Head-to-Head Model Comparison

Ministral 3B vs Gemini 1.5 Flash-8B

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.

Ministral 3BProfile

Gemini 1.5 Flash-8BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Ministral 3B

Modelo Ligero y Económico

Ministral 3B de Mistral AI es un modelo de IA diseñado para máxima eficiencia de costos por token y alta velocidad en producción, con ventana de contexto de 128K, velocidad de 180 tokens/s, índice de inteligencia de 33/100 y tarifa de $0.04

Intelligence Index33 / 100

Input Token:

$0.04 / 1M

Output Token:

$0.04 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22.1%

Google

Gemini 1.5 Flash-8B

IA Multimodal Nativa

Gemini 1.5 Flash-8B de Google es un modelo de IA diseñado para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, con ventana de contexto de 1.0M, velocidad de 160 tokens/s, índice de inteligencia de 37/100 y t

Intelligence Index37 / 100

Input Token:

$0.04 / 1M

Output Token:

$0.15 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

24.5%

Diferencias Clave de un Vistazo

Ministral 3B is 1.6x cheaper for API usage (input + output tokens combined).
Gemini 1.5 Flash-8B has a larger context window (1M vs 128K tokens).
Ministral 3B is faster at 180 tok/s vs 160 tok/s.
Gemini 1.5 Flash-8B leads on SWE-bench coding (24.5% vs 22.1%).

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

Característica	Ministral 3B	Gemini 1.5 Flash-8B
Intelligence Index Composite reasoning & knowledge score (0-100)	33	37
Provider	Mistral AI	Google
Input Price Cost per million input tokens	$0.04 / 1M	$0.04 / 1M
Output Price Cost per million output tokens	$0.04 / 1M	$0.15 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.03 / 1M	$0.03 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	180 tok/s	160 tok/s
SWE-bench Verified Real-world software engineering	22.1%	24.5%
GPQA Diamond Graduate-level reasoning	32.5%	38.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.01 / 1M

¿Quién gana en cada área?

At-a-glance decision framework

Código

Gemini 1.5 Flash-8B

+2.4% SWE-bench lead

Costo

Ministral 3B

1.6x cheaper monthly runtime

Velocidad

Ministral 3B

+13% faster generation

Contexto

Gemini 1.5 Flash-8B

1.05M tokens window

Puntuaciones de Benchmarks Verificadas

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

Ministral 3BGemini 1.5 Flash-8B

SWE-bench Verified (Autonomous Coding)

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

22.1%vs24.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

32.5%vs38.2%

MMLU-Pro (Multi-task Language Understanding)

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

49.8%vs58.6%

LMSYS Chatbot Arena Elo

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

1180ptsvs1220pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

2.4%vs3.5%

Terminal-Bench (Agentic Coding & Terminal Use)

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

14.8%vs16.5%

SciCode (Scientific Computing)

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

15.2%vs17.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

24.5%vs31.4%

AIME (Advanced Math Competition)

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

28.5%vs34.5%

Long-Context Reasoning

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

60.5%vs69.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

180 tok/s vs160 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

Ministral 3B0.21s

Gemini 1.5 Flash-8B0.25s

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

Ministral 3B2.99s

Gemini 1.5 Flash-8B3.38s

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)

Ministral 3B Monthly API Bill

$0.13 / month

$0.04/1M in · $0.04/1M out

Gemini 1.5 Flash-8B Monthly API Bill

$0.21 / month

$0.0375/1M in · $0.15/1M out

Ministral 3B saves approximately $0.08 / month (39%) under this workload.1.6x Savings

Ministral 3B · Strengths & Key Advantages

Ventana de contexto de 128K con hasta 8K tokens de salida máxima (SWE-bench Verified 22.1% · GPQA Diamond 32.5%)
Rendimiento ágil de 180 tokens/segundo con latencia inicial (TTFT) de solo 210 ms
Economía de API competitiva ($0.04/$0.04 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

Gemini 1.5 Flash-8B · Strengths & Key Advantages

Ventana de contexto de 1.0M con hasta 8K tokens de salida máxima (SWE-bench Verified 24.5% · GPQA Diamond 38.2%)
Rendimiento ágil de 160 tokens/segundo con latencia inicial (TTFT) de solo 250 ms
Economía de API competitiva ($0.0375/$0.15 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	Ministral 3B	Gemini 1.5 Flash-8B
Cost per Task Estimated USD to finish one task	$0.03	$0.04
Output Tokens / Task Tokens generated per task	15K	18K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	Ministral 3B	Gemini 1.5 Flash-8B
Official API Model ID (SDK)	ministral-3b-latest	models/gemini-1-5-flash-8b
API Protocol	Mistral AI REST API (OpenAI-Compatible)	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2024-10	2024-10
Knowledge Cutoff	October 2024	May 2024
Architecture	Ultra-Compact Dense Transformer	High-Throughput Multimodal Transformer
Parameters	3B	8B
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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($0.04/1M in) across full 128K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Cached input at $0.02/1M tokens	Implicit + Explicit Context Caching at $0.01/1M with configurable TTL
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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	API Prompt Engineering & RAG Distillation	Vertex AI Supervised Adapter Tuning & Context Distillation
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) · Video (~258–300 tokens/sec)
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	Google DeepMind Frontier Safety Framework 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	text, vision, audio, video
Output Modalities	text	text

Ministral 3B vs Gemini 1.5 Flash-8B: Respuestas Rápidas (FAQ)

Which is more intelligent, Ministral 3B or Gemini 1.5 Flash-8B?

Gemini 1.5 Flash-8B is more intelligent. It scores 37 compared with 33 on the composite Intelligence Index.

Which model is faster?

Ministral 3B is faster, generating 180 tokens per second compared with 160 tokens per second.

Which model has lower latency?

Ministral 3B has lower latency, with a time to first token of 0.21s compared with 0.25s.

Which model has a larger context window?

Gemini 1.5 Flash-8B has a larger context window, supporting 1.05M tokens compared with 128K tokens.

Veredicto

Tanto Ministral 3B (Ideal para máxima eficiencia de costos por token y alta velocidad en producción, sistemas RAG empresariales y automatización mediante API.) como Gemini 1.5 Flash-8B (Ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Gemini 1.5 Flash-8B lidera en inteligencia general y programación, Ministral 3B ofrece mayor velocidad de generación de tokens y Ministral 3B resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Ministral 3B o Gemini 1.5 Flash-8B?+

En las pruebas de inteligencia compuesta y resolución de código, Gemini 1.5 Flash-8B obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Ministral 3B frente a Gemini 1.5 Flash-8B?+

Ministral 3B cuesta $0.04 / $0.04 por millón de tokens de entrada/salida, mientras que Gemini 1.5 Flash-8B cuesta $0.04 / $0.15. En costo combinado, Ministral 3B es la opción más económica.

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

Ministral 3B cuenta con 128K tokens de contexto frente a 1.05M tokens de Gemini 1.5 Flash-8B. En velocidad de inferencia, Ministral 3B 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)

Ministral 3B vs Gemini 1.5 Flash-8B

Cambia y Compara Cualquier Modelo

Ministral 3B

Gemini 1.5 Flash-8B

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Ministral 3B · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 1.5 Flash-8B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Ministral 3B vs Gemini 1.5 Flash-8B: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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