Llama 3.1 Nemotron 70B Instruct vs Nemotron Mini 4B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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NVIDIA 的 Llama 3.1 Nemotron 70B Instruct 与 NVIDIA 的 Nemotron Mini 4B Instruct 是当前备受关注的两款主力 AI 模型。Llama 3.1 Nemotron 70B Instruct 提供 128K Token 上下文窗口，API 定价为每百万 Token 输入 $0.35 / 输出 $0.70；而 Nemotron Mini 4B Instruct 提供 128K Token 上下文窗口，定价为每百万 Token 输入 $0.05 / 输出 $0.15。以下为两者的基准测试、延迟与成本全面对比。
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Llama 3.1 Nemotron 70B Instruct
开源权重基础大模型NVIDIA 推出的 Llama 3.1 Nemotron 70B Instruct 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 128K 上下文窗口、80 Token/秒生成速度与 47/100 综合智能指数（每百万 Token $0.35 输入 / $0.7 输出）。
$0.35 / 1M
$0.70 / 1M
128K tokens
41.2%
Nemotron Mini 4B Instruct
开源权重基础大模型NVIDIA 推出的 Nemotron Mini 4B Instruct 是一款支持私有云服务器自托管、微调与完全数据主权的开源权重基础大模型，具备 128K 上下文窗口、160 Token/秒生成速度与 35/100 综合智能指数（每百万 Token $0.05 输入 / $0.15 输出）。
$0.05 / 1M
$0.15 / 1M
128K tokens
22%
核心差异一览
- Nemotron Mini 4B Instruct is 5.4x cheaper for API usage (input + output tokens combined).
- Nemotron Mini 4B Instruct is faster at 160 tok/s vs 80 tok/s.
- Llama 3.1 Nemotron 70B Instruct leads on SWE-bench coding (41.2% vs 22.0%).
| Feature | Llama 3.1 Nemotron 70B Instruct | Nemotron Mini 4B Instruct |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 47 | 35 |
Provider | NVIDIA | NVIDIA |
Input Price Cost per million input tokens | $0.35 / 1M | $0.05 / 1M |
Output Price Cost per million output tokens | $0.70 / 1M | $0.15 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.21 / 1M | $0.04 / 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 | 8K | 4K |
Throughput Speed Tokens generated per second | 80 tok/s | 160 tok/s |
SWE-bench Verified Real-world software engineering | 41.2% | 22% |
GPQA Diamond Graduate-level reasoning | 58.5% | 38% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.02 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework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.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
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.
Llama 3.1 Nemotron 70B Instruct · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 8K 输出容量，核心基准表现优异（SWE-bench Verified 41.2% · GPQA Diamond 58.5%）
- 高达 80 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 450 毫秒
- 开放模型权重 (70B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Nemotron Mini 4B Instruct · Strengths & Key Advantages
- 提供 128K 上下文窗口与最高 4K 输出容量，核心基准表现优异（SWE-bench Verified 22% · GPQA Diamond 38%）
- 高达 160 Token/秒的生成吞吐量，首字延迟 (TTFT) 仅约 250 毫秒
- 开放模型权重 (4B)，支持通过 vLLM、SGLang 或 Ollama 在私有 GPU 集群上本地部署与微调
Considerations & Trade-offs
- 在本地以满血精度运行需要较高的 GPU 显存 (VRAM) 与运维基础设施
- 在超长链路的前沿科研推导任务上，深度略逊于顶配重型旗舰推理模型
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.1 Nemotron 70B Instruct | Nemotron Mini 4B Instruct |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.28 | $0.03 |
Output Tokens / Task Tokens generated per task | 32K | 15K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.1 Nemotron 70B Instruct | Nemotron Mini 4B Instruct |
|---|---|---|
| Official API Model ID (SDK) | llama-3-1-nemotron-70b | nemotron-mini-4b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2024-08 |
| Knowledge Cutoff | December 2024 | June 2024 |
| Architecture | RLHF Aligned Llama 3.1 Derivative | Pruned and Distilled Compact Transformer |
| Parameters | 70B | 4B |
| 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 ($0.35/1M in) across full 128K window | Flat rate ($0.05/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Cached input at $0.1/1M tokens | Cached input at $0.02/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | 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 | 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) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 9 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 |
Llama 3.1 Nemotron 70B Instruct vs Nemotron Mini 4B Instruct: 常见问题解答 (FAQ)
Which is more intelligent, Llama 3.1 Nemotron 70B Instruct or Nemotron Mini 4B Instruct?
Llama 3.1 Nemotron 70B Instruct is more intelligent. It scores 47 compared with 35 on the composite Intelligence Index.
Which model is faster?
Nemotron Mini 4B Instruct is faster, generating 160 tokens per second compared with 80 tokens per second.
Which model is cheaper to run?
Nemotron Mini 4B Instruct is cheaper at $0.04 per 1M tokens blended, compared with $0.21 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron Mini 4B Instruct has lower latency, with a time to first token of 0.25s compared with 0.45s.
综合结论
综合来看，Llama 3.1 Nemotron 70B Instruct（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）与 Nemotron Mini 4B Instruct（适合：支持私有云服务器自托管、微调与完全数据主权的开源权重基础任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。）各具优势：Llama 3.1 Nemotron 70B Instruct 在综合智能与编程基准测试中表现更强，Nemotron Mini 4B Instruct 拥有更快的 Token 生成速度，而 Nemotron Mini 4B Instruct 则在规模化 API 部署中具备更高的成本效益。
常见问题 (FAQ)
在编程和深度推理方面，Llama 3.1 Nemotron 70B Instruct 和 Nemotron Mini 4B Instruct 哪个更强？+
在综合智能指数与编程基准评测中，Llama 3.1 Nemotron 70B Instruct 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。
Llama 3.1 Nemotron 70B Instruct 与 Nemotron Mini 4B Instruct 的 API 价格哪个更便宜？+
Llama 3.1 Nemotron 70B Instruct 的 API 价格为输入 $0.35 / 输出 $0.70（每百万 Token），Nemotron Mini 4B Instruct 为输入 $0.05 / 输出 $0.15。综合混合成本来看，Nemotron Mini 4B Instruct 更具性价比。
哪款模型拥有更大的上下文窗口和更快的响应速度？+
Llama 3.1 Nemotron 70B Instruct 支持 128K Token 上下文窗口，而 Nemotron Mini 4B Instruct 支持 128K Token。在流式生成速度方面，Nemotron Mini 4B Instruct 的吞吐表现更快。
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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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: NVIDIA 与 NVIDIA 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。