.1 Nemotron Safety Guard 8B v3 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 Content Safety Reasoning 4B or Llama 3.1 Nemotron Safety Guard 8B v3?+
Both Nemotron Content Safety Reasoning 4B and Llama 3.1 Nemotron Safety Guard 8B v3 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 Content Safety Reasoning 4B or Llama 3.1 Nemotron Safety Guard 8B v3?+
Nemotron Content Safety Reasoning 4B scores at the frontier of coding benchmarks, while Llama 3.1 Nemotron Safety Guard 8B v3 scores competitively for software generation. For complex autonomous debugging and agentic engineering, Nemotron Content Safety Reasoning 4B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Nemotron Content Safety Reasoning 4B and Llama 3.1 Nemotron Safety Guard 8B v3?+
Nemotron Content Safety Reasoning 4B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Llama 3.1 Nemotron Safety Guard 8B v3 focuses on standard fast inference.
How does inference speed (tokens per second) compare between Nemotron Content Safety Reasoning 4B and Llama 3.1 Nemotron Safety Guard 8B v3?+
Nemotron Content Safety Reasoning 4B generates approximately 150 tokens per second, while Llama 3.1 Nemotron Safety Guard 8B v3 delivers 180 tokens per second. In latency-sensitive production environments, Llama 3.1 Nemotron Safety Guard 8B v3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron Content Safety Reasoning 4B and Llama 3.1 Nemotron Safety Guard 8B v3 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.1 Nemotron Safety Guard 8B v3 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron Content Safety Reasoning 4B. 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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs