Head-to-Head Model Comparison

Nemotron 3 Super 120B A12B vs Llama 3.1 405B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Nemotron 3 Super 120B A12BProfile

Llama 3.1 405BProfile

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Popular Matchups:

NVIDIA

Nemotron 3 Super 120B A12B

Autonomes Agenten-Modell

Nemotron 3 Super 120B A12B von NVIDIA ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 262K Kontextfenster, 85 Token/s Durchsatz, einem Intelligenz-Index von 53/100 und $0.3/$0

Intelligence Index53 / 100

Input Token:

$0.30 / 1M

Output Token:

$0.80 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

51%

Llama 3.1 405B

Open-Weights-Basismodell

Llama 3.1 405B von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 50 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und kostenlosem Open-Weights-Zuga

Intelligence Index48 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

41.5%

Wichtigste Unterschiede auf einen Blick

Llama 3.1 405B is 1.1x cheaper for API usage (input + output tokens combined).
Nemotron 3 Super 120B A12B has a larger context window (262K vs 128K tokens).
Nemotron 3 Super 120B A12B is faster at 85 tok/s vs 50 tok/s.
Nemotron 3 Super 120B A12B leads on SWE-bench coding (51.0% vs 41.5%).

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

Feature	Nemotron 3 Super 120B A12B	Llama 3.1 405B
Intelligence Index Composite reasoning & knowledge score (0-100)	53	48
Provider	NVIDIA	Meta
Input Price Cost per million input tokens	$0.30 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.80 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.18 / 1M	$0.53 / 1M
Context Window Maximum input + output tokens	262.1K tokens~393 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	33K	8K
Throughput Speed Tokens generated per second	85 tok/s	50 tok/s
SWE-bench Verified Real-world software engineering	51%	41.5%
GPQA Diamond Graduate-level reasoning	64.5%	53%
Cached Input Price Prompt caching discount per 1M tokens	$0.06 / 1M	$0.75 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Nemotron 3 Super 120B A12B

+9.5% SWE-bench lead

Cost

Llama 3.1 405B

1.1x cheaper monthly runtime

Speed

Nemotron 3 Super 120B A12B

+70% faster generation

Context

Nemotron 3 Super 120B A12B

262.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Nemotron 3 Super 120B A12BLlama 3.1 405B

SWE-bench Verified (Autonomous Coding)

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

51%vs41.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

64.5%vs53%

MMLU-Pro (Multi-task Language Understanding)

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

82.8%vs77.2%

LMSYS Chatbot Arena Elo

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

1350ptsvs1288pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

45%vsN/A

AIME (Advanced Math Competition)

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

74%vsN/A

Long-Context Reasoning

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

82%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs50 tok/s

Latenz & Antwortzeit

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

Nemotron 3 Super 120B A12B0.46s

Llama 3.1 405B0.85s

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

Nemotron 3 Super 120B A12B6.34s

Llama 3.1 405B10.85s

Interaktiver Token-Kosten-Simulator

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)

Nemotron 3 Super 120B A12B Monthly API Bill

$1.26 / month

$0.3/1M in · $0.8/1M out

Llama 3.1 405B Monthly API Bill

$1.13 / month

$0/1M in · $0/1M out

Llama 3.1 405B saves approximately $0.14 / month (11%) under this workload.1.1x Savings

Nemotron 3 Super 120B A12B · Strengths & Key Advantages

262K Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 51% · GPQA Diamond 64.5%)
Hohe Generierungsgeschwindigkeit von 85 Token/Sekunde bei nur 460 ms Erstlatenz (TTFT)
Offene Modellgewichte (120B (12B MoE)) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Llama 3.1 405B · Strengths & Key Advantages

128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 41.5% · GPQA Diamond 53%)
Hohe Generierungsgeschwindigkeit von 50 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
Offene Modellgewichte (405B) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Nemotron 3 Super 120B A12B	Llama 3.1 405B
Cost per Task Estimated USD to finish one task	$0.38	$0.20
Output Tokens / Task Tokens generated per task	38K	24K
Reasoning Tokens / Task Thinking tokens burned per task	18K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Nemotron 3 Super 120B A12B	Llama 3.1 405B
Official API Model ID (SDK)	nemotron-3-super-120b-a12b	meta-llama/Llama-3.1-405B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-03	2024-07
Knowledge Cutoff	January 2026	December 2023
Architecture	High-Throughput Sparse MoE (120B Total / 12B Active)	World’s Largest Open Foundation Transformer
Parameters	120B (12B active MoE)	405B
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 ($0.3/1M in) across full 262K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.06/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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) · 74 GB VRAM (4-bit Quantized) · 258 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 251 GB VRAM (4-bit Quantized) · 871 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

Nemotron 3 Super 120B A12B vs Llama 3.1 405B: Schnelle Antworten (FAQ)

Which is more intelligent, Nemotron 3 Super 120B A12B or Llama 3.1 405B?

Nemotron 3 Super 120B A12B is more intelligent. It scores 53 compared with 48 on the composite Intelligence Index.

Which model is faster?

Nemotron 3 Super 120B A12B is faster, generating 85 tokens per second compared with 50 tokens per second.

Which model is cheaper to run?

Nemotron 3 Super 120B A12B is cheaper at $0.18 per 1M tokens blended, compared with $0.53 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron 3 Super 120B A12B has lower latency, with a time to first token of 0.46s compared with 0.85s.

Which model has a larger context window?

Nemotron 3 Super 120B A12B has a larger context window, supporting 262.1K tokens compared with 128K tokens.

Fazit

Sowohl Nemotron 3 Super 120B A12B (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 3.1 405B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Nemotron 3 Super 120B A12B führt bei komplexem Reasoning und Coding, Nemotron 3 Super 120B A12B liefert den höheren Token-Durchsatz und Nemotron 3 Super 120B A12B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Nemotron 3 Super 120B A12B oder Llama 3.1 405B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Nemotron 3 Super 120B A12B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Nemotron 3 Super 120B A12B und Llama 3.1 405B?+

Nemotron 3 Super 120B A12B kostet $0.30 / $0.80 pro 1M Input-/Output-Tokens, Llama 3.1 405B liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Nemotron 3 Super 120B A12B günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Nemotron 3 Super 120B A12B bietet 262.1K Tokens Kontext gegenüber 128K Tokens bei Llama 3.1 405B. Bei der Generierungsgeschwindigkeit liegt Nemotron 3 Super 120B A12B vorn.

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)

Nemotron 3 Super 120B A12B vs Llama 3.1 405B

Beliebige Modelle wechseln & vergleichen

Nemotron 3 Super 120B A12B

Llama 3.1 405B

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Nemotron 3 Super 120B A12B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.1 405B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Nemotron 3 Super 120B A12B vs Llama 3.1 405B: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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