Nemotron Mini 4B Instruct
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。
Technical Specifications
- Provider
- NVIDIA
- Category
- open-source
- Release Date
- 2024-08
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- 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-mini-4b
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in NVIDIA lineup)
- 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 ($0.05/1M in) across full 128K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.02/1M tokens
- Computer Use & Browser GUI Agent
- 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
- 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)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 2 GB VRAM (4-bit Quantized) · 9 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 Mini 4B Instruct Cost, Context, Hardware & API Integration
Pay-as-you-go API is cheaper for this volume (saves $15.65/mo vs $20 flat plan).
Break-even point: ~2,299 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 Mini 4B Instruct:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 22%
- GPQA Diamond
- 38%
- MMLU-Pro
- 58.5%
- HumanEval
- 68.5%
- Terminal-Bench
- 20%
- AIME
- 38%
- Long-Context Reasoning
- 58%
- Chatbot Arena Elo
- 1180pts
Strengths
- 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
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 Mini 4B Instruct 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 Mini 4B Instruct
Frequently Asked Questions about Nemotron Mini 4B Instruct
How much does the Nemotron Mini 4B Instruct API cost per 1M tokens?+
Nemotron Mini 4B Instruct by NVIDIA is priced at $0.05 per 1M input tokens and $0.15 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 Mini 4B Instruct 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 Mini 4B Instruct's context window?+
Nemotron Mini 4B Instruct 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 4,096 tokens.
Can I run Nemotron Mini 4B Instruct locally on my own GPU (Open Weights)?+
Yes, Nemotron Mini 4B Instruct (4B) 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 Mini 4B Instruct 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 Mini 4B Instruct perform on coding and reasoning benchmarks?+
Nemotron Mini 4B Instruct achieves a composite Intelligence Index of 35/100, scoring 22% on SWE-bench Verified, 38% on GPQA Diamond, 58.5% on MMLU-Pro, and 1180 pts on Chatbot Arena Elo.
What is Nemotron Mini 4B Instruct best used for?+
Nemotron Mini 4B Instruct 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 Mini 4B Instruct 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 3.5 Content Safety
Nemotron 3 Nano Omni 30B A3B Reasoning
Nemotron 3 Content Safety
Nemotron 3 Super 120B A12B
Nemotron VoiceChat
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Nemotron Mini 4B Instruct:
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.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
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.
数据来源: NVIDIA Technical Blog & Hugging Face Release (Aug 2024)↗