Nemotron 3 Nano 30B A3B vs Nemotron Nano 12B v2 VL
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3 Nano 30B A3B by NVIDIA and Nemotron Nano 12B v2 VL 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 3 Nano 30B A3B holds a notable pricing advantage for token ingestion. On output generation, Nemotron 3 Nano 30B A3B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nemotron 3 Nano 30B A3B provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Nemotron 3 Nano 30B A3B 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.
Cambia e Confronta Qualsiasi Modello
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Nemotron 3 Nano 30B A3B
Modello a Pesi ApertiNemotron 3 Nano 30B A3B di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 262K token e tariffazione API di $0.1/1M token in input e $0.25/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.10 / 1M
$0.25 / 1M
262.1K tokens
38%
Nemotron Nano 12B v2 VL
Modello a Pesi ApertiNemotron Nano 12B v2 VL di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0.12/1M token in input e $0.3/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.12 / 1M
$0.30 / 1M
128K tokens
31%
Differenze Chiave a Colpo d’Occhio
- 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%).
| 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 |
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 3 Nano 30B A3B · Strengths & Key Advantages
- Architettura Sparse Mixture-of-Experts (30B Total / 3B Active) avanzata di NVIDIA con supporto fino a 262K token di contesto
- Ottimo rapporto qualità-prezzo ($0.1/1M input, $0.25/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
Nemotron Nano 12B v2 VL · Strengths & Key Advantages
- Architettura Compact Vision-Language Transformer avanzata di NVIDIA con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($0.12/1M input, $0.3/1M output) e bassa latenza
- Elevate prestazioni nei benchmark di programmazione, logica e comprensione multilingue
Considerations & Trade-offs
- L'utilizzo intensivo di contesti molto ampi richiede un attento monitoraggio dei costi dei token
- Per casi d'uso verticali complessi è consigliabile un prompt engineering mirato
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 |
Specifiche Tecniche
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: Risposte Rapide (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.
Verdetto
Verdict & Model Selection Recommendation: Choose Nemotron 3 Nano 30B A3B if your priority is Local developer workstations, high-concurrency enterprise chatbots, and budget RAG pipelines. Its performance in demanding tasks justifies its operational token cost. Choose Nemotron Nano 12B v2 VL if your workflow prioritizes Robotics perception, automated visual QA, and low-cost multimodal assistants. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Domande Frequenti (FAQ)
How does Nemotron 3 Nano 30B A3B compare to Nemotron Nano 12B v2 VL in API token pricing and cost per token?+
Nemotron 3 Nano 30B A3B costs $0.10 per 1M input tokens and $0.25 per 1M output tokens, whereas Nemotron Nano 12B v2 VL costs $0.12 input and $0.30 output. Overall, Nemotron 3 Nano 30B A3B 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 3 Nano 30B A3B or Nemotron Nano 12B v2 VL?+
Nemotron 3 Nano 30B A3B supports a context window of 262K tokens (~197k words), while Nemotron Nano 12B v2 VL supports 128K tokens (~96k words). Therefore, Nemotron 3 Nano 30B A3B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Nemotron 3 Nano 30B A3B or Nemotron Nano 12B v2 VL?+
Nemotron 3 Nano 30B A3B scores 38% on SWE-bench Verified while Nemotron Nano 12B v2 VL scores 31%. For autonomous software engineering and multi-file debugging, Nemotron 3 Nano 30B A3B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Nemotron 3 Nano 30B A3B and Nemotron Nano 12B v2 VL?+
Nemotron 3 Nano 30B A3B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nemotron Nano 12B v2 VL provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron 3 Nano 30B A3B and Nemotron Nano 12B v2 VL?+
Nemotron 3 Nano 30B A3B generates approximately 130 tokens per second, while Nemotron Nano 12B v2 VL delivers 110 tokens per second. In latency-sensitive production environments, Nemotron 3 Nano 30B A3B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron 3 Nano 30B A3B and Nemotron Nano 12B v2 VL to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron 3 Nano 30B A3B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron Nano 12B v2 VL. 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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs