Nemotron 3 Super 120B A12B vs Llama 3.1 405B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Nemotron 3 Super 120B A12B by NVIDIA and Llama 3.1 405B by Meta 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, Llama 3.1 405B holds a notable pricing advantage for token ingestion. On output generation, Llama 3.1 405B 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 Super 120B A12B 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 Super 120B A12B
Modello a Pesi ApertiNemotron 3 Super 120B A12B di NVIDIA è un modello di intelligenza artificiale con finestra di contesto da 262K token e tariffazione API di $0.3/1M token in input e $0.8/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.30 / 1M
$0.80 / 1M
262.1K tokens
51%
Llama 3.1 405B
Modello a Pesi ApertiLlama 3.1 405B di Meta è un modello di intelligenza artificiale con finestra di contesto da 128K token e tariffazione API di $0.0/1M token in input e $0.0/1M in output, ideale per coding, ragionamento e automazione aziendale.
$0.00 / 1M
$0.00 / 1M
128K tokens
41.5%
Differenze Chiave a Colpo d’Occhio
- Llama 3.1 405B is 1.1x cheaper for API usage (input + output tokens combined).
- Nemotron 3 Super 120B A12B has a larger context window (262K vs 128K tokens).
- Nemotron 3 Super 120B A12B is faster at 85 tok/s vs 50 tok/s.
- Nemotron 3 Super 120B A12B leads on SWE-bench coding (51.0% vs 41.5%).
| Feature | Nemotron 3 Super 120B A12B | Llama 3.1 405B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 53 | 48 |
Provider | NVIDIA | Meta |
Input Price Cost per million input tokens | $0.30 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.80 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.18 / 1M | $0.53 / 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 | 33K | 8K |
Throughput Speed Tokens generated per second | 85 tok/s | 50 tok/s |
SWE-bench Verified Real-world software engineering | 51% | 41.5% |
GPQA Diamond Graduate-level reasoning | 64.5% | 53% |
Cached Input Price Prompt caching discount per 1M tokens | $0.06 / 1M | $0.75 / 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 Super 120B A12B · Strengths & Key Advantages
- Architettura High-Throughput Sparse MoE (120B Total / 12B Active) avanzata di NVIDIA con supporto fino a 262K token di contesto
- Ottimo rapporto qualità-prezzo ($0.3/1M input, $0.8/1M 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
Llama 3.1 405B · Strengths & Key Advantages
- Architettura World’s Largest Open Foundation Transformer avanzata di Meta con supporto fino a 128K token di contesto
- Ottimo rapporto qualità-prezzo ($0.0/1M input, $0.0/1M 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 Super 120B A12B | Llama 3.1 405B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.38 | $0.20 |
Output Tokens / Task Tokens generated per task | 38K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 0K |
Specifiche Tecniche
Architecture, release timeline, capabilities, and modalities.
| Feature | Nemotron 3 Super 120B A12B | Llama 3.1 405B |
|---|---|---|
| Official API Model ID (SDK) | nemotron-3-super-120b-a12b | meta-llama/Llama-3.1-405B |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-03 | 2024-07 |
| Knowledge Cutoff | January 2026 | December 2023 |
| Architecture | High-Throughput Sparse MoE (120B Total / 12B Active) | World’s Largest Open Foundation Transformer |
| Parameters | 120B (12B active MoE) | 405B |
| 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 ($0.3/1M in) across full 262K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.06/1M 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) · 74 GB VRAM (4-bit Quantized) · 258 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 251 GB VRAM (4-bit Quantized) · 871 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 3 Super 120B A12B vs Llama 3.1 405B: Risposte Rapide (FAQ)
Which is more intelligent, Nemotron 3 Super 120B A12B or Llama 3.1 405B?
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 Super 120B A12B is faster, generating 85 tokens per second compared with 50 tokens per second.
Which model is cheaper to run?
Nemotron 3 Super 120B A12B is cheaper at $0.18 per 1M tokens blended, compared with $0.53 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Nemotron 3 Super 120B A12B has lower latency, with a time to first token of 0.46s compared with 0.85s.
Which model has a larger context window?
Nemotron 3 Super 120B A12B has a larger context window, supporting 262.1K tokens compared with 128K tokens.
Verdetto
Verdict & Model Selection Recommendation: Choose Nemotron 3 Super 120B A12B if your priority is Enterprise multi-agent architectures, massive data extraction, and autonomous coding. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3.1 405B if your workflow prioritizes Synthetic data generation, smaller model distillation, national sovereign AI, and air-gapped defense labs. 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 Super 120B A12B compare to Llama 3.1 405B in API token pricing and cost per token?+
Nemotron 3 Super 120B A12B costs $0.30 per 1M input tokens and $0.80 per 1M output tokens, whereas Llama 3.1 405B costs $0.00 input and $0.00 output. Overall, Llama 3.1 405B 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 Super 120B A12B or Llama 3.1 405B?+
Nemotron 3 Super 120B A12B supports a context window of 262K tokens (~197k words), while Llama 3.1 405B supports 128K tokens (~96k 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 Super 120B A12B or Llama 3.1 405B?+
Nemotron 3 Super 120B A12B scores 51% on SWE-bench Verified while Llama 3.1 405B scores 41.5%. 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 Super 120B A12B and Llama 3.1 405B?+
Nemotron 3 Super 120B A12B features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Llama 3.1 405B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Nemotron 3 Super 120B A12B and Llama 3.1 405B?+
Nemotron 3 Super 120B A12B generates approximately 85 tokens per second, while Llama 3.1 405B delivers 50 tokens per second. In latency-sensitive production environments, Nemotron 3 Super 120B A12B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Nemotron 3 Super 120B A12B and Llama 3.1 405B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3.1 405B 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.
Fonti ufficiali: GetInfoUs Independent AI Benchmarks & Official API Specs