Head-to-Head Model Comparison

Llama 3.1 Nemotron Ultra 253B vs DeepSeek-R1-0528

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia y Compara Cualquier Modelo

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Llama 3.1 Nemotron Ultra 253BProfile

DeepSeek-R1-0528Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

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%

DeepSeek

DeepSeek-R1-0528

Razonamiento de Pesos Abiertos

DeepSeek-R1-0528 de DeepSeek 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 48 tokens/s, índice de inteligencia de 52/10

Intelligence Index52 / 100

Input Token:

$0.55 / 1M

Output Token:

$2.19 / 1M

Context Window:

128K tokens

SWE-bench Verified:

51.8%

Diferencias Clave de un Vistazo

DeepSeek-R1-0528 is 1.2x cheaper for API usage (input + output tokens combined).
Llama 3.1 Nemotron Ultra 253B is faster at 60 tok/s vs 48 tok/s.
Llama 3.1 Nemotron Ultra 253B leads on SWE-bench coding (52.8% vs 51.8%).

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

Característica	Llama 3.1 Nemotron Ultra 253B	DeepSeek-R1-0528
Intelligence Index Composite reasoning & knowledge score (0-100)	54	52
Provider	NVIDIA	DeepSeek
Input Price Cost per million input tokens	$0.80 / 1M	$0.55 / 1M
Output Price Cost per million output tokens	$2.40 / 1M	$2.19 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.54 / 1M	$0.43 / 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	8K
Throughput Speed Tokens generated per second	60 tok/s	48 tok/s
SWE-bench Verified Real-world software engineering	52.8%	51.8%
GPQA Diamond Graduate-level reasoning	66%	74.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.20 / 1M	$0.14 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Llama 3.1 Nemotron Ultra 253B

+1.0% SWE-bench lead

Costo

DeepSeek-R1-0528

1.2x cheaper monthly runtime

Velocidad

Llama 3.1 Nemotron Ultra 253B

+25% faster generation

Contexto

Tied

128K tokens window

Puntuaciones de Benchmarks Verificadas

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

Llama 3.1 Nemotron Ultra 253BDeepSeek-R1-0528

SWE-bench Verified (Autonomous Coding)

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

52.8%vs51.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

66%vs74.2%

MMLU-Pro (Multi-task Language Understanding)

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

84%vs85.6%

LMSYS Chatbot Arena Elo

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

1362ptsvs1355pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs12%

Terminal-Bench (Agentic Coding & Terminal Use)

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

46.5%vs34%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs31.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs54%

AIME (Advanced Math Competition)

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

76%vs85%

Long-Context Reasoning

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

82%vs78%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

60 tok/s vs48 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

Llama 3.1 Nemotron Ultra 253B0.60s

DeepSeek-R1-05280.90s

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

Llama 3.1 Nemotron Ultra 253B8.93s

DeepSeek-R1-052811.32s

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)

Llama 3.1 Nemotron Ultra 253B Monthly API Bill

$3.66 / month

$0.8/1M in · $2.4/1M out

DeepSeek-R1-0528 Monthly API Bill

$3.01 / month

$0.55/1M in · $2.19/1M out

DeepSeek-R1-0528 saves approximately $0.65 / month (18%) under this workload.1.2x Savings

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

DeepSeek-R1-0528 · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 51.8% · GPQA Diamond 74.2%)
Rendimiento ágil de 48 tokens/segundo con latencia inicial (TTFT) de solo 900 ms
Pesos abiertos (671B (37B MoE)) 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	Llama 3.1 Nemotron Ultra 253B	DeepSeek-R1-0528
Cost per Task Estimated USD to finish one task	$0.65	$0.62
Output Tokens / Task Tokens generated per task	42K	45K
Reasoning Tokens / Task Thinking tokens burned per task	20K	25K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Llama 3.1 Nemotron Ultra 253B	DeepSeek-R1-0528
Official API Model ID (SDK)	llama-3-1-nemotron-ultra-253b	deepseek-reasoner
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-04	2025-05
Knowledge Cutoff	December 2024	March 2025
Architecture	High-Throughput Aligned Frontier MoE	Open-Weights Reasoning MoE
Parameters	253B	671B (37B 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 ($0.8/1M in) across full 128K window	Flat rate ($0.55/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.2/1M tokens	Automatic prefix caching at $0.14/1M (no extra cache-write fee)
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) · 157 GB VRAM (4-bit Quantized) · 544 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 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

Llama 3.1 Nemotron Ultra 253B vs DeepSeek-R1-0528: Respuestas Rápidas (FAQ)

Which is more intelligent, Llama 3.1 Nemotron Ultra 253B or DeepSeek-R1-0528?

Llama 3.1 Nemotron Ultra 253B is more intelligent. It scores 54 compared with 52 on the composite Intelligence Index.

Which model is faster?

Llama 3.1 Nemotron Ultra 253B is faster, generating 60 tokens per second compared with 48 tokens per second.

Which model is cheaper to run?

DeepSeek-R1-0528 is cheaper at $0.43 per 1M tokens blended, compared with $0.54 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.1 Nemotron Ultra 253B has lower latency, with a time to first token of 0.60s compared with 0.90s.

Veredicto

Tanto 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.) como DeepSeek-R1-0528 (Ideal para razonamiento de cadena de pensamiento con pesos abiertos y alta eficiencia algorítmica, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Llama 3.1 Nemotron Ultra 253B lidera en inteligencia general y programación, Llama 3.1 Nemotron Ultra 253B ofrece mayor velocidad de generación de tokens y DeepSeek-R1-0528 resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Llama 3.1 Nemotron Ultra 253B o DeepSeek-R1-0528?+

En las pruebas de inteligencia compuesta y resolución de código, Llama 3.1 Nemotron Ultra 253B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Llama 3.1 Nemotron Ultra 253B frente a DeepSeek-R1-0528?+

Llama 3.1 Nemotron Ultra 253B cuesta $0.80 / $2.40 por millón de tokens de entrada/salida, mientras que DeepSeek-R1-0528 cuesta $0.55 / $2.19. En costo combinado, DeepSeek-R1-0528 es la opción más económica.

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

Llama 3.1 Nemotron Ultra 253B cuenta con 128K tokens de contexto frente a 128K tokens de DeepSeek-R1-0528. 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)

Llama 3.1 Nemotron Ultra 253B vs DeepSeek-R1-0528

Cambia y Compara Cualquier Modelo

Llama 3.1 Nemotron Ultra 253B

DeepSeek-R1-0528

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Llama 3.1 Nemotron Ultra 253B · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek-R1-0528 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Llama 3.1 Nemotron Ultra 253B vs DeepSeek-R1-0528: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

Related reading