Nemotron Nano 9B v2 vs Nemotron Research Reasoning
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron Nano 9B v2 di NVIDIA e Nemotron Research Reasoning di NVIDIA sono due modelli di IA di riferimento. Nemotron Nano 9B v2 offre una finestra di contesto di 131.1K token a $0.08/$0.20 per milione di token, mentre Nemotron Research Reasoning mette a disposizione 128K token di contesto a $0.00/$0.00 per milione di token. Di seguito analizziamo benchmark, latenza e costi.
Cambia e Confronta Qualsiasi Modello
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Nemotron Nano 9B v2
Modello Leggero ed EconomicoNemotron 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
$0.08 / 1M
$0.20 / 1M
131.1K tokens
32.5%
Nemotron Research Reasoning
Ragionamento a Pesi ApertiNemotron Research Reasoning di NVIDIA è un modello di IA progettato per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, con finestra di contesto da 128K, velocità di 45 token/s, indice di intelligenza di 68/10
$0.00 / 1M
$0.00 / 1M
128K tokens
48.5%
Differenze Chiave a Colpo d’Occhio
- 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 |
Chi vince in ogni categoria?
At-a-glance decision frameworkPunteggi Benchmark Verificati
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.
Latenza e Tempo di Risposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulatore Interattivo dei Costi Token
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
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
Nemotron Research Reasoning · Strengths & Key Advantages
- Finestra di contesto da 128K con capacità massima di output fino a 16K token (SWE-bench Verified 48.5% · GPQA Diamond 62%)
- Throughput elevato di 45 token/secondo con latenza al primo token (TTFT) di soli 520 ms
- Pesi aperti (70B) 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.
| 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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
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.) sia Nemotron Research Reasoning (Ideale per il ragionamento Chain-of-Thought a pesi aperti ad alta efficienza algoritmica, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Nemotron Research Reasoning guida nei benchmark di ragionamento e programmazione, Nemotron Nano 9B v2 garantisce una generazione più rapida e Nemotron Research Reasoning offre il miglior rapporto qualità-prezzo per carichi API elevati.
Domande Frequenti (FAQ)
Quale modello è migliore per coding e ragionamento, Nemotron Nano 9B v2 o Nemotron Research Reasoning?+
Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Nemotron Research Reasoning. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.
Qual è la differenza di prezzo API tra Nemotron Nano 9B v2 e Nemotron Research Reasoning?+
Nemotron Nano 9B v2 costa $0.08 / $0.20 per milione di token di input/output, mentre Nemotron Research Reasoning costa $0.00 / $0.00. Nel costo combinato, Nemotron Research Reasoning risulta più economico.
Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+
Nemotron Nano 9B v2 supporta 131.1K token di contesto contro 128K token di Nemotron Research Reasoning, mentre Nemotron Nano 9B v2 offre una velocità di generazione superiore.
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.
Fonti ufficiali: Documentazione API ufficiale di NVIDIA e NVIDIA, schede di sistema e benchmark indipendenti (2026).