Nemotron Nano 12B v2 VL vs Nemotron Nano 9B v2
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron Nano 12B v2 VL dari NVIDIA dan Nemotron Nano 9B v2 dari NVIDIA adalah dua model AI unggulan. Nemotron Nano 12B v2 VL menawarkan jendela konteks 128K token dengan harga $0.12/$0.30 per 1 juta token, sedangkan Nemotron Nano 9B v2 menyediakan konteks 131.1K token seharga $0.08/$0.20 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Nemotron Nano 12B v2 VL
AI Multimodal AsliNemotron 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
$0.12 / 1M
$0.30 / 1M
128K tokens
31%
Nemotron Nano 9B v2
Model Ringan & Hemat BiayaNemotron Nano 9B v2 dari NVIDIA adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 131K, kecepatan 140 token/detik, indeks kecerdasan 42/100, serta t
$0.08 / 1M
$0.20 / 1M
131.1K tokens
32.5%
Perbedaan Utama Sekilas
- 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%).
| Feature | Nemotron Nano 12B v2 VL | Nemotron Nano 9B v2 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 43 | 42 |
Provider | NVIDIA | NVIDIA |
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 text | 131.1K tokens~197 A4 pages of text |
Max Output Tokens Per single request | 8K | 8K |
Throughput Speed Tokens generated per second | 110 tok/s | 140 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
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
Nemotron Nano 9B v2 · Strengths & Key Advantages
- Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 32.5% · GPQA Diamond 48%)
- Kecepatan generasi tinggi 140 token/detik dengan latensi token pertama (TTFT) hanya 280 ms
- Bobot terbuka (9B) 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 Nano 12B v2 VL | Nemotron Nano 9B v2 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.09 | $0.06 |
Output Tokens / Task Tokens generated per task | 24K | 22K |
Reasoning Tokens / Task Thinking tokens burned per task | 8K | 8K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron Nano 12B v2 VL | Nemotron Nano 9B v2 |
|---|---|---|
| Official API Model ID (SDK) | nemotron-nano-12b-v2-vl | nemotron-nano-9b-v2 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-10 | 2025-08 |
| Knowledge Cutoff | August 2025 | June 2025 |
| Architecture | Compact Vision-Language Transformer | Efficient Dense Transformer |
| Parameters | 12B | 9B |
| 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 ($0.12/1M in) across full 128K window | Flat rate ($0.08/1M in) across full 131K window |
| Prompt Cache Policy & TTL | Cached input at $0.03/1M tokens | Cached input at $0.02/1M 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / 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 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, vision | text |
| Output Modalities | text | text |
Nemotron Nano 12B v2 VL vs Nemotron Nano 9B v2: Jawaban Cepat (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.
Kesimpulan
Baik 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.) maupun Nemotron Nano 9B v2 (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Nemotron Nano 12B v2 VL unggul dalam penalaran dan pemrograman, Nemotron Nano 9B v2 memberikan kecepatan generasi token lebih tinggi, dan Nemotron Nano 9B v2 lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Nemotron Nano 12B v2 VL atau Nemotron Nano 9B v2?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Nemotron Nano 12B v2 VL memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Nemotron Nano 12B v2 VL vs Nemotron Nano 9B v2?+
Nemotron Nano 12B v2 VL mengenakan biaya $0.12 / $0.30 per 1M token input/output, sedangkan Nemotron Nano 9B v2 sebesar $0.08 / $0.20. Secara keseluruhan, Nemotron Nano 9B v2 lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Nemotron Nano 12B v2 VL mendukung konteks 128K token dibandingkan 131.1K token pada Nemotron Nano 9B v2, sementara Nemotron Nano 9B v2 unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
Sumber resmi: Dokumentasi API resmi NVIDIA & NVIDIA serta evaluasi benchmark independen (2026).