Head-to-Head Model Comparison

Llama 5 vs DeepSeek V4 Pro

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

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Llama 5Profile

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Llama 5

Açık Ağırlıklı Temel Model

Meta tarafından geliştirilen Llama 5; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 5M bağlam penceresi, 65 token/sn hız, 94/100 zeka indeksi ve ücretsiz açık ağırlık erişimi s

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

Otonom Ajan ve Araç Modeli

DeepSeek tarafından geliştirilen DeepSeek V4 Pro; otonom ajan iş akışları, araç çağırma (tool calling) ve çok adımlı otomasyon için tasarlanmış olup 1M bağlam penceresi, 80 token/sn hız, 53/100 zeka indeksi ve 1M token başına $1.32/$3.96 fi

Intelligence Index53 / 100

Input Token:

$1.32 / 1M

Output Token:

$3.96 / 1M

Context Window:

1M tokens

SWE-bench Verified:

68.5%

Bir Bakışta Temel Farklar

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

Hangi Alanda Kim Kazanıyor?

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

Doğrulanmış Benchmark Puanları

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

Gecikme Süresi ve Yanıt Hızı

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

Gerçek Zamanlı Token Maliyet Simülatörü

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

5M bağlam penceresi ve yanıt başına maksimum 128K çıktı tokenı kapasitesi (SWE-bench Verified 74% · GPQA Diamond 84%)
Sadece 850 ms ilk token gecikmesi (TTFT) ile saniyede 65 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (600B+ total (~40-80B ))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

DeepSeek V4 Pro · Strengths & Key Advantages

1M bağlam penceresi ve yanıt başına maksimum 128K çıktı tokenı kapasitesi (SWE-bench Verified 68.5% · GPQA Diamond 81.2%)
Sadece 580 ms ilk token gecikmesi (TTFT) ile saniyede 80 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (MoE (Colossal))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

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

Teknik Özellikler

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: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Llama 5 (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de DeepSeek V4 Pro (otonom ajan iş akışları, araç çağırma (tool calling) ve çok adımlı otomasyon, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Llama 5 karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, DeepSeek V4 Pro daha yüksek üretim hızı sağlar; Llama 5 ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Llama 5 mi yoksa DeepSeek V4 Pro mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Llama 5 önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Llama 5 ve DeepSeek V4 Pro API fiyatları nasıl karşılaştırılır?+

Llama 5 1M giriş/çıkış tokeni başına $0.00 / $0.00 ücretlendirilirken, DeepSeek V4 Pro $1.32 / $3.96 seviyesindedir. Karma maliyette Llama 5 daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Llama 5 5M token bağlam penceresi sunarken DeepSeek V4 Pro 1M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise DeepSeek V4 Pro öndedir.

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

İstediğiniz Modelleri Seçin ve Karşılaştırın

Llama 5

DeepSeek V4 Pro

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

DeepSeek V4 Pro · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Llama 5 vs DeepSeek V4 Pro: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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