Head-to-Head Model Comparison

Mistral Saba vs GPT-4.5

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 SabaProfile

GPT-4.5Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Mistral Saba

Modelo Multilingüe Global

Mistral Saba de Mistral AI es un modelo de IA diseñado para traducción multilingüe, comprensión intercultural y flujos de trabajo globales, con ventana de contexto de 33K, velocidad de 80 tokens/s, índice de inteligencia de 40/100 y tarifa

Intelligence Index40 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.60 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

38%

OpenAI

GPT-4.5

Motor de Razonamiento Profundo

GPT-4.5 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 128K, velocidad de 45 tokens/s, índice de inteligencia de 46/100 y tarifa

Intelligence Index46 / 100

Input Token:

$75.00 / 1M

Output Token:

$150.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

42%

Diferencias Clave de un Vistazo

Mistral Saba is 301.2x cheaper for API usage (input + output tokens combined).
GPT-4.5 has a larger context window (128K vs 33K tokens).
Mistral Saba is faster at 80 tok/s vs 45 tok/s.
GPT-4.5 leads on SWE-bench coding (42.0% vs 38.0%).

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

Característica	Mistral Saba	GPT-4.5
Intelligence Index Composite reasoning & knowledge score (0-100)	40	46
Provider	Mistral AI	OpenAI
Input Price Cost per million input tokens	$0.20 / 1M	$75.00 / 1M
Output Price Cost per million output tokens	$0.60 / 1M	$150.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.14 / 1M	$43.13 / 1M
Context Window Maximum input + output tokens	32.8K tokens~49 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	80 tok/s	45 tok/s
SWE-bench Verified Real-world software engineering	38%	42%
GPQA Diamond Graduate-level reasoning	46.5%	66.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.05 / 1M	$18.75 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

GPT-4.5

+4.0% SWE-bench lead

Costo

Mistral Saba

301.2x cheaper monthly runtime

Velocidad

Mistral Saba

+78% faster generation

Contexto

GPT-4.5

128K tokens window

Puntuaciones de Benchmarks Verificadas

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

Mistral SabaGPT-4.5

SWE-bench Verified (Autonomous Coding)

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

38%vs42%

GPQA Diamond (Doctorate-Level Science & Math)

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

46.5%vs66.5%

MMLU-Pro (Multi-task Language Understanding)

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

65.8%vs81.5%

LMSYS Chatbot Arena Elo

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

1260ptsvs1332pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

5.2%vs9.4%

Terminal-Bench (Agentic Coding & Terminal Use)

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

24.5%vs25%

SciCode (Scientific Computing)

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

24%vs34.5%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

41%vs56%

AIME (Advanced Math Competition)

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

48%vs60%

Long-Context Reasoning

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

59%vs68%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

80 tok/s vs45 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 Saba0.46s

GPT-4.51.60s

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

Mistral Saba6.71s

GPT-4.512.71s

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 Saba Monthly API Bill

$0.92 / month

$0.2/1M in · $0.6/1M out

GPT-4.5 Monthly API Bill

$275.63 / month

$75/1M in · $150/1M out

Mistral Saba saves approximately $274.71 / month (100%) under this workload.301.2x Savings

Mistral Saba · Strengths & Key Advantages

Ventana de contexto de 33K con hasta 8K tokens de salida máxima (SWE-bench Verified 38% · GPQA Diamond 46.5%)
Rendimiento ágil de 80 tokens/segundo con latencia inicial (TTFT) de solo 460 ms
Economía de API competitiva ($0.2/$0.6 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

GPT-4.5 · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 16K tokens de salida máxima (SWE-bench Verified 42% · GPQA Diamond 66.5%)
Rendimiento ágil de 45 tokens/segundo con latencia inicial (TTFT) de solo 1600 ms
Economía de API competitiva ($75/$150 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 Saba	GPT-4.5
Cost per Task Estimated USD to finish one task	$0.18	$17.85
Output Tokens / Task Tokens generated per task	24K	55K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Mistral Saba	GPT-4.5
Official API Model ID (SDK)	mistral-saba-latest	gpt-4-5
API Protocol	Mistral AI REST API (OpenAI-Compatible)	OpenAI Responses API & Chat Completions v1
Release Date	2025-02	2025-02
Knowledge Cutoff	January 2025	October 2023
Architecture	Multilingual Dense Transformer	Large dense pre-reasoning transformer
Parameters	24B	Confidential
Enterprise Rate Limits (RPM / TPM)	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.2/1M in) across full 33K window	Flat rate ($75/1M in) across full 128K window
Prompt Cache Policy & TTL	Cached input at $0.05/1M tokens	Automatic prefix caching at $18.75/1M (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	API Prompt Engineering & RAG Distillation	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)
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	Mistral AI Production Safety & Alignment Verified	OpenAI Preparedness Framework Audited & System Card Published
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	text, vision
Output Modalities	text	text

Mistral Saba vs GPT-4.5: Respuestas Rápidas (FAQ)

Which is more intelligent, Mistral Saba or GPT-4.5?

GPT-4.5 is more intelligent. It scores 46 compared with 40 on the composite Intelligence Index.

Which model is faster?

Mistral Saba is faster, generating 80 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

Mistral Saba is cheaper at $0.14 per 1M tokens blended, compared with $43.13 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Mistral Saba has lower latency, with a time to first token of 0.46s compared with 1.60s.

Which model has a larger context window?

GPT-4.5 has a larger context window, supporting 128K tokens compared with 32.8K tokens.

Veredicto

Tanto Mistral Saba (Ideal para traducción multilingüe, comprensión intercultural y flujos de trabajo globales, sistemas RAG empresariales y automatización mediante API.) como GPT-4.5 (Ideal para razonamiento lógico profundo, matemáticas avanzadas y resolución científica paso a paso, sistemas RAG empresariales y automatización mediante API.) destacan en producción: GPT-4.5 lidera en inteligencia general y programación, Mistral Saba ofrece mayor velocidad de generación de tokens y Mistral Saba resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Mistral Saba o GPT-4.5?+

En las pruebas de inteligencia compuesta y resolución de código, GPT-4.5 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Mistral Saba frente a GPT-4.5?+

Mistral Saba cuesta $0.20 / $0.60 por millón de tokens de entrada/salida, mientras que GPT-4.5 cuesta $75.00 / $150.00. En costo combinado, Mistral Saba es la opción más económica.

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

Mistral Saba cuenta con 32.8K tokens de contexto frente a 128K tokens de GPT-4.5. En velocidad de inferencia, Mistral Saba 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 Saba vs GPT-4.5

Cambia y Compara Cualquier Modelo

Mistral Saba

GPT-4.5

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Mistral Saba · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4.5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Mistral Saba vs GPT-4.5: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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