Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

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

Model Dasar Bobot Terbuka

Nemotron 3 Nano 30B A3B dari NVIDIA adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 262K, kecepatan 130 token/detik, indeks kecerdasan 45/100, serta t

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

AI Multimodal Asli

Nemotron Nano 12B v2 VL dari NVIDIA adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 128K, kecepatan 110 token/detik, indeks kecerdasan 43/100, sert

Intelligence Index43 / 100

Input Token:

$0.12 / 1M

Output Token:

$0.30 / 1M

Context Window:

128K tokens

SWE-bench Verified:

31%

Perbedaan Utama Sekilas

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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Jendela konteks 262K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 38% · GPQA Diamond 54%)
Kecepatan generasi tinggi 130 token/detik dengan latensi token pertama (TTFT) hanya 320 ms
Bobot terbuka (30B (3B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Nemotron Nano 12B v2 VL · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 31% · GPQA Diamond 47%)
Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 350 ms
Bobot terbuka (12B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama

Considerations & Trade-offs

Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Nemotron 3 Nano 30B A3B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Nemotron Nano 12B v2 VL (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Nemotron 3 Nano 30B A3B unggul dalam penalaran dan pemrograman, Nemotron 3 Nano 30B A3B memberikan kecepatan generasi token lebih tinggi, dan Nemotron 3 Nano 30B A3B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Nemotron 3 Nano 30B A3B atau Nemotron Nano 12B v2 VL?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Nemotron 3 Nano 30B A3B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Nemotron 3 Nano 30B A3B vs Nemotron Nano 12B v2 VL?+

Nemotron 3 Nano 30B A3B mengenakan biaya $0.10 / $0.25 per 1M token input/output, sedangkan Nemotron Nano 12B v2 VL sebesar $0.12 / $0.30. Secara keseluruhan, Nemotron 3 Nano 30B A3B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Nemotron 3 Nano 30B A3B mendukung konteks 262.1K token dibandingkan 128K token pada Nemotron Nano 12B v2 VL, sementara Nemotron 3 Nano 30B A3B unggul dalam kecepatan output token per detik.

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

Ganti & Bandingkan Model Apa Saja

Nemotron 3 Nano 30B A3B

Nemotron Nano 12B v2 VL

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

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

Spesifikasi Teknis

Nemotron 3 Nano 30B A3B vs Nemotron Nano 12B v2 VL: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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