Head-to-Head Model Comparison

Mixtral 8x22B vs Llama 3 (70B)

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Mixtral 8x22BProfile

Llama 3 (70B)Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Mixtral 8x22B

Modello Base a Pesi Aperti

Mixtral 8x22B di Mistral AI è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 66K, velocità di 55 token/s, indice di intelligenza di 44/100

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)

Modello Base a Pesi Aperti

Llama 3 (70B) di Meta è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 8K, velocità di 110 token/s, indice di intelligenza di 42/100 e tar

Intelligence Index42 / 100

Input Token:

$0.35 / 1M

Output Token:

$0.80 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

24%

Differenze Chiave a Colpo d’Occhio

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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 66K con capacità massima di output fino a 8K token (SWE-bench Verified 36% · GPQA Diamond 51.2%)
Throughput elevato di 55 token/secondo con latenza al primo token (TTFT) di soli 700 ms
Pesi aperti (141B total (39B MoE)) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Llama 3 (70B) · Strengths & Key Advantages

Finestra di contesto da 8K con capacità massima di output fino a 4K token (SWE-bench Verified 24% · GPQA Diamond 46%)
Throughput elevato di 110 token/secondo con latenza al primo token (TTFT) di soli 420 ms
Pesi aperti (70B) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

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

Specifiche Tecniche

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): Risposte Rapide (FAQ)

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.

Verdetto

Sia Mixtral 8x22B (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Llama 3 (70B) (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Mixtral 8x22B guida nei benchmark di ragionamento e programmazione, Llama 3 (70B) garantisce una generazione più rapida e Llama 3 (70B) offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Mixtral 8x22B o Llama 3 (70B)?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Mixtral 8x22B. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Mixtral 8x22B e Llama 3 (70B)?+

Mixtral 8x22B costa $1.20 / $1.20 per milione di token di input/output, mentre Llama 3 (70B) costa $0.35 / $0.80. Nel costo combinato, Llama 3 (70B) risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Mixtral 8x22B supporta 65.5K token di contesto contro 8.2K token di Llama 3 (70B), mentre Llama 3 (70B) offre una velocità di generazione superiore.

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)

Cambia e Confronta Qualsiasi Modello

Mixtral 8x22B

Llama 3 (70B)

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Mixtral 8x22B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3 (70B) · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Mixtral 8x22B vs Llama 3 (70B): Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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