Head-to-Head Model Comparison

Llama 3.1 Nemotron 70B Instruct vs Nemotron Mini 4B Instruct

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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Llama 3.1 Nemotron 70B InstructProfile

Nemotron Mini 4B InstructProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Llama 3.1 Nemotron 70B Instruct

Modelo Base de Pesos Abertos

O Llama 3.1 Nemotron 70B Instruct da NVIDIA é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 80 tokens/s, índice de inteligênc

Intelligence Index47 / 100

Input Token:

$0.35 / 1M

Output Token:

$0.70 / 1M

Context Window:

128K tokens

SWE-bench Verified:

41.2%

NVIDIA

Nemotron Mini 4B Instruct

Modelo Base de Pesos Abertos

O Nemotron Mini 4B Instruct da NVIDIA é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 128K, velocidade de 160 tokens/s, índice de inteligência de

Intelligence Index35 / 100

Input Token:

$0.05 / 1M

Output Token:

$0.15 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22%

Principais Diferenças em Resumo

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%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

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

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Llama 3.1 Nemotron 70B Instruct

+19.2% SWE-bench lead

Cost

Nemotron Mini 4B Instruct

5.4x cheaper monthly runtime

Speed

Nemotron Mini 4B Instruct

+100% faster generation

Context

Tied

128K tokens window

Pontuações de Benchmarks Verificadas

Official evaluation metrics verified by independent third-party research labs.

Llama 3.1 Nemotron 70B InstructNemotron Mini 4B Instruct

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

41.2%vs22%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

58.5%vs38%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

79.2%vs58.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1318ptsvs1180pts

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

38%vs20%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

68%vs38%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

75%vs58%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs160 tok/s

Latência e Tempo de Resposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Llama 3.1 Nemotron 70B Instruct0.45s

Nemotron Mini 4B Instruct0.25s

Est. End-to-End Response (500 tokens)TTFT + generation time

Llama 3.1 Nemotron 70B Instruct6.70s

Nemotron Mini 4B Instruct3.38s

Simulador Interativo de Custo de Tokens

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Llama 3.1 Nemotron 70B Instruct Monthly API Bill

$1.30 / month

$0.35/1M in · $0.7/1M out

Nemotron Mini 4B Instruct Monthly API Bill

$0.24 / month

$0.05/1M in · $0.15/1M out

Nemotron Mini 4B Instruct saves approximately $1.06 / month (82%) under this workload.5.4x Savings

Llama 3.1 Nemotron 70B Instruct · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 41.2% · GPQA Diamond 58.5%)
Taxa de transferência rápida de 80 tokens/segundo com latência inicial (TTFT) de 450 ms
Pesos abertos (70B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Nemotron Mini 4B Instruct · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 4K tokens (SWE-bench Verified 22% · GPQA Diamond 38%)
Taxa de transferência rápida de 160 tokens/segundo com latência inicial (TTFT) de 250 ms
Pesos abertos (4B) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

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

Especificações Técnicas

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: Respostas Rápidas (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.

Veredito

Tanto Llama 3.1 Nemotron 70B Instruct (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Nemotron Mini 4B Instruct (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Llama 3.1 Nemotron 70B Instruct lidera em inteligência geral e programação, Nemotron Mini 4B Instruct oferece geração mais rápida e Nemotron Mini 4B Instruct apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Llama 3.1 Nemotron 70B Instruct ou Nemotron Mini 4B Instruct?+

Nas avaliações de inteligência e código, Llama 3.1 Nemotron 70B Instruct leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Llama 3.1 Nemotron 70B Instruct ou Nemotron Mini 4B Instruct?+

O Llama 3.1 Nemotron 70B Instruct custa $0.35 / $0.70 por 1M de tokens (entrada/saída) e o Nemotron Mini 4B Instruct custa $0.05 / $0.15. No custo combinado, Nemotron Mini 4B Instruct é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Llama 3.1 Nemotron 70B Instruct suporta 128K tokens de contexto contra 128K tokens do Nemotron Mini 4B Instruct, enquanto Nemotron Mini 4B Instruct entrega maior velocidade de geração.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Llama 3.1 Nemotron 70B Instruct vs Nemotron Mini 4B Instruct

Troque e Compare Qualquer Modelo

Llama 3.1 Nemotron 70B Instruct

Nemotron Mini 4B Instruct

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Llama 3.1 Nemotron 70B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Nemotron Mini 4B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Llama 3.1 Nemotron 70B Instruct vs Nemotron Mini 4B Instruct: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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