Head-to-Head Model Comparison

DeepSeek V3 vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

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DeepSeek V3Profile

Llama 3.3 70BProfile

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DeepSeek

DeepSeek V3

Açık Ağırlıklı Temel Model

DeepSeek tarafından geliştirilen DeepSeek V3; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 128K bağlam penceresi, 60 token/sn hız, 44/100 zeka indeksi ve 1M token başına $0.27

Intelligence Index44 / 100

Input Token:

$0.27 / 1M

Output Token:

$1.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

Llama 3.3 70B

Açık Ağırlıklı Temel Model

Meta tarafından geliştirilen Llama 3.3 70B; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 128K bağlam penceresi, 280 token/sn hız, 38/100 zeka indeksi ve ücretsiz açık ağırlık

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

Bir Bakışta Temel Farklar

Llama 3.3 70B is 10.0x cheaper for API usage (input + output tokens combined).
Llama 3.3 70B is faster at 280 tok/s vs 60 tok/s.
DeepSeek V3 leads on SWE-bench coding (49.2% vs 39.5%).

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

Feature	DeepSeek V3	Llama 3.3 70B
Intelligence Index Composite reasoning & knowledge score (0-100)	44	38
Provider	DeepSeek	Meta
Input Price Cost per million input tokens	$0.27 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$1.10 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.21 / 1M	$0.07 / 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	8K	4K
Throughput Speed Tokens generated per second	60 tok/s	280 tok/s
SWE-bench Verified Real-world software engineering	49.2%	39.5%
GPQA Diamond Graduate-level reasoning	59.1%	48.9%
Cached Input Price Prompt caching discount per 1M tokens	$0.07 / 1M	$0.10 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

DeepSeek V3

+9.7% SWE-bench lead

Cost

Llama 3.3 70B

10.0x cheaper monthly runtime

Speed

Llama 3.3 70B

+367% faster generation

Context

Tied

128K tokens window

Doğrulanmış Benchmark Puanları

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

DeepSeek V3Llama 3.3 70B

SWE-bench Verified (Autonomous Coding)

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

49.2%vs39.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

59.1%vs48.9%

MMLU-Pro (Multi-task Language Understanding)

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

75.9%vs71%

LMSYS Chatbot Arena Elo

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

1300ptsvs1285pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

9.5%vs7.8%

Terminal-Bench (Agentic Coding & Terminal Use)

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

32.5%vs26.1%

SciCode (Scientific Computing)

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

29.5%vs23.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

51.7%vs42.5%

AIME (Advanced Math Competition)

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

68%vs56.2%

Long-Context Reasoning

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

68.3%vs63.9%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

60 tok/s vs280 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

DeepSeek V30.68s

Llama 3.3 70B0.25s

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

DeepSeek V39.01s

Llama 3.3 70B2.04s

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)

DeepSeek V3 Monthly API Bill

$1.50 / month

$0.27/1M in · $1.1/1M out

Llama 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

Llama 3.3 70B saves approximately $1.35 / month (90%) under this workload.10.0x Savings

DeepSeek V3 · Strengths & Key Advantages

128K bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (SWE-bench Verified 49.2% · GPQA Diamond 59.1%)
Sadece 680 ms ilk token gecikmesi (TTFT) ile saniyede 60 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (671B)

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

Llama 3.3 70B · Strengths & Key Advantages

128K bağlam penceresi ve yanıt başına maksimum 4K çıktı tokenı kapasitesi (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Sadece 250 ms ilk token gecikmesi (TTFT) ile saniyede 280 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (70B)

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	DeepSeek V3	Llama 3.3 70B
Cost per Task Estimated USD to finish one task	$0.14	$0.21
Output Tokens / Task Tokens generated per task	45K	45K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	DeepSeek V3	Llama 3.3 70B
Official API Model ID (SDK)	deepseek-chat	meta-llama/Llama-3.3-70B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-12	2024-12
Knowledge Cutoff	July 2024	December 2023
Architecture	671B Mixture-of-Experts (37B active)	Dense Autoregressive Transformer
Parameters	671B	70B
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 ($0.27/1M in) across full 128K window	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix caching at $0.07/1M (no extra cache-write fee)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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) · 416 GB VRAM (4-bit Quantized) · 1443 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	text
Output Modalities	text	text

DeepSeek V3 vs Llama 3.3 70B: Hızlı Yanıtlar (SSS)

Which is more intelligent, DeepSeek V3 or Llama 3.3 70B?

DeepSeek V3 is more intelligent. It scores 44 compared with 38 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $0.21 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.68s.

Sonuç

Hem DeepSeek V3 (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 Llama 3.3 70B (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: DeepSeek V3 karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Llama 3.3 70B daha yüksek üretim hızı sağlar; Llama 3.3 70B ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için DeepSeek V3 mi yoksa Llama 3.3 70B mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde DeepSeek V3 önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

DeepSeek V3 ve Llama 3.3 70B API fiyatları nasıl karşılaştırılır?+

DeepSeek V3 1M giriş/çıkış tokeni başına $0.27 / $1.10 ücretlendirilirken, Llama 3.3 70B $0.00 / $0.00 seviyesindedir. Karma maliyette Llama 3.3 70B daha avantajlıdır.

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

DeepSeek V3 128K token bağlam penceresi sunarken Llama 3.3 70B 128K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Llama 3.3 70B 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)

DeepSeek V3 vs Llama 3.3 70B

İstediğiniz Modelleri Seçin ve Karşılaştırın

DeepSeek V3

Llama 3.3 70B

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

DeepSeek V3 · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

DeepSeek V3 vs Llama 3.3 70B: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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