Head-to-Head Model Comparison

Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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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

Razonamiento de Pesos Abiertos

Nemotron Research Reasoning de NVIDIA es un modelo de IA diseñado para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, con ventana de contexto de 128K, velocidad de 45 tokens/s, índice de inteligencia

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

Modelo Base de Pesos Abiertos

Llama 3.1 Nemotron Ultra 253B de NVIDIA es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 60 tokens/s, índice de inteligenci

Intelligence Index54 / 100

Input Token:

$0.80 / 1M

Output Token:

$2.40 / 1M

Context Window:

128K tokens

SWE-bench Verified:

52.8%

Diferencias Clave de un Vistazo

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 →

Característica	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

¿Quién gana en cada área?

At-a-glance decision framework

Código

Llama 3.1 Nemotron Ultra 253B

+4.3% SWE-bench lead

Costo

Nemotron Research Reasoning

1.0x cheaper monthly runtime

Velocidad

Llama 3.1 Nemotron Ultra 253B

+33% faster generation

Contexto

Tied

128K tokens window

Puntuaciones de Benchmarks Verificadas

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

Latencia y Tiempo de Respuesta

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

Simulador Interactivo de Costos de Tokens

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

Ventana de contexto de 128K con hasta 16K tokens de salida máxima (SWE-bench Verified 48.5% · GPQA Diamond 62%)
Rendimiento ágil de 45 tokens/segundo con latencia inicial (TTFT) de solo 520 ms
Pesos abiertos (70B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 16K tokens de salida máxima (SWE-bench Verified 52.8% · GPQA Diamond 66%)
Rendimiento ágil de 60 tokens/segundo con latencia inicial (TTFT) de solo 600 ms
Pesos abiertos (253B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

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

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	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: Respuestas Rápidas (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.

Veredicto

Tanto Nemotron Research Reasoning (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) como Llama 3.1 Nemotron Ultra 253B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Nemotron Research Reasoning lidera en inteligencia general y programación, Llama 3.1 Nemotron Ultra 253B ofrece mayor velocidad de generación de tokens y Nemotron Research Reasoning resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Nemotron Research Reasoning o Llama 3.1 Nemotron Ultra 253B?+

En las pruebas de inteligencia compuesta y resolución de código, Nemotron Research Reasoning obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Nemotron Research Reasoning frente a Llama 3.1 Nemotron Ultra 253B?+

Nemotron Research Reasoning cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Llama 3.1 Nemotron Ultra 253B cuesta $0.80 / $2.40. En costo combinado, Nemotron Research Reasoning es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Nemotron Research Reasoning cuenta con 128K tokens de contexto frente a 128K tokens de Llama 3.1 Nemotron Ultra 253B. En velocidad de inferencia, Llama 3.1 Nemotron Ultra 253B ofrece un rendimiento superior.

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 y Compara Cualquier Modelo

Nemotron Research Reasoning

Llama 3.1 Nemotron Ultra 253B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

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

Especificaciones Técnicas

Nemotron Research Reasoning vs Llama 3.1 Nemotron Ultra 253B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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