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Nemotron Nano 12B v2 VL vs Nemotron Nano 9B v2

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Nemotron Nano 12B v2 VL vs Nemotron Nano 9B v2: Benchmarks, Pricing & Speed Comparison

Nemotron Nano 12B v2 VL di NVIDIA e Nemotron Nano 9B v2 di NVIDIA sono due modelli di IA di riferimento. Nemotron Nano 12B v2 VL offre una finestra di contesto di 128K token a $0.12/$0.30 per milione di token, mentre Nemotron Nano 9B v2 mette a disposizione 131.1K token di contesto a $0.08/$0.20 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

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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%

NVIDIA

Nemotron Nano 9B v2

Modello Leggero ed Economico

Nemotron Nano 9B v2 di NVIDIA è un modello di IA progettato per la massima efficienza dei costi per token e l’inferenza rapida in produzione, con finestra di contesto da 131K, velocità di 140 token/s, indice di intelligenza di 42/100 e tari

Intelligence Index42 / 100
Input Token:

$0.08 / 1M

Output Token:

$0.20 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

32.5%

Differenze Chiave a Colpo d’Occhio

  • Nemotron Nano 9B v2 is 1.5x cheaper for API usage (input + output tokens combined).
  • Nemotron Nano 9B v2 has a larger context window (131K vs 128K tokens).
  • Nemotron Nano 9B v2 is faster at 140 tok/s vs 110 tok/s.
  • Nemotron Nano 9B v2 leads on SWE-bench coding (32.5% vs 31.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureNemotron Nano 12B v2 VLNemotron Nano 9B v2
Intelligence Index
Composite reasoning & knowledge score (0-100)
4342
Provider
NVIDIANVIDIA
Input Price
Cost per million input tokens
$0.12 / 1M$0.08 / 1M
Output Price
Cost per million output tokens
$0.30 / 1M$0.20 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.07 / 1M$0.05 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
110 tok/s140 tok/s
SWE-bench Verified
Real-world software engineering
31%32.5%
GPQA Diamond
Graduate-level reasoning
47%48%
Cached Input Price
Prompt caching discount per 1M tokens
$0.03 / 1M$0.02 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
Nemotron Nano 9B v2
+1.5% SWE-bench lead
Cost
Nemotron Nano 9B v2
1.5x cheaper monthly runtime
Speed
Nemotron Nano 9B v2
+27% faster generation
Context
Nemotron Nano 9B v2
131.1K tokens window

Punteggi Benchmark Verificati

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

Nemotron Nano 12B v2 VLNemotron Nano 9B v2
SWE-bench Verified (Autonomous Coding)

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

31%vs32.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

47%vs48%
MMLU-Pro (Multi-task Language Understanding)

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

70%vs71%
LMSYS Chatbot Arena Elo

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

1250ptsvs1245pts
Terminal-Bench (Agentic Coding & Terminal Use)

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

27%vs28%
AIME (Advanced Math Competition)

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

52%vs54%
Long-Context Reasoning

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

66%vs68%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

110 tok/s vs140 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 Nano 12B v2 VL0.35s
Nemotron Nano 9B v20.28s
Est. End-to-End Response (500 tokens)TTFT + generation time
Nemotron Nano 12B v2 VL4.90s
Nemotron Nano 9B v23.85s

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 Nano 12B v2 VL Monthly API Bill
$0.49 / month
$0.12/1M in · $0.3/1M out
Nemotron Nano 9B v2 Monthly API Bill
$0.33 / month
$0.08/1M in · $0.2/1M out
Nemotron Nano 9B v2 saves approximately $0.16 / month (33%) under this workload.1.5x Savings

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

Nemotron Nano 9B v2 · Strengths & Key Advantages

  • Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 32.5% · GPQA Diamond 48%)
  • Throughput elevato di 140 token/secondo con latenza al primo token (TTFT) di soli 280 ms
  • Pesi aperti (9B) 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 Nano 12B v2 VLNemotron Nano 9B v2
Cost per Task
Estimated USD to finish one task
$0.09$0.06
Output Tokens / Task
Tokens generated per task
24K22K
Reasoning Tokens / Task
Thinking tokens burned per task
8K8K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureNemotron Nano 12B v2 VLNemotron Nano 9B v2
Official API Model ID (SDK)nemotron-nano-12b-v2-vlnemotron-nano-9b-v2
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-102025-08
Knowledge CutoffAugust 2025June 2025
ArchitectureCompact Vision-Language TransformerEfficient Dense Transformer
Parameters12B9B
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 ($0.12/1M in) across full 128K windowFlat rate ($0.08/1M in) across full 131K window
Prompt Cache Policy & TTLCached input at $0.03/1M tokensCached input at $0.02/1M 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) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 7 GB VRAM (4-bit Quantized) · 26 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 6 GB VRAM (4-bit Quantized) · 19 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 Modalitiestext, visiontext
Output Modalitiestexttext

Nemotron Nano 12B v2 VL vs Nemotron Nano 9B v2: Risposte Rapide (FAQ)

Which is more intelligent, Nemotron Nano 12B v2 VL or Nemotron Nano 9B v2?

Nemotron Nano 12B v2 VL is more intelligent. It scores 43 compared with 42 on the composite Intelligence Index.

Which model is faster?

Nemotron Nano 9B v2 is faster, generating 140 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

Nemotron Nano 9B v2 is cheaper at $0.05 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron Nano 9B v2 has lower latency, with a time to first token of 0.28s compared with 0.35s.

Which model has a larger context window?

Nemotron Nano 9B v2 has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Verdetto

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.) sia Nemotron Nano 9B v2 (Ideale per la massima efficienza dei costi per token e l’inferenza rapida in produzione, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Nemotron Nano 12B v2 VL guida nei benchmark di ragionamento e programmazione, Nemotron Nano 9B v2 garantisce una generazione più rapida e Nemotron Nano 9B v2 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Nemotron Nano 12B v2 VL o Nemotron Nano 9B v2?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Nemotron Nano 12B v2 VL. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Nemotron Nano 12B v2 VL e Nemotron Nano 9B v2?+

Nemotron Nano 12B v2 VL costa $0.12 / $0.30 per milione di token di input/output, mentre Nemotron Nano 9B v2 costa $0.08 / $0.20. Nel costo combinato, Nemotron Nano 9B v2 risulta più economico.

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

Nemotron Nano 12B v2 VL supporta 128K token di contesto contro 131.1K token di Nemotron Nano 9B v2, mentre Nemotron Nano 9B v2 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).