Head-to-Head Model Comparison

Mistral Large 2 vs GPT-4o

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Cambia y Compara Cualquier Modelo

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

Mistral Large 2Profile

GPT-4oProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Mistral Large 2

Modelo Base de Pesos Abiertos

Mistral Large 2 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 128K, velocidad de 75 tokens/s, índice de inteligencia de 46/10

Intelligence Index46 / 100

Input Token:

$2.00 / 1M

Output Token:

$6.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38%

OpenAI

GPT-4o

Modelo de Voz y Audio Nativo

GPT-4o de OpenAI es un modelo de IA diseñado para síntesis de voz natural, transcripción acústica y procesamiento de audio en tiempo real, con ventana de contexto de 128K, velocidad de 105 tokens/s, índice de inteligencia de 41/100 y tarifa

Intelligence Index41 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38.8%

Diferencias Clave de un Vistazo

Mistral Large 2 is 1.5x cheaper for API usage (input + output tokens combined).
GPT-4o is faster at 105 tok/s vs 75 tok/s.
GPT-4o leads on SWE-bench coding (38.8% vs 38.0%).

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

Característica	Mistral Large 2	GPT-4o
Intelligence Index Composite reasoning & knowledge score (0-100)	46	41
Provider	Mistral AI	OpenAI
Input Price Cost per million input tokens	$2.00 / 1M	$2.50 / 1M
Output Price Cost per million output tokens	$6.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.70 / 1M	$2.38 / 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	16K
Throughput Speed Tokens generated per second	75 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	38%	38.8%
GPQA Diamond Graduate-level reasoning	54%	53.6%
Cached Input Price Prompt caching discount per 1M tokens	$1.00 / 1M	$1.25 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

GPT-4o

+0.8% SWE-bench lead

Costo

Mistral Large 2

1.5x cheaper monthly runtime

Velocidad

GPT-4o

+40% faster generation

Contexto

Tied

128K tokens window

Puntuaciones de Benchmarks Verificadas

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

Mistral Large 2GPT-4o

SWE-bench Verified (Autonomous Coding)

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

38%vs38.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

54%vs53.6%

MMLU-Pro (Multi-task Language Understanding)

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

76.5%vs72.6%

LMSYS Chatbot Arena Elo

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

1282ptsvs1286pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs4.1%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs18.5%

SciCode (Scientific Computing)

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

N/Avs27.3%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs43.9%

AIME (Advanced Math Competition)

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

N/Avs29.7%

Long-Context Reasoning

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

N/Avs58.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs105 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

Mistral Large 20.52s

GPT-4o0.41s

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

Mistral Large 27.19s

GPT-4o5.17s

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)

Mistral Large 2 Monthly API Bill

$9.90 / month

$2/1M in · $6/1M out

GPT-4o Monthly API Bill

$14.63 / month

$2.5/1M in · $10/1M out

Mistral Large 2 saves approximately $4.72 / month (32%) under this workload.1.5x Savings

Mistral Large 2 · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 38% · GPQA Diamond 54%)
Rendimiento ágil de 75 tokens/segundo con latencia inicial (TTFT) de solo 520 ms
Pesos abiertos (123B) 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 volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

GPT-4o · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 16K tokens de salida máxima (SWE-bench Verified 38.8% · GPQA Diamond 53.6%)
Rendimiento ágil de 105 tokens/segundo con latencia inicial (TTFT) de solo 410 ms
Economía de API competitiva ($2.5/$10 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Mistral Large 2	GPT-4o
Cost per Task Estimated USD to finish one task	$0.20	$0.68
Output Tokens / Task Tokens generated per task	24K	24K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Mistral Large 2	GPT-4o
Official API Model ID (SDK)	mistral-large-2-latest	gpt-4o
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2024-07	2024-05
Knowledge Cutoff	July 2024	October 2023
Architecture	123B Dense Frontier Transformer	Omni-Multimodal Dense
Parameters	123B	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($2/1M in) across full 128K window	Flat rate ($2.5/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $1/1M tokens	Automatic prefix caching at $1.25/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 76 GB VRAM (4-bit Quantized) · 264 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision, audio
Output Modalities	text	text, audio

Mistral Large 2 vs GPT-4o: Respuestas Rápidas (FAQ)

Which is more intelligent, Mistral Large 2 or GPT-4o?

Mistral Large 2 is more intelligent. It scores 46 compared with 41 on the composite Intelligence Index.

Which model is faster?

GPT-4o is faster, generating 105 tokens per second compared with 75 tokens per second.

Which model is cheaper to run?

Mistral Large 2 is cheaper at $1.70 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.52s.

Veredicto

Tanto Mistral Large 2 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como GPT-4o (Ideal para síntesis de voz natural, transcripción acústica y procesamiento de audio en tiempo real, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Mistral Large 2 lidera en inteligencia general y programación, GPT-4o ofrece mayor velocidad de generación de tokens y Mistral Large 2 resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Mistral Large 2 o GPT-4o?+

En las pruebas de inteligencia compuesta y resolución de código, Mistral Large 2 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Mistral Large 2 frente a GPT-4o?+

Mistral Large 2 cuesta $2.00 / $6.00 por millón de tokens de entrada/salida, mientras que GPT-4o cuesta $2.50 / $10.00. En costo combinado, Mistral Large 2 es la opción más económica.

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

Mistral Large 2 cuenta con 128K tokens de contexto frente a 128K tokens de GPT-4o. En velocidad de inferencia, GPT-4o 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)

Mistral Large 2 vs GPT-4o

Cambia y Compara Cualquier Modelo

Mistral Large 2

GPT-4o

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Mistral Large 2 · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4o · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Mistral Large 2 vs GPT-4o: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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