Head-to-Head Model Comparison

Llama 5 vs DeepSeek V4 Pro

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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Llama 5Profile

DeepSeek V4 ProProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 5

Modelo Base de Pesos Abiertos

Llama 5 de Meta es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 5M, velocidad de 65 tokens/s, índice de inteligencia de 94/100 y tarifa de ac

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

DeepSeek

DeepSeek V4 Pro

IA Agéntica y Uso de Herramientas

DeepSeek V4 Pro de DeepSeek es un modelo de IA diseñado para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, con ventana de contexto de 1M, velocidad de 80 tokens/s, índice de inteligencia de 53/100

Intelligence Index53 / 100

Input Token:

$1.32 / 1M

Output Token:

$3.96 / 1M

Context Window:

1M tokens

SWE-bench Verified:

68.5%

Diferencias Clave de un Vistazo

Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
Llama 5 has a larger context window (5M vs 1M tokens).
DeepSeek V4 Pro is faster at 80 tok/s vs 65 tok/s.
Llama 5 leads on SWE-bench coding (74.0% vs 68.5%).

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

Característica	Llama 5	DeepSeek V4 Pro
Intelligence Index Composite reasoning & knowledge score (0-100)	94	53
Provider	Meta	DeepSeek
Input Price Cost per million input tokens	$0.00 / 1M	$1.32 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$3.96 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.76 / 1M
Context Window Maximum input + output tokens	5M tokens~7,500 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	128K	128K
Throughput Speed Tokens generated per second	65 tok/s	80 tok/s
SWE-bench Verified Real-world software engineering	74%	68.5%
GPQA Diamond Graduate-level reasoning	84%	81.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.14 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Llama 5

+5.5% SWE-bench lead

Costo

Llama 5

1.0x cheaper monthly runtime

Velocidad

DeepSeek V4 Pro

+23% faster generation

Contexto

Llama 5

5M tokens window

Puntuaciones de Benchmarks Verificadas

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

Llama 5DeepSeek V4 Pro

SWE-bench Verified (Autonomous Coding)

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

74%vs68.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

84%vs81.2%

MMLU-Pro (Multi-task Language Understanding)

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

87%vs88.9%

LMSYS Chatbot Arena Elo

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

1485ptsvs1438pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs15.6%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs46.5%

SciCode (Scientific Computing)

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

N/Avs43%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vs70.2%

AIME (Advanced Math Competition)

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

88%vs91%

Long-Context Reasoning

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

N/Avs90%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs80 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 50.85s

DeepSeek V4 Pro0.58s

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

Llama 58.54s

DeepSeek V4 Pro6.83s

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 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

DeepSeek V4 Pro Monthly API Bill

$5.75 / month

$1.32/1M in · $3.96/1M out

Llama 5 saves approximately $5.75 / month (100%) under this workload.1.0x Savings

Llama 5 · Strengths & Key Advantages

Ventana de contexto de 5M con hasta 128K tokens de salida máxima (SWE-bench Verified 74% · GPQA Diamond 84%)
Rendimiento ágil de 65 tokens/segundo con latencia inicial (TTFT) de solo 850 ms
Pesos abiertos (600B+ total (~40-80B )) 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 V4 Pro · Strengths & Key Advantages

Ventana de contexto de 1M con hasta 128K tokens de salida máxima (SWE-bench Verified 68.5% · GPQA Diamond 81.2%)
Rendimiento ágil de 80 tokens/segundo con latencia inicial (TTFT) de solo 580 ms
Pesos abiertos (MoE (Colossal)) 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 5	DeepSeek V4 Pro
Cost per Task Estimated USD to finish one task	$0.00	$0.67
Output Tokens / Task Tokens generated per task	40K	52K
Reasoning Tokens / Task Thinking tokens burned per task	20K	25K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Llama 5	DeepSeek V4 Pro
Official API Model ID (SDK)	meta-llama/Llama-5	deepseek-reasoner
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-04	2026-08
Knowledge Cutoff	January 2026	May 2026
Architecture	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)	Next-Gen Sparse MoE with Agent RL Tuning
Parameters	600B+ total (~40-80B active)	MoE (Colossal)
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 ($1.32/1M in) across full 1000K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix caching at $0.14/1M (no extra cache-write fee)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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, vision, video, audio	text
Output Modalities	text	text

Llama 5 vs DeepSeek V4 Pro: Respuestas Rápidas (FAQ)

Which is more intelligent, Llama 5 or DeepSeek V4 Pro?

Llama 5 is more intelligent. It scores 94 compared with 53 on the composite Intelligence Index.

Which model is faster?

DeepSeek V4 Pro is faster, generating 80 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $0.76 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek V4 Pro has lower latency, with a time to first token of 0.58s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.

Veredicto

Tanto Llama 5 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como DeepSeek V4 Pro (Ideal para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Llama 5 lidera en inteligencia general y programación, DeepSeek V4 Pro ofrece mayor velocidad de generación de tokens y Llama 5 resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Llama 5 o DeepSeek V4 Pro?+

En las pruebas de inteligencia compuesta y resolución de código, Llama 5 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Llama 5 frente a DeepSeek V4 Pro?+

Llama 5 cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que DeepSeek V4 Pro cuesta $1.32 / $3.96. En costo combinado, Llama 5 es la opción más económica.

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

Llama 5 cuenta con 5M tokens de contexto frente a 1M tokens de DeepSeek V4 Pro. En velocidad de inferencia, DeepSeek V4 Pro 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 5 vs DeepSeek V4 Pro

Cambia y Compara Cualquier Modelo

Llama 5

DeepSeek V4 Pro

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek V4 Pro · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Llama 5 vs DeepSeek V4 Pro: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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