Nemotron 3 Nano Omni 30B A3B Reasoning vs Nemotron 3 Super 120B A12B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Nemotron 3 Nano Omni 30B A3B Reasoning by NVIDIA and Nemotron 3 Super 120B A12B 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 Omni 30B A3B Reasoning holds a notable pricing advantage for token ingestion. On output generation, Nemotron 3 Nano Omni 30B A3B Reasoning is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Nemotron 3 Super 120B A12B provides an expanded memory horizon with 262K tokens. In latency-sensitive production environments, Nemotron 3 Nano Omni 30B A3B Reasoning 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.
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Nemotron 3 Nano Omni 30B A3B Reasoning
Açık Ağırlıklı ModelNVIDIA tarafından geliştirilen Nemotron 3 Nano Omni 30B A3B Reasoning, 256K token bağlam penceresi ve 1M girdi başına $0.2, 1M çıktı başına $0.6 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.20 / 1M
$0.60 / 1M
256K tokens
42%
Nemotron 3 Super 120B A12B
Açık Ağırlıklı ModelNVIDIA tarafından geliştirilen Nemotron 3 Super 120B A12B, 262K token bağlam penceresi ve 1M girdi başına $0.3, 1M çıktı başına $0.8 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.30 / 1M
$0.80 / 1M
262.1K tokens
51%
Bir Bakışta Temel Farklar
- Nemotron 3 Nano Omni 30B A3B Reasoning is 1.4x cheaper for API usage (input + output tokens combined).
- Nemotron 3 Super 120B A12B has a larger context window (262K vs 256K tokens).
- Nemotron 3 Nano Omni 30B A3B Reasoning is faster at 105 tok/s vs 85 tok/s.
- Nemotron 3 Super 120B A12B leads on SWE-bench coding (51.0% vs 42.0%).
| Feature | Nemotron 3 Nano Omni 30B A3B Reasoning | Nemotron 3 Super 120B A12B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 48 | 53 |
Provider | NVIDIA | NVIDIA |
Input Price Cost per million input tokens | $0.20 / 1M | $0.30 / 1M |
Output Price Cost per million output tokens | $0.60 / 1M | $0.80 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.14 / 1M | $0.18 / 1M |
Context Window Maximum input + output tokens | 256K tokens~384 A4 pages of text | 262.1K tokens~393 A4 pages of text |
Max Output Tokens Per single request | 16K | 33K |
Throughput Speed Tokens generated per second | 105 tok/s | 85 tok/s |
SWE-bench Verified Real-world software engineering | 42% | 51% |
GPQA Diamond Graduate-level reasoning | 56.5% | 64.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.06 / 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 3 Nano Omni 30B A3B Reasoning · Strengths & Key Advantages
- NVIDIA imzalı gelişmiş Omnimodal Sparse MoE (30B Total / 3B Active) mimarisi ve 256K token bağlam kapasitesi
- Dengeli API maliyeti ($0.2/1M girdi, $0.6/1M 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 3 Super 120B A12B · Strengths & Key Advantages
- NVIDIA imzalı gelişmiş High-Throughput Sparse MoE (120B Total / 12B Active) mimarisi ve 262K token bağlam kapasitesi
- Dengeli API maliyeti ($0.3/1M girdi, $0.8/1M 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 3 Nano Omni 30B A3B Reasoning | Nemotron 3 Super 120B A12B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.22 | $0.38 |
Output Tokens / Task Tokens generated per task | 28K | 38K |
Reasoning Tokens / Task Thinking tokens burned per task | 12K | 18K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3 Nano Omni 30B A3B Reasoning | Nemotron 3 Super 120B A12B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-nano-omni-30b-a3b | nemotron-3-super-120b-a12b |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-04 | 2026-03 |
| Knowledge Cutoff | February 2026 | January 2026 |
| Architecture | Omnimodal Sparse MoE (30B Total / 3B Active) | High-Throughput Sparse MoE (120B Total / 12B Active) |
| Parameters | 30B (3B active MoE) | 120B (12B active MoE) |
| 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 ($0.2/1M in) across full 256K window | Flat rate ($0.3/1M in) across full 262K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Cached input at $0.06/1M 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) · Audio (~25–32 tokens/sec) | Text BPE (~4 chars/token) |
| 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) · 74 GB VRAM (4-bit Quantized) · 258 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, audio | text |
| Output Modalities | text | text |
Nemotron 3 Nano Omni 30B A3B Reasoning vs Nemotron 3 Super 120B A12B: Hızlı Yanıtlar (SSS)
Which is more intelligent, Nemotron 3 Nano Omni 30B A3B Reasoning or Nemotron 3 Super 120B A12B?
Nemotron 3 Super 120B A12B is more intelligent. It scores 53 compared with 48 on the composite Intelligence Index.
Which model is faster?
Nemotron 3 Nano Omni 30B A3B Reasoning is faster, generating 105 tokens per second compared with 85 tokens per second.
Which model is cheaper to run?
Nemotron 3 Nano Omni 30B A3B Reasoning is cheaper at $0.14 per 1M tokens blended, compared with $0.18 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron 3 Nano Omni 30B A3B Reasoning has lower latency, with a time to first token of 0.38s compared with 0.46s.
Which model has a larger context window?
Nemotron 3 Super 120B A12B has a larger context window, supporting 262.1K tokens compared with 256K tokens.
Sonuç
Verdict & Model Selection Recommendation: Choose Nemotron 3 Nano Omni 30B A3B Reasoning if your priority is Interactive robotics, smart camera analysis, and multimodal customer kiosks. Its performance in demanding tasks justifies its operational token cost. Choose Nemotron 3 Super 120B A12B if your workflow prioritizes Enterprise multi-agent architectures, massive data extraction, and autonomous coding. 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 3 Nano Omni 30B A3B Reasoning compare to Nemotron 3 Super 120B A12B in API token pricing and cost per token?+
Nemotron 3 Nano Omni 30B A3B Reasoning costs $0.20 per 1M input tokens and $0.60 per 1M output tokens, whereas Nemotron 3 Super 120B A12B costs $0.30 input and $0.80 output. Overall, Nemotron 3 Nano Omni 30B A3B 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 3 Nano Omni 30B A3B Reasoning or Nemotron 3 Super 120B A12B?+
Nemotron 3 Nano Omni 30B A3B Reasoning supports a context window of 256K tokens (~192k words), while Nemotron 3 Super 120B A12B supports 262K tokens (~197k words). Therefore, Nemotron 3 Super 120B A12B 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 Omni 30B A3B Reasoning or Nemotron 3 Super 120B A12B?+
Nemotron 3 Nano Omni 30B A3B Reasoning scores 42% on SWE-bench Verified while Nemotron 3 Super 120B A12B scores 51%. For autonomous software engineering and multi-file debugging, Nemotron 3 Super 120B A12B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Nemotron 3 Nano Omni 30B A3B Reasoning and Nemotron 3 Super 120B A12B?+
Nemotron 3 Nano Omni 30B A3B Reasoning features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Nemotron 3 Super 120B A12B provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Nemotron 3 Nano Omni 30B A3B Reasoning and Nemotron 3 Super 120B A12B?+
Nemotron 3 Nano Omni 30B A3B Reasoning generates approximately 105 tokens per second, while Nemotron 3 Super 120B A12B delivers 85 tokens per second. In latency-sensitive production environments, Nemotron 3 Nano Omni 30B A3B Reasoning provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron 3 Nano Omni 30B A3B Reasoning and Nemotron 3 Super 120B A12B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Nemotron 3 Nano Omni 30B A3B Reasoning to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Nemotron 3 Super 120B A12B. 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