Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

İstediğiniz Modelleri Seçin ve Karşılaştırın

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

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

Açık Ağırlıklı Temel Model

NVIDIA tarafından geliştirilen Llama 3.1 Nemotron 70B Instruct; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 128K bağlam penceresi, 80 token/sn hız, 47/100 zeka indeksi ve 1M

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

Açık Ağırlıklı Temel Model

NVIDIA tarafından geliştirilen Nemotron Mini 4B Instruct; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 128K bağlam penceresi, 160 token/sn hız, 35/100 zeka indeksi ve 1M token

Intelligence Index35 / 100

Input Token:

$0.05 / 1M

Output Token:

$0.15 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22%

Bir Bakışta Temel Farklar

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

Hangi Alanda Kim Kazanıyor?

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

Doğrulanmış Benchmark Puanları

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

Gecikme Süresi ve Yanıt Hızı

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

Gerçek Zamanlı Token Maliyet Simülatörü

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

128K bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (SWE-bench Verified 41.2% · GPQA Diamond 58.5%)
Sadece 450 ms ilk token gecikmesi (TTFT) ile saniyede 80 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (70B)

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Nemotron Mini 4B Instruct · Strengths & Key Advantages

128K bağlam penceresi ve yanıt başına maksimum 4K çıktı tokenı kapasitesi (SWE-bench Verified 22% · GPQA Diamond 38%)
Sadece 250 ms ilk token gecikmesi (TTFT) ile saniyede 160 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (4B)

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

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

Teknik Özellikler

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: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Llama 3.1 Nemotron 70B Instruct (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de Nemotron Mini 4B Instruct (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Llama 3.1 Nemotron 70B Instruct karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Nemotron Mini 4B Instruct daha yüksek üretim hızı sağlar; Nemotron Mini 4B Instruct ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Llama 3.1 Nemotron 70B Instruct mi yoksa Nemotron Mini 4B Instruct mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Llama 3.1 Nemotron 70B Instruct önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Llama 3.1 Nemotron 70B Instruct ve Nemotron Mini 4B Instruct API fiyatları nasıl karşılaştırılır?+

Llama 3.1 Nemotron 70B Instruct 1M giriş/çıkış tokeni başına $0.35 / $0.70 ücretlendirilirken, Nemotron Mini 4B Instruct $0.05 / $0.15 seviyesindedir. Karma maliyette Nemotron Mini 4B Instruct daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Llama 3.1 Nemotron 70B Instruct 128K token bağlam penceresi sunarken Nemotron Mini 4B Instruct 128K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Nemotron Mini 4B Instruct öndedir.

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

İstediğiniz Modelleri Seçin ve Karşılaştırın

Llama 3.1 Nemotron 70B Instruct

Nemotron Mini 4B Instruct

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

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

Teknik Özellikler

Llama 3.1 Nemotron 70B Instruct vs Nemotron Mini 4B Instruct: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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