Head-to-Head Model Comparison

Llama 5 vs DeepSeek V4 Pro

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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Llama 5Profile

DeepSeek V4 ProProfile

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Llama 5

Modelo Base de Pesos Abertos

O Llama 5 da Meta é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 5M, velocidade de 65 tokens/s, índice de inteligência de 94/100 e tarifa de ace

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 Agêntica e Uso de Ferramentas

O DeepSeek V4 Pro da DeepSeek é um modelo de IA projetado para fluxos de trabalho agênticos autônomos, chamada de ferramentas e automação, oferecendo janela de contexto de 1M, velocidade de 80 tokens/s, índice de inteligência de 53/100 e ta

Intelligence Index53 / 100

Input Token:

$1.32 / 1M

Output Token:

$3.96 / 1M

Context Window:

1M tokens

SWE-bench Verified:

68.5%

Principais Diferenças em Resumo

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 →

Feature	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

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Llama 5

+5.5% SWE-bench lead

Cost

Llama 5

1.0x cheaper monthly runtime

Speed

DeepSeek V4 Pro

+23% faster generation

Context

Llama 5

5M tokens window

Pontuações 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

Latência e Tempo de Resposta

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 Interativo de Custo 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

Janela de contexto de 5M com limite máximo de saída de 128K tokens (SWE-bench Verified 74% · GPQA Diamond 84%)
Taxa de transferência rápida de 65 tokens/segundo com latência inicial (TTFT) de 850 ms
Pesos abertos (600B+ total (~40-80B )) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

DeepSeek V4 Pro · Strengths & Key Advantages

Janela de contexto de 1M com limite máximo de saída de 128K tokens (SWE-bench Verified 68.5% · GPQA Diamond 81.2%)
Taxa de transferência rápida de 80 tokens/segundo com latência inicial (TTFT) de 580 ms
Pesos abertos (MoE (Colossal)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

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

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	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: Respostas 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.

Veredito

Tanto Llama 5 (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto DeepSeek V4 Pro (Indicado para fluxos de trabalho agênticos autônomos, chamada de ferramentas e automação, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Llama 5 lidera em inteligência geral e programação, DeepSeek V4 Pro oferece geração mais rápida e Llama 5 apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Llama 5 ou DeepSeek V4 Pro?+

Nas avaliações de inteligência e código, Llama 5 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Llama 5 ou DeepSeek V4 Pro?+

O Llama 5 custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o DeepSeek V4 Pro custa $1.32 / $3.96. No custo combinado, Llama 5 é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Llama 5 suporta 5M tokens de contexto contra 1M tokens do DeepSeek V4 Pro, enquanto DeepSeek V4 Pro entrega maior velocidade de geração.

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

Troque e Compare Qualquer Modelo

Llama 5

DeepSeek V4 Pro

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek V4 Pro · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

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

Veredito

Perguntas Frequentes (FAQ)

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