Head-to-Head Model Comparison

Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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Nemotron Research ReasoningProfile

Llama 3.1 Nemotron Ultra 253BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

NVIDIA

Nemotron Research Reasoning

Ragionamento a Pesi Aperti

Nemotron 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

Intelligence Index68 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

48.5%

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%

Differenze Chiave a Colpo d’Occhio

Nemotron Research Reasoning is 1.0x cheaper for API usage (input + output tokens combined).
Llama 3.1 Nemotron Ultra 253B is faster at 60 tok/s vs 45 tok/s.
Llama 3.1 Nemotron Ultra 253B leads on SWE-bench coding (52.8% vs 48.5%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Nemotron Research Reasoning	Llama 3.1 Nemotron Ultra 253B
Intelligence Index Composite reasoning & knowledge score (0-100)	68	54
Provider	NVIDIA	NVIDIA
Input Price Cost per million input tokens	$0.00 / 1M	$0.80 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$2.40 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.54 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	16K	16K
Throughput Speed Tokens generated per second	45 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	48.5%	52.8%
GPQA Diamond Graduate-level reasoning	62%	66%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.20 / 1M

Chi vince in ogni categoria?

At-a-glance decision framework

Coding

Llama 3.1 Nemotron Ultra 253B

+4.3% SWE-bench lead

Cost

Nemotron Research Reasoning

1.0x cheaper monthly runtime

Speed

Llama 3.1 Nemotron Ultra 253B

+33% faster generation

Context

Tied

128K tokens window

Punteggi Benchmark Verificati

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

Nemotron Research ReasoningLlama 3.1 Nemotron Ultra 253B

SWE-bench Verified (Autonomous Coding)

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

48.5%vs52.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

62%vs66%

MMLU-Pro (Multi-task Language Understanding)

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

82%vs84%

LMSYS Chatbot Arena Elo

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

1340ptsvs1362pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

42%vs46.5%

AIME (Advanced Math Competition)

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

68%vs76%

Long-Context Reasoning

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

78%vs82%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs60 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

Nemotron Research Reasoning0.52s

Llama 3.1 Nemotron Ultra 253B0.60s

Est. End-to-End Response (500 tokens)TTFT + generation time

Nemotron Research Reasoning11.63s

Llama 3.1 Nemotron Ultra 253B8.93s

Simulatore Interattivo dei Costi Token

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Nemotron Research Reasoning Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 3.1 Nemotron Ultra 253B Monthly API Bill

$3.66 / month

$0.8/1M in · $2.4/1M out

Nemotron Research Reasoning saves approximately $3.66 / month (100%) under this workload.1.0x Savings

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

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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Nemotron Research Reasoning	Llama 3.1 Nemotron Ultra 253B
Cost per Task Estimated USD to finish one task	$0.00	$0.65
Output Tokens / Task Tokens generated per task	18K	42K
Reasoning Tokens / Task Thinking tokens burned per task	12K	20K

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Feature	Nemotron Research Reasoning	Llama 3.1 Nemotron Ultra 253B
Official API Model ID (SDK)	nemotron-research-reasoning	llama-3-1-nemotron-ultra-253b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-08	2025-04
Knowledge Cutoff	July 2025	December 2024
Architecture	Research-Grade Dense Transformer	High-Throughput Aligned Frontier MoE
Parameters	70B	253B
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)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($0.8/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.2/1M 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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 157 GB VRAM (4-bit Quantized) · 544 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 Research Reasoning vs Llama 3.1 Nemotron Ultra 253B: Risposte Rapide (FAQ)

Which is more intelligent, Nemotron Research Reasoning or Llama 3.1 Nemotron Ultra 253B?

Nemotron Research Reasoning is more intelligent. It scores 68 compared with 54 on the composite Intelligence Index.

Which model is faster?

Llama 3.1 Nemotron Ultra 253B is faster, generating 60 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 $0.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Nemotron Research Reasoning has lower latency, with a time to first token of 0.52s compared with 0.60s.

Verdetto

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.) 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.) eccellono in produzione: Nemotron Research Reasoning guida nei benchmark di ragionamento e programmazione, Llama 3.1 Nemotron Ultra 253B 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 Research Reasoning o Llama 3.1 Nemotron Ultra 253B?+

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 Research Reasoning e Llama 3.1 Nemotron Ultra 253B?+

Nemotron Research Reasoning costa $0.00 / $0.00 per milione di token di input/output, mentre Llama 3.1 Nemotron Ultra 253B costa $0.80 / $2.40. Nel costo combinato, Nemotron Research Reasoning risulta più economico.

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

Nemotron Research Reasoning supporta 128K token di contesto contro 128K token di Llama 3.1 Nemotron Ultra 253B, mentre Llama 3.1 Nemotron Ultra 253B 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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B

Cambia e Confronta Qualsiasi Modello

Nemotron Research Reasoning

Llama 3.1 Nemotron Ultra 253B

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Nemotron Research Reasoning · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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