Llama 3.1 Nemotron 70B Instruct vs Nemotron Mini 4B Instruct
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.1 Nemotron 70B Instruct by NVIDIA and Nemotron Mini 4B Instruct 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 Mini 4B Instruct holds a notable pricing advantage for token ingestion. On output generation, Nemotron Mini 4B Instruct 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 Mini 4B Instruct 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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Llama 3.1 Nemotron 70B Instruct
开源权重模型NVIDIA 推出的 Llama 3.1 Nemotron 70B Instruct 人工智能模型，具备 128K 上下文窗口，输入价格为 $0.35/百万 Token，输出价格为 $0.7/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.35 / 1M
$0.70 / 1M
128K tokens
41.2%
Nemotron Mini 4B Instruct
开源权重模型NVIDIA 推出的 Nemotron Mini 4B Instruct 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
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
- 由 NVIDIA 研发的高精度 RLHF Aligned Llama 3.1 Derivative 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0.35/1M，输出 $0.7/1M），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
Nemotron Mini 4B Instruct · Strengths & Key Advantages
- 由 NVIDIA 研发的高精度 Pruned and Distilled Compact 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 $0.15/1M），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
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
Verdict & Model Selection Recommendation: Choose Llama 3.1 Nemotron 70B Instruct if your priority is Enterprise customer support, synthetic data generation, and local agent orchestration. Its performance in demanding tasks justifies its operational token cost. Choose Nemotron Mini 4B Instruct if your workflow prioritizes Local desktop AI companions, offline edge PCs, and high-speed data extraction. 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 Llama 3.1 Nemotron 70B Instruct compare to Nemotron Mini 4B Instruct in API token pricing and cost per token?+
Llama 3.1 Nemotron 70B Instruct costs $0.35 per 1M input tokens and $0.70 per 1M output tokens, whereas Nemotron Mini 4B Instruct costs $0.05 input and $0.15 output. Overall, Nemotron Mini 4B Instruct 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: Llama 3.1 Nemotron 70B Instruct or Nemotron Mini 4B Instruct?+
Both Llama 3.1 Nemotron 70B Instruct and Nemotron Mini 4B Instruct 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: Llama 3.1 Nemotron 70B Instruct or Nemotron Mini 4B Instruct?+
Llama 3.1 Nemotron 70B Instruct scores 41.2% on SWE-bench Verified while Nemotron Mini 4B Instruct scores 22%. For autonomous software engineering and multi-file debugging, Llama 3.1 Nemotron 70B Instruct holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 3.1 Nemotron 70B Instruct and Nemotron Mini 4B Instruct?+
Llama 3.1 Nemotron 70B Instruct is optimized for low-latency direct generation. In contrast, Nemotron Mini 4B Instruct focuses on standard fast inference.
How does inference speed (tokens per second) compare between Llama 3.1 Nemotron 70B Instruct and Nemotron Mini 4B Instruct?+
Llama 3.1 Nemotron 70B Instruct generates approximately 80 tokens per second, while Nemotron Mini 4B Instruct delivers 160 tokens per second. In latency-sensitive production environments, Nemotron Mini 4B Instruct provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 3.1 Nemotron 70B Instruct and Nemotron Mini 4B Instruct to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron Mini 4B Instruct to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Llama 3.1 Nemotron 70B Instruct. 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