Nemotron Nano 9B v2 vs Nemotron Research Reasoning
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron Nano 9B v2 by NVIDIA and Nemotron Research Reasoning by NVIDIA represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Nemotron Research Reasoning holds a notable pricing advantage for token ingestion. On output generation, Nemotron Research Reasoning is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nemotron Nano 9B v2 provides an expanded memory horizon with 131K tokens. In latency-sensitive production environments, Nemotron Nano 9B v2 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
İstediğiniz Modelleri Seçin ve Karşılaştırın
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Nemotron Nano 9B v2
Açık Ağırlıklı ModelNVIDIA tarafından geliştirilen Nemotron Nano 9B v2, 131K token bağlam penceresi ve 1M girdi başına $0.08, 1M çıktı başına $0.2 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.08 / 1M
$0.20 / 1M
131.1K tokens
32.5%
Nemotron Research Reasoning
Açık Ağırlıklı ModelNVIDIA tarafından geliştirilen Nemotron Research Reasoning, 128K token bağlam penceresi ve 1M girdi başına $0, 1M çıktı başına $0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.00 / 1M
$0.00 / 1M
128K tokens
48.5%
Bir Bakışta Temel Farklar
- 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%).
| 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 |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Nemotron Nano 9B v2 · Strengths & Key Advantages
- NVIDIA imzalı gelişmiş Efficient Dense Transformer mimarisi ve 131K token bağlam kapasitesi
- Dengeli API maliyeti ($0.08/1M girdi, $0.2/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Nemotron Research Reasoning · Strengths & Key Advantages
- NVIDIA imzalı gelişmiş Research-Grade Dense Transformer mimarisi ve 128K token bağlam kapasitesi
- Dengeli API maliyeti ($0/1M girdi, $0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
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 |
Teknik Özellikler
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: Hızlı Yanıtlar (SSS)
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.
Sonuç
Verdict & Model Selection Recommendation: Choose Nemotron Nano 9B v2 if your priority is Robotics control, local PC copilots, and real-time interactive voice agents. Its performance in demanding tasks justifies its operational token cost. Choose Nemotron Research Reasoning if your workflow prioritizes Scientific research, complex data analysis, and academic reasoning workflows. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Sıkça Sorulan Sorular (SSS)
How does Nemotron Nano 9B v2 compare to Nemotron Research Reasoning in API token pricing and cost per token?+
Nemotron Nano 9B v2 costs $0.08 per 1M input tokens and $0.20 per 1M output tokens, whereas Nemotron Research Reasoning costs $0.00 input and $0.00 output. Overall, Nemotron Research Reasoning is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Nemotron Nano 9B v2 or Nemotron Research Reasoning?+
Nemotron Nano 9B v2 supports a context window of 131K tokens (~98k words), while Nemotron Research Reasoning supports 128K tokens (~96k words). Therefore, Nemotron Nano 9B v2 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nemotron Nano 9B v2 or Nemotron Research Reasoning?+
Nemotron Nano 9B v2 scores 32.5% on SWE-bench Verified while Nemotron Research Reasoning scores 48.5%. For autonomous software engineering and multi-file debugging, Nemotron Research Reasoning holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Nemotron Nano 9B v2 and Nemotron Research Reasoning?+
Nemotron Nano 9B v2 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nemotron Research Reasoning provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron Nano 9B v2 and Nemotron Research Reasoning?+
Nemotron Nano 9B v2 generates approximately 140 tokens per second, while Nemotron Research Reasoning delivers 45 tokens per second. In latency-sensitive production environments, Nemotron Nano 9B v2 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron Nano 9B v2 and Nemotron Research Reasoning to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron Research Reasoning to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron Nano 9B v2. This tiered routing pattern cuts overall API bills by up to 70%.
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.
Resmi kaynaklar: GetInfoUs Independent AI Benchmarks & Official API Specs