Head-to-Head Model Comparison

Mixtral 8x22B vs Llama 3 (70B)

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

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Mixtral 8x22BProfile

Llama 3 (70B)Profile

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5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Mixtral 8x22B

Açık Ağırlıklı Temel Model

Mistral AI tarafından geliştirilen Mixtral 8x22B; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 66K bağlam penceresi, 55 token/sn hız, 44/100 zeka indeksi ve 1M token başına $1

Intelligence Index44 / 100

Input Token:

$1.20 / 1M

Output Token:

$1.20 / 1M

Context Window:

65.5K tokens

SWE-bench Verified:

36%

Llama 3 (70B)

Açık Ağırlıklı Temel Model

Meta tarafından geliştirilen Llama 3 (70B); özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 8K bağlam penceresi, 110 token/sn hız, 42/100 zeka indeksi ve 1M token başına $0.35/$0

Intelligence Index42 / 100

Input Token:

$0.35 / 1M

Output Token:

$0.80 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

24%

Bir Bakışta Temel Farklar

Llama 3 (70B) is 2.7x cheaper for API usage (input + output tokens combined).
Mixtral 8x22B has a larger context window (66K vs 8K tokens).
Llama 3 (70B) is faster at 110 tok/s vs 55 tok/s.
Mixtral 8x22B leads on SWE-bench coding (36.0% vs 24.0%).

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

Feature	Mixtral 8x22B	Llama 3 (70B)
Intelligence Index Composite reasoning & knowledge score (0-100)	44	42
Provider	Mistral AI	Meta
Input Price Cost per million input tokens	$1.20 / 1M	$0.35 / 1M
Output Price Cost per million output tokens	$1.20 / 1M	$0.80 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.78 / 1M	$0.21 / 1M
Context Window Maximum input + output tokens	65.5K tokens~98 A4 pages of text	8.2K tokens~12 A4 pages of text
Max Output Tokens Per single request	8K	4K
Throughput Speed Tokens generated per second	55 tok/s	110 tok/s
SWE-bench Verified Real-world software engineering	36%	24%
GPQA Diamond Graduate-level reasoning	51.2%	46%
Cached Input Price Prompt caching discount per 1M tokens	$0.60 / 1M	$0.09 / 1M

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

Mixtral 8x22B

+12.0% SWE-bench lead

Cost

Llama 3 (70B)

2.7x cheaper monthly runtime

Speed

Llama 3 (70B)

+100% faster generation

Context

Mixtral 8x22B

65.5K tokens window

Doğrulanmış Benchmark Puanları

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

Mixtral 8x22BLlama 3 (70B)

SWE-bench Verified (Autonomous Coding)

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

36%vs24%

GPQA Diamond (Doctorate-Level Science & Math)

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

51.2%vs46%

MMLU-Pro (Multi-task Language Understanding)

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

74%vs72%

LMSYS Chatbot Arena Elo

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

1260ptsvs1245pts

Terminal-Bench (Agentic Coding & Terminal Use)

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

31%vsN/A

AIME (Advanced Math Competition)

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

62%vsN/A

Long-Context Reasoning

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

68%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

55 tok/s vs110 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

Mixtral 8x22B0.70s

Llama 3 (70B)0.42s

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

Mixtral 8x22B9.79s

Llama 3 (70B)4.97s

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)

Mixtral 8x22B Monthly API Bill

$3.78 / month

$1.2/1M in · $1.2/1M out

Llama 3 (70B) Monthly API Bill

$1.38 / month

$0.35/1M in · $0.8/1M out

Llama 3 (70B) saves approximately $2.40 / month (64%) under this workload.2.7x Savings

Mixtral 8x22B · Strengths & Key Advantages

66K bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (SWE-bench Verified 36% · GPQA Diamond 51.2%)
Sadece 700 ms ilk token gecikmesi (TTFT) ile saniyede 55 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (141B total (39B MoE))

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 (70B) · Strengths & Key Advantages

8K bağlam penceresi ve yanıt başına maksimum 4K çıktı tokenı kapasitesi (SWE-bench Verified 24% · GPQA Diamond 46%)
Sadece 420 ms ilk token gecikmesi (TTFT) ile saniyede 110 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	Mixtral 8x22B	Llama 3 (70B)
Cost per Task Estimated USD to finish one task	$0.45	$0.20
Output Tokens / Task Tokens generated per task	35K	24K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

Feature	Mixtral 8x22B	Llama 3 (70B)
Official API Model ID (SDK)	mixtral-8x22b-latest	meta-llama/Llama-3-(70B)
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-04	2024-04
Knowledge Cutoff	March 2024	March 2024
Architecture	Sparse Mixture-of-Experts (8x22B)	GQA High-Capacity Transformer
Parameters	141B total (39B active MoE)	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 ($1.2/1M in) across full 66K window	Flat rate ($0.35/1M in) across full 8K window
Prompt Cache Policy & TTL	Cached input at $0.6/1M tokens	Cached input at $0.0875/1M tokens
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) · 87 GB VRAM (4-bit Quantized) · 303 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

Mixtral 8x22B vs Llama 3 (70B): Hızlı Yanıtlar (SSS)

Which is more intelligent, Mixtral 8x22B or Llama 3 (70B)?

Mixtral 8x22B is more intelligent. It scores 44 compared with 42 on the composite Intelligence Index.

Which model is faster?

Llama 3 (70B) is faster, generating 110 tokens per second compared with 55 tokens per second.

Which model is cheaper to run?

Llama 3 (70B) is cheaper at $0.21 per 1M tokens blended, compared with $0.78 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3 (70B) has lower latency, with a time to first token of 0.42s compared with 0.70s.

Which model has a larger context window?

Mixtral 8x22B has a larger context window, supporting 65.5K tokens compared with 8.2K tokens.

Sonuç

Hem Mixtral 8x22B (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 (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: Mixtral 8x22B karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Llama 3 (70B) daha yüksek üretim hızı sağlar; Llama 3 (70B) ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Mixtral 8x22B mi yoksa Llama 3 (70B) mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Mixtral 8x22B önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Mixtral 8x22B ve Llama 3 (70B) API fiyatları nasıl karşılaştırılır?+

Mixtral 8x22B 1M giriş/çıkış tokeni başına $1.20 / $1.20 ücretlendirilirken, Llama 3 (70B) $0.35 / $0.80 seviyesindedir. Karma maliyette Llama 3 (70B) daha avantajlıdır.

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

Mixtral 8x22B 65.5K token bağlam penceresi sunarken Llama 3 (70B) 8.2K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Llama 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)

Mixtral 8x22B vs Llama 3 (70B)

İstediğiniz Modelleri Seçin ve Karşılaştırın

Mixtral 8x22B

Llama 3 (70B)

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

Mixtral 8x22B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3 (70B) · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Mixtral 8x22B vs Llama 3 (70B): Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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