GetInfoUs
Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
Condividi
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Nemotron 3 Nano 30B A3B vs Nemotron Nano 12B v2 VL: Benchmarks, Pricing & Speed Comparison

Nemotron 3 Nano 30B A3B di NVIDIA e Nemotron Nano 12B v2 VL di NVIDIA sono due modelli di IA di riferimento. Nemotron 3 Nano 30B A3B offre una finestra di contesto di 262.1K token a $0.10/$0.25 per milione di token, mentre Nemotron Nano 12B v2 VL mette a disposizione 128K token di contesto a $0.12/$0.30 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

Cambia e Confronta Qualsiasi Modello

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

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
NVIDIA

Nemotron 3 Nano 30B A3B

Modello Base a Pesi Aperti

Nemotron 3 Nano 30B A3B di NVIDIA è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 262K, velocità di 130 token/s, indice di intelligenza d

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

IA Multimodale Nativa

Nemotron Nano 12B v2 VL di NVIDIA è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 128K, velocità di 110 token/s, indice di intelligenza d

Intelligence Index43 / 100
Input Token:

$0.12 / 1M

Output Token:

$0.30 / 1M

Context Window:

128K tokens

SWE-bench Verified:

31%

Differenze Chiave a Colpo d’Occhio

  • 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 →
FeatureNemotron 3 Nano 30B A3BNemotron Nano 12B v2 VL
Intelligence Index
Composite reasoning & knowledge score (0-100)
4543
Provider
NVIDIANVIDIA
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 text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
16K8K
Throughput Speed
Tokens generated per second
130 tok/s110 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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

100K / day
10K500K1M
30K / day
5K150K300K
50% 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

  • Finestra di contesto da 262K con capacità massima di output fino a 16K token (SWE-bench Verified 38% · GPQA Diamond 54%)
  • Throughput elevato di 130 token/secondo con latenza al primo token (TTFT) di soli 320 ms
  • Pesi aperti (30B (3B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
  • Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Nemotron Nano 12B v2 VL · Strengths & Key Advantages

  • Finestra di contesto da 128K con capacità massima di output fino a 8K token (SWE-bench Verified 31% · GPQA Diamond 47%)
  • Throughput elevato di 110 token/secondo con latenza al primo token (TTFT) di soli 350 ms
  • Pesi aperti (12B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama
Considerations & Trade-offs
  • Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
  • Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Token Use & Task Economics

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

MetricNemotron 3 Nano 30B A3BNemotron Nano 12B v2 VL
Cost per Task
Estimated USD to finish one task
$0.12$0.09
Output Tokens / Task
Tokens generated per task
28K24K
Reasoning Tokens / Task
Thinking tokens burned per task
10K8K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureNemotron 3 Nano 30B A3BNemotron Nano 12B v2 VL
Official API Model ID (SDK)nemotron-3-nano-30b-a3bnemotron-nano-12b-v2-vl
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-122025-10
Knowledge CutoffOctober 2025August 2025
ArchitectureSparse Mixture-of-Experts (30B Total / 3B Active)Compact Vision-Language Transformer
Parameters30B (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 CloudUnlimited (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 windowFlat rate ($0.12/1M in) across full 128K window
Prompt Cache Policy & TTLCached input at $0.02/1M tokensCached input at $0.03/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext, vision
Output Modalitiestexttext

Nemotron 3 Nano 30B A3B vs Nemotron Nano 12B v2 VL: Risposte Rapide (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.

Verdetto

Sia Nemotron 3 Nano 30B A3B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Nemotron Nano 12B v2 VL (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Nemotron 3 Nano 30B A3B guida nei benchmark di ragionamento e programmazione, Nemotron 3 Nano 30B A3B garantisce una generazione più rapida e Nemotron 3 Nano 30B A3B offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Nemotron 3 Nano 30B A3B o Nemotron Nano 12B v2 VL?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Nemotron 3 Nano 30B A3B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Nemotron 3 Nano 30B A3B e Nemotron Nano 12B v2 VL?+

Nemotron 3 Nano 30B A3B costa $0.10 / $0.25 per milione di token di input/output, mentre Nemotron Nano 12B v2 VL costa $0.12 / $0.30. Nel costo combinato, Nemotron 3 Nano 30B A3B risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Nemotron 3 Nano 30B A3B supporta 262.1K token di contesto contro 128K token di Nemotron Nano 12B v2 VL, mentre Nemotron 3 Nano 30B A3B offre una velocità di generazione superiore.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Fonti ufficiali: Documentazione API ufficiale di NVIDIA e NVIDIA, schede di sistema e benchmark indipendenti (2026).