Head-to-Head Model Comparison

Nemotron Nano 9B v2 vs Nemotron Research Reasoning

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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Nemotron Nano 9B v2Profile

Nemotron Research ReasoningProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Nemotron Nano 9B v2

Model Ringan & Hemat Biaya

Nemotron 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

Intelligence Index42 / 100

Input Token:

$0.08 / 1M

Output Token:

$0.20 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

32.5%

NVIDIA

Nemotron Research Reasoning

Penalaran Bobot Terbuka

Nemotron Research Reasoning dari NVIDIA adalah model AI yang dirancang untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, menghadirkan jendela konteks 128K, kecepatan 45 token/detik, indeks kecerdasan

Intelligence Index68 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

48.5%

Perbedaan Utama Sekilas

Nemotron Research Reasoning is 1.0x 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 45 tok/s.
Nemotron Research Reasoning leads on SWE-bench coding (48.5% vs 32.5%).

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

Feature	Nemotron Nano 9B v2	Nemotron Research Reasoning
Intelligence Index Composite reasoning & knowledge score (0-100)	42	68
Provider	NVIDIA	NVIDIA
Input Price Cost per million input tokens	$0.08 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.20 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.05 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	131.1K tokens~197 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	16K
Throughput Speed Tokens generated per second	140 tok/s	45 tok/s
SWE-bench Verified Real-world software engineering	32.5%	48.5%
GPQA Diamond Graduate-level reasoning	48%	62%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.00 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Nemotron Research Reasoning

+16.0% SWE-bench lead

Cost

Nemotron Research Reasoning

1.0x cheaper monthly runtime

Speed

Nemotron Nano 9B v2

+211% faster generation

Context

Nemotron Nano 9B v2

131.1K tokens window

Skor Benchmark Terverifikasi

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

Nemotron Nano 9B v2Nemotron Research Reasoning

SWE-bench Verified (Autonomous Coding)

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

32.5%vs48.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

48%vs62%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs82%

LMSYS Chatbot Arena Elo

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

1245ptsvs1340pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

28%vs42%

AIME (Advanced Math Competition)

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

54%vs68%

Long-Context Reasoning

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

68%vs78%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs45 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 Nano 9B v20.28s

Nemotron Research Reasoning0.52s

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

Nemotron Nano 9B v23.85s

Nemotron Research Reasoning11.63s

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 Nano 9B v2 Monthly API Bill

$0.33 / month

$0.08/1M in · $0.2/1M out

Nemotron Research Reasoning Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Nemotron Research Reasoning saves approximately $0.33 / month (100%) under this workload.1.0x Savings

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

Nemotron Research Reasoning · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 48.5% · GPQA Diamond 62%)
Kecepatan generasi tinggi 45 token/detik dengan latensi token pertama (TTFT) hanya 520 ms
Bobot terbuka (70B) 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 9B v2	Nemotron Research Reasoning
Cost per Task Estimated USD to finish one task	$0.06	$0.00
Output Tokens / Task Tokens generated per task	22K	18K
Reasoning Tokens / Task Thinking tokens burned per task	8K	12K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Nemotron Nano 9B v2	Nemotron Research Reasoning
Official API Model ID (SDK)	nemotron-nano-9b-v2	nemotron-research-reasoning
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2025-08
Knowledge Cutoff	June 2025	July 2025
Architecture	Efficient Dense Transformer	Research-Grade Dense Transformer
Parameters	9B	70B
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 ($0.08/1M in) across full 131K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 6 GB VRAM (4-bit Quantized) · 19 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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
Output Modalities	text	text

Nemotron Nano 9B v2 vs Nemotron Research Reasoning: Jawaban Cepat (FAQ)

Which is more intelligent, Nemotron Nano 9B v2 or Nemotron Research Reasoning?

Nemotron Research Reasoning is more intelligent. It scores 68 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 45 tokens per second.

Which model is cheaper to run?

Nemotron Research Reasoning is cheaper at $0.00 per 1M tokens blended, compared with $0.05 (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 0.52s.

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 9B v2 (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Nemotron Research Reasoning (Sangat cocok untuk penalaran rantai pemikiran (Chain-of-Thought) berbobot terbuka yang sangat efisien, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Nemotron Research Reasoning unggul dalam penalaran dan pemrograman, Nemotron Nano 9B v2 memberikan kecepatan generasi token lebih tinggi, dan Nemotron Research Reasoning lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Nemotron Nano 9B v2 atau Nemotron Research Reasoning?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Nemotron Research Reasoning memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Nemotron Nano 9B v2 vs Nemotron Research Reasoning?+

Nemotron Nano 9B v2 mengenakan biaya $0.08 / $0.20 per 1M token input/output, sedangkan Nemotron Research Reasoning sebesar $0.00 / $0.00. Secara keseluruhan, Nemotron Research Reasoning lebih hemat biaya.

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

Nemotron Nano 9B v2 mendukung konteks 131.1K token dibandingkan 128K token pada Nemotron Research Reasoning, sementara Nemotron Nano 9B v2 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 Nano 9B v2 vs Nemotron Research Reasoning

Ganti & Bandingkan Model Apa Saja

Nemotron Nano 9B v2

Nemotron Research Reasoning

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Nemotron Nano 9B v2 · Strengths & Key Advantages

Considerations & Trade-offs

Nemotron Research Reasoning · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Nemotron Nano 9B v2 vs Nemotron Research Reasoning: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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