Head-to-Head Model Comparison

o3 vs Magistral Medium

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.

o3Profile

Magistral MediumProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

OpenAI

o3

Motor de Razonamiento Profundo

o3 de OpenAI es un modelo de IA diseñado para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, con ventana de contexto de 200K, velocidad de 75 tokens/s, índice de inteligencia de 56/100 y tarifa de $

Intelligence Index56 / 100

Input Token:

$2.00 / 1M

Output Token:

$8.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

69.1%

Mistral AI

Magistral Medium

Motor de Razonamiento Profundo

Magistral Medium de Mistral AI es un modelo de IA diseñado para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, con ventana de contexto de 128K, velocidad de 50 tokens/s, índice de inteligencia de 51

Intelligence Index51 / 100

Input Token:

$1.00 / 1M

Output Token:

$3.50 / 1M

Context Window:

128K tokens

SWE-bench Verified:

45.2%

Diferencias Clave de un Vistazo

Magistral Medium is 2.2x cheaper for API usage (input + output tokens combined).
o3 has a larger context window (200K vs 128K tokens).
o3 is faster at 75 tok/s vs 50 tok/s.
o3 leads on SWE-bench coding (69.1% vs 45.2%).

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

Característica	o3	Magistral Medium
Intelligence Index Composite reasoning & knowledge score (0-100)	56	51
Provider	OpenAI	Mistral AI
Input Price Cost per million input tokens	$2.00 / 1M	$1.00 / 1M
Output Price Cost per million output tokens	$8.00 / 1M	$3.50 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$1.55 / 1M	$0.72 / 1M
Context Window Maximum input + output tokens	200K tokens~300 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	100K	33K
Throughput Speed Tokens generated per second	75 tok/s	50 tok/s
SWE-bench Verified Real-world software engineering	69.1%	45.2%
GPQA Diamond Graduate-level reasoning	76.5%	67.8%
Cached Input Price Prompt caching discount per 1M tokens	$0.50 / 1M	$0.25 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

+23.9% SWE-bench lead

Costo

Magistral Medium

2.2x cheaper monthly runtime

Velocidad

+50% faster generation

Contexto

200K tokens window

Puntuaciones de Benchmarks Verificadas

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

o3Magistral Medium

SWE-bench Verified (Autonomous Coding)

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

69.1%vs45.2%

GPQA Diamond (Doctorate-Level Science & Math)

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

76.5%vs67.8%

MMLU-Pro (Multi-task Language Understanding)

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

87%vs82.5%

LMSYS Chatbot Arena Elo

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

1345ptsvs1335pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

12.9%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

42%vs40%

SciCode (Scientific Computing)

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

42.5%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

70.5%vsN/A

AIME (Advanced Math Competition)

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

91.6%vs76.5%

Long-Context Reasoning

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

78.5%vs80%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs50 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

o32.80s

Magistral Medium0.85s

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

o39.47s

Magistral Medium10.85s

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)

o3 Monthly API Bill

$10.95 / month

$2/1M in · $8/1M out

Magistral Medium Monthly API Bill

$5.03 / month

$1/1M in · $3.5/1M out

Magistral Medium saves approximately $5.92 / month (54%) under this workload.2.2x Savings

o3 · Strengths & Key Advantages

Ventana de contexto de 200K con hasta 100K tokens de salida máxima (SWE-bench Verified 69.1% · GPQA Diamond 76.5%)
Rendimiento ágil de 75 tokens/segundo con latencia inicial (TTFT) de solo 2800 ms
Economía de API competitiva ($2/$8 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

Magistral Medium · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 33K tokens de salida máxima (SWE-bench Verified 45.2% · GPQA Diamond 67.8%)
Rendimiento ágil de 50 tokens/segundo con latencia inicial (TTFT) de solo 850 ms
Economía de API competitiva ($1/$3.5 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 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	o3	Magistral Medium
Cost per Task Estimated USD to finish one task	$1.35	$0.65
Output Tokens / Task Tokens generated per task	48K	40K
Reasoning Tokens / Task Thinking tokens burned per task	26K	22K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	o3	Magistral Medium
Official API Model ID (SDK)	o3	magistral-medium-latest
API Protocol	OpenAI Responses API & Chat Completions v1	Mistral AI REST API (OpenAI-Compatible)
Release Date	2025-04	2025-03
Knowledge Cutoff	June 2024	January 2025
Architecture	Reasoning transformer with adjustable thinking effort	Reinforcement-Learned Reasoning Transformer
Parameters	Confidential	Confidential (~70B)
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($2/1M in) across full 200K window	Flat rate ($1/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix caching at $0.5/1M (no extra cache-write fee)	Cached input at $0.25/1M tokens
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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	Native Web Search tool + Hosted Code Interpreter & File Search RAG	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)	API Prompt Engineering & RAG Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	Text BPE (~4 chars/token)
Quantization & Min GPU VRAM	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	OpenAI Preparedness Framework Audited & System Card Published	Mistral AI Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision	text
Output Modalities	text	text

o3 vs Magistral Medium: Respuestas Rápidas (FAQ)

Which is more intelligent, o3 or Magistral Medium?

o3 is more intelligent. It scores 56 compared with 51 on the composite Intelligence Index.

Which model is faster?

o3 is faster, generating 75 tokens per second compared with 50 tokens per second.

Which model is cheaper to run?

Magistral Medium is cheaper at $0.72 per 1M tokens blended, compared with $1.55 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Magistral Medium has lower latency, with a time to first token of 0.85s compared with 2.80s.

Which model has a larger context window?

o3 has a larger context window, supporting 200K tokens compared with 128K tokens.

Veredicto

Tanto o3 (Ideal para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, sistemas RAG empresariales y automatización mediante API.) como Magistral Medium (Ideal para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, sistemas RAG empresariales y automatización mediante API.) destacan en producción: o3 lidera en inteligencia general y programación, o3 ofrece mayor velocidad de generación de tokens y Magistral Medium resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, o3 o Magistral Medium?+

En las pruebas de inteligencia compuesta y resolución de código, o3 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de o3 frente a Magistral Medium?+

o3 cuesta $2.00 / $8.00 por millón de tokens de entrada/salida, mientras que Magistral Medium cuesta $1.00 / $3.50. En costo combinado, Magistral Medium es la opción más económica.

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

o3 cuenta con 200K tokens de contexto frente a 128K tokens de Magistral Medium. En velocidad de inferencia, o3 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)

o3 vs Magistral Medium

Cambia y Compara Cualquier Modelo

o3

Magistral Medium

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

o3 · Strengths & Key Advantages

Considerations & Trade-offs

Magistral Medium · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

o3 vs Magistral Medium: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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