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Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1: Benchmarks, Pricing & Speed Comparison

Llama 3.1 Nemotron Ultra 253B di NVIDIA e Llama 3.3 Nemotron Super 49B v1 di NVIDIA sono due modelli di IA di riferimento. Llama 3.1 Nemotron Ultra 253B offre una finestra di contesto di 128K token a $0.80/$2.40 per milione di token, mentre Llama 3.3 Nemotron Super 49B v1 mette a disposizione 131.1K token di contesto a $0.25/$0.50 per milione di token. Di seguito analizziamo benchmark, latenza e costi.

Confronto Diretto (VS)

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NVIDIA

Llama 3.1 Nemotron Ultra 253B

Modello Base a Pesi Aperti

Llama 3.1 Nemotron Ultra 253B di NVIDIA è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 128K, velocità di 60 token/s, indice di intellige

Intelligence Index54 / 100
Input Token:

$0.80 / 1M

Output Token:

$2.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

52.8%

NVIDIA

Llama 3.3 Nemotron Super 49B v1

Modello Base a Pesi Aperti

Llama 3.3 Nemotron Super 49B v1 di NVIDIA è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 131K, velocità di 95 token/s, indice di intelli

Intelligence Index46 / 100
Input Token:

$0.25 / 1M

Output Token:

$0.50 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

40%

Differenze Chiave a Colpo d’Occhio

  • Llama 3.3 Nemotron Super 49B v1 is 3.9x cheaper for API usage (input + output tokens combined).
  • Llama 3.3 Nemotron Super 49B v1 has a larger context window (131K vs 128K tokens).
  • Llama 3.3 Nemotron Super 49B v1 is faster at 95 tok/s vs 60 tok/s.
  • Llama 3.1 Nemotron Ultra 253B leads on SWE-bench coding (52.8% vs 40.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureLlama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1
Intelligence Index
Composite reasoning & knowledge score (0-100)
5446
Provider
NVIDIANVIDIA
Input Price
Cost per million input tokens
$0.80 / 1M$0.25 / 1M
Output Price
Cost per million output tokens
$2.40 / 1M$0.50 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.54 / 1M$0.16 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text131.1K tokens~197 A4 pages of text
Max Output Tokens
Per single request
16K8K
Throughput Speed
Tokens generated per second
60 tok/s95 tok/s
SWE-bench Verified
Real-world software engineering
52.8%40%
GPQA Diamond
Graduate-level reasoning
66%56%
Cached Input Price
Prompt caching discount per 1M tokens
$0.20 / 1M$0.08 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework
Coding
Llama 3.1 Nemotron Ultra 253B
+12.8% SWE-bench lead
Cost
Llama 3.3 Nemotron Super 49B v1
3.9x cheaper monthly runtime
Speed
Llama 3.3 Nemotron Super 49B v1
+58% faster generation
Context
Llama 3.3 Nemotron Super 49B v1
131.1K tokens window

Punteggi Benchmark Verificati

Official evaluation metrics verified by independent third-party research labs.

Llama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

52.8%vs40%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

66%vs56%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

84%vs78%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1362ptsvs1305pts
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

46.5%vs36%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

76%vs66%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

82%vs74%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

60 tok/s vs95 tok/s

Latenza e Tempo di Risposta

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
Llama 3.1 Nemotron Ultra 253B0.60s
Llama 3.3 Nemotron Super 49B v10.40s
Est. End-to-End Response (500 tokens)TTFT + generation time
Llama 3.1 Nemotron Ultra 253B8.93s
Llama 3.3 Nemotron Super 49B v15.66s

Simulatore Interattivo dei Costi Token

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Llama 3.1 Nemotron Ultra 253B Monthly API Bill
$3.66 / month
$0.8/1M in · $2.4/1M out
Llama 3.3 Nemotron Super 49B v1 Monthly API Bill
$0.95 / month
$0.25/1M in · $0.5/1M out
Llama 3.3 Nemotron Super 49B v1 saves approximately $2.71 / month (74%) under this workload.3.9x Savings

Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages

  • Finestra di contesto da 128K con capacità massima di output fino a 16K token (SWE-bench Verified 52.8% · GPQA Diamond 66%)
  • Throughput elevato di 60 token/secondo con latenza al primo token (TTFT) di soli 600 ms
  • Pesi aperti (253B) 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

Llama 3.3 Nemotron Super 49B v1 · Strengths & Key Advantages

  • Finestra di contesto da 131K con capacità massima di output fino a 8K token (SWE-bench Verified 40% · GPQA Diamond 56%)
  • Throughput elevato di 95 token/secondo con latenza al primo token (TTFT) di soli 400 ms
  • Pesi aperti (49B) 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.

MetricLlama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1
Cost per Task
Estimated USD to finish one task
$0.65$0.22
Output Tokens / Task
Tokens generated per task
42K30K
Reasoning Tokens / Task
Thinking tokens burned per task
20K12K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

FeatureLlama 3.1 Nemotron Ultra 253BLlama 3.3 Nemotron Super 49B v1
Official API Model ID (SDK)llama-3-1-nemotron-ultra-253bllama-3-3-nemotron-super-49b-v1
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-042025-04
Knowledge CutoffDecember 2024January 2025
ArchitectureHigh-Throughput Aligned Frontier MoECompact High-Density Transformer
Parameters253B49B
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.8/1M in) across full 128K windowFlat rate ($0.25/1M in) across full 131K window
Prompt Cache Policy & TTLCached input at $0.2/1M tokensCached input at $0.08/1M tokens
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 157 GB VRAM (4-bit Quantized) · 544 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 30 GB VRAM (4-bit Quantized) · 105 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

Llama 3.1 Nemotron Ultra 253B vs Llama 3.3 Nemotron Super 49B v1: Risposte Rapide (FAQ)

Which is more intelligent, Llama 3.1 Nemotron Ultra 253B or Llama 3.3 Nemotron Super 49B v1?

Llama 3.1 Nemotron Ultra 253B is more intelligent. It scores 54 compared with 46 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 Nemotron Super 49B v1 is faster, generating 95 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

Llama 3.3 Nemotron Super 49B v1 is cheaper at $0.16 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 Nemotron Super 49B v1 has lower latency, with a time to first token of 0.40s compared with 0.60s.

Which model has a larger context window?

Llama 3.3 Nemotron Super 49B v1 has a larger context window, supporting 131.1K tokens compared with 128K tokens.

Verdetto

Sia Llama 3.1 Nemotron Ultra 253B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Llama 3.3 Nemotron Super 49B v1 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Llama 3.1 Nemotron Ultra 253B guida nei benchmark di ragionamento e programmazione, Llama 3.3 Nemotron Super 49B v1 garantisce una generazione più rapida e Llama 3.3 Nemotron Super 49B v1 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Llama 3.1 Nemotron Ultra 253B o Llama 3.3 Nemotron Super 49B v1?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Llama 3.1 Nemotron Ultra 253B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Llama 3.1 Nemotron Ultra 253B e Llama 3.3 Nemotron Super 49B v1?+

Llama 3.1 Nemotron Ultra 253B costa $0.80 / $2.40 per milione di token di input/output, mentre Llama 3.3 Nemotron Super 49B v1 costa $0.25 / $0.50. Nel costo combinato, Llama 3.3 Nemotron Super 49B v1 risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Llama 3.1 Nemotron Ultra 253B supporta 128K token di contesto contro 131.1K token di Llama 3.3 Nemotron Super 49B v1, mentre Llama 3.3 Nemotron Super 49B v1 offre una velocità di generazione superiore.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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).