Head-to-Head Model Comparison

Mixtral 8x22B vs Llama 3 (70B)

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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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

Modelo Base de Pesos Abiertos

Mixtral 8x22B de Mistral AI es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 66K, velocidad de 55 tokens/s, índice de inteligencia de 44/100 y

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)

Modelo Base de Pesos Abiertos

Llama 3 (70B) de Meta es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 8K, velocidad de 110 tokens/s, índice de inteligencia de 42/100 y tarif

Intelligence Index42 / 100

Input Token:

$0.35 / 1M

Output Token:

$0.80 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

24%

Diferencias Clave de un Vistazo

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 →

Característica	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

¿Quién gana en cada área?

At-a-glance decision framework

Código

Mixtral 8x22B

+12.0% SWE-bench lead

Costo

Llama 3 (70B)

2.7x cheaper monthly runtime

Velocidad

Llama 3 (70B)

+100% faster generation

Contexto

Mixtral 8x22B

65.5K tokens window

Puntuaciones de Benchmarks Verificadas

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

Latencia y Tiempo de Respuesta

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

Simulador Interactivo de Costos 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)

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

Ventana de contexto de 66K con hasta 8K tokens de salida máxima (SWE-bench Verified 36% · GPQA Diamond 51.2%)
Rendimiento ágil de 55 tokens/segundo con latencia inicial (TTFT) de solo 700 ms
Pesos abiertos (141B total (39B MoE)) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Llama 3 (70B) · Strengths & Key Advantages

Ventana de contexto de 8K con hasta 4K tokens de salida máxima (SWE-bench Verified 24% · GPQA Diamond 46%)
Rendimiento ágil de 110 tokens/segundo con latencia inicial (TTFT) de solo 420 ms
Pesos abiertos (70B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

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

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	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): Respuestas Rápidas (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.

Veredicto

Tanto Mixtral 8x22B (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Llama 3 (70B) (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Mixtral 8x22B lidera en inteligencia general y programación, Llama 3 (70B) ofrece mayor velocidad de generación de tokens y Llama 3 (70B) resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Mixtral 8x22B o Llama 3 (70B)?+

En las pruebas de inteligencia compuesta y resolución de código, Mixtral 8x22B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Mixtral 8x22B frente a Llama 3 (70B)?+

Mixtral 8x22B cuesta $1.20 / $1.20 por millón de tokens de entrada/salida, mientras que Llama 3 (70B) cuesta $0.35 / $0.80. En costo combinado, Llama 3 (70B) es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Mixtral 8x22B cuenta con 65.5K tokens de contexto frente a 8.2K tokens de Llama 3 (70B). En velocidad de inferencia, Llama 3 (70B) ofrece un rendimiento superior.

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 y Compara Cualquier Modelo

Mixtral 8x22B

Llama 3 (70B)

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Mixtral 8x22B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3 (70B) · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Mixtral 8x22B vs Llama 3 (70B): Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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