Head-to-Head Model Comparison

Nemotron 3 Nano 30B A3B vs Nemotron Nano 12B v2 VL

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Nemotron 3 Nano 30B A3BProfile

Nemotron Nano 12B v2 VLProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Nemotron 3 Nano 30B A3B

Open-Weights-Basismodell

Nemotron 3 Nano 30B A3B von NVIDIA ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 262K Kontextfenster, 130 Token/s Durchsatz, einem Intelligenz-Index von 45/100 und $0.1/$0.25 pro 1M

Intelligence Index45 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.25 / 1M

Context Window:

262.1K tokens

SWE-bench Verified:

38%

NVIDIA

Nemotron Nano 12B v2 VL

Nativ Multimodale KI

Nemotron Nano 12B v2 VL von NVIDIA ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 128K Kontextfenster, 110 Token/s Durchsatz, einem Intelligenz-Index von 43/100 und $0.12/$0.3 pro

Intelligence Index43 / 100

Input Token:

$0.12 / 1M

Output Token:

$0.30 / 1M

Context Window:

128K tokens

SWE-bench Verified:

31%

Wichtigste Unterschiede auf einen Blick

Nemotron 3 Nano 30B A3B is 1.2x cheaper for API usage (input + output tokens combined).
Nemotron 3 Nano 30B A3B has a larger context window (262K vs 128K tokens).
Nemotron 3 Nano 30B A3B is faster at 130 tok/s vs 110 tok/s.
Nemotron 3 Nano 30B A3B leads on SWE-bench coding (38.0% vs 31.0%).

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

Feature	Nemotron 3 Nano 30B A3B	Nemotron Nano 12B v2 VL
Intelligence Index Composite reasoning & knowledge score (0-100)	45	43
Provider	NVIDIA	NVIDIA
Input Price Cost per million input tokens	$0.10 / 1M	$0.12 / 1M
Output Price Cost per million output tokens	$0.25 / 1M	$0.30 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.06 / 1M	$0.07 / 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	16K	8K
Throughput Speed Tokens generated per second	130 tok/s	110 tok/s
SWE-bench Verified Real-world software engineering	38%	31%
GPQA Diamond Graduate-level reasoning	54%	47%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.03 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Nemotron 3 Nano 30B A3B

+7.0% SWE-bench lead

Cost

Nemotron 3 Nano 30B A3B

1.2x cheaper monthly runtime

Speed

Nemotron 3 Nano 30B A3B

+18% faster generation

Context

Nemotron 3 Nano 30B A3B

262.1K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Nemotron 3 Nano 30B A3BNemotron Nano 12B v2 VL

SWE-bench Verified (Autonomous Coding)

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

38%vs31%

GPQA Diamond (Doctorate-Level Science & Math)

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

54%vs47%

MMLU-Pro (Multi-task Language Understanding)

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

75%vs70%

LMSYS Chatbot Arena Elo

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

1285ptsvs1250pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

34%vs27%

AIME (Advanced Math Competition)

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

64%vs52%

Long-Context Reasoning

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

76%vs66%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 tok/s vs110 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 Nano 30B A3B0.32s

Nemotron Nano 12B v2 VL0.35s

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

Nemotron 3 Nano 30B A3B4.17s

Nemotron Nano 12B v2 VL4.90s

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 Nano 30B A3B Monthly API Bill

$0.41 / month

$0.1/1M in · $0.25/1M out

Nemotron Nano 12B v2 VL Monthly API Bill

$0.49 / month

$0.12/1M in · $0.3/1M out

Nemotron 3 Nano 30B A3B saves approximately $0.09 / month (18%) under this workload.1.2x Savings

Nemotron 3 Nano 30B A3B · Strengths & Key Advantages

262K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 38% · GPQA Diamond 54%)
Hohe Generierungsgeschwindigkeit von 130 Token/Sekunde bei nur 320 ms Erstlatenz (TTFT)
Offene Modellgewichte (30B (3B 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

Nemotron Nano 12B v2 VL · Strengths & Key Advantages

128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 31% · GPQA Diamond 47%)
Hohe Generierungsgeschwindigkeit von 110 Token/Sekunde bei nur 350 ms Erstlatenz (TTFT)
Offene Modellgewichte (12B) 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 Nano 30B A3B	Nemotron Nano 12B v2 VL
Cost per Task Estimated USD to finish one task	$0.12	$0.09
Output Tokens / Task Tokens generated per task	28K	24K
Reasoning Tokens / Task Thinking tokens burned per task	10K	8K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Nemotron 3 Nano 30B A3B	Nemotron Nano 12B v2 VL
Official API Model ID (SDK)	nemotron-3-nano-30b-a3b	nemotron-nano-12b-v2-vl
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-12	2025-10
Knowledge Cutoff	October 2025	August 2025
Architecture	Sparse Mixture-of-Experts (30B Total / 3B Active)	Compact Vision-Language Transformer
Parameters	30B (3B active MoE)	12B
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 ($0.1/1M in) across full 262K window	Flat rate ($0.12/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	Cached input at $0.03/1M 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) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 19 GB VRAM (4-bit Quantized) · 65 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 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, vision
Output Modalities	text	text

Nemotron 3 Nano 30B A3B vs Nemotron Nano 12B v2 VL: Schnelle Antworten (FAQ)

Which is more intelligent, Nemotron 3 Nano 30B A3B or Nemotron Nano 12B v2 VL?

Nemotron 3 Nano 30B A3B is more intelligent. It scores 45 compared with 43 on the composite Intelligence Index.

Which model is faster?

Nemotron 3 Nano 30B A3B is faster, generating 130 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

Nemotron 3 Nano 30B A3B is cheaper at $0.06 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron 3 Nano 30B A3B has lower latency, with a time to first token of 0.32s compared with 0.35s.

Which model has a larger context window?

Nemotron 3 Nano 30B A3B has a larger context window, supporting 262.1K tokens compared with 128K tokens.

Fazit

Sowohl Nemotron 3 Nano 30B A3B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Nemotron Nano 12B v2 VL (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Nemotron 3 Nano 30B A3B führt bei komplexem Reasoning und Coding, Nemotron 3 Nano 30B A3B liefert den höheren Token-Durchsatz und Nemotron 3 Nano 30B A3B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Nemotron 3 Nano 30B A3B oder Nemotron Nano 12B v2 VL?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Nemotron 3 Nano 30B A3B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Nemotron 3 Nano 30B A3B und Nemotron Nano 12B v2 VL?+

Nemotron 3 Nano 30B A3B kostet $0.10 / $0.25 pro 1M Input-/Output-Tokens, Nemotron Nano 12B v2 VL liegt bei $0.12 / $0.30. Beim gewichteten Gesamtpreis ist Nemotron 3 Nano 30B A3B günstiger.

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

Nemotron 3 Nano 30B A3B bietet 262.1K Tokens Kontext gegenüber 128K Tokens bei Nemotron Nano 12B v2 VL. Bei der Generierungsgeschwindigkeit liegt Nemotron 3 Nano 30B A3B 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 Nano 30B A3B vs Nemotron Nano 12B v2 VL

Beliebige Modelle wechseln & vergleichen

Nemotron 3 Nano 30B A3B

Nemotron Nano 12B v2 VL

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Nemotron 3 Nano 30B A3B · Strengths & Key Advantages

Considerations & Trade-offs

Nemotron Nano 12B v2 VL · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Nemotron 3 Nano 30B A3B vs Nemotron Nano 12B v2 VL: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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