Head-to-Head Model Comparison

Gemini 1.5 Flash-8B vs Ministral 8B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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Gemini 1.5 Flash-8BProfile

Ministral 8BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 1.5 Flash-8B

IA Multimodal Nativa

O Gemini 1.5 Flash-8B da Google é um modelo de IA projetado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, oferecendo janela de contexto de 1.0M, velocidade de 160 tokens/s, índice de inteligência de 37

Intelligence Index37 / 100

Input Token:

$0.04 / 1M

Output Token:

$0.15 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

24.5%

Mistral AI

Ministral 8B

Modelo Leve e Econômico

O Ministral 8B da Mistral AI é um modelo de IA projetado para máxima economia por token e alto desempenho em escala de produção, oferecendo janela de contexto de 128K, velocidade de 110 tokens/s, índice de inteligência de 38/100 e tarifa de

Intelligence Index38 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

32.5%

Principais Diferenças em Resumo

Gemini 1.5 Flash-8B is 1.5x cheaper for API usage (input + output tokens combined).
Gemini 1.5 Flash-8B has a larger context window (1M vs 128K tokens).
Gemini 1.5 Flash-8B is faster at 160 tok/s vs 110 tok/s.
Ministral 8B leads on SWE-bench coding (32.5% vs 24.5%).

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

Feature	Gemini 1.5 Flash-8B	Ministral 8B
Intelligence Index Composite reasoning & knowledge score (0-100)	37	38
Provider	Google	Mistral AI
Input Price Cost per million input tokens	$0.04 / 1M	$0.10 / 1M
Output Price Cost per million output tokens	$0.15 / 1M	$0.10 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.03 / 1M	$0.07 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,573 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	160 tok/s	110 tok/s
SWE-bench Verified Real-world software engineering	24.5%	32.5%
GPQA Diamond Graduate-level reasoning	38.2%	41.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.01 / 1M	$0.05 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Ministral 8B

+8.0% SWE-bench lead

Cost

Gemini 1.5 Flash-8B

1.5x cheaper monthly runtime

Speed

Gemini 1.5 Flash-8B

+45% faster generation

Context

Gemini 1.5 Flash-8B

1.05M tokens window

Pontuações de Benchmarks Verificadas

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

Gemini 1.5 Flash-8BMinistral 8B

SWE-bench Verified (Autonomous Coding)

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

24.5%vs32.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

38.2%vs41.5%

MMLU-Pro (Multi-task Language Understanding)

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

58.6%vs60.1%

LMSYS Chatbot Arena Elo

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

1220ptsvs1235pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

3.5%vs4.2%

Terminal-Bench (Agentic Coding & Terminal Use)

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

16.5%vs21%

SciCode (Scientific Computing)

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

17.8%vs21.8%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

31.4%vs35.8%

AIME (Advanced Math Competition)

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

34.5%vs41.2%

Long-Context Reasoning

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

69.2%vs65.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs110 tok/s

Latência e Tempo de Resposta

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

Gemini 1.5 Flash-8B0.25s

Ministral 8B0.33s

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

Gemini 1.5 Flash-8B3.38s

Ministral 8B4.88s

Simulador Interativo de Custo 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)

Gemini 1.5 Flash-8B Monthly API Bill

$0.21 / month

$0.0375/1M in · $0.15/1M out

Ministral 8B Monthly API Bill

$0.32 / month

$0.1/1M in · $0.1/1M out

Gemini 1.5 Flash-8B saves approximately $0.11 / month (35%) under this workload.1.5x Savings

Gemini 1.5 Flash-8B · Strengths & Key Advantages

Janela de contexto de 1.0M com limite máximo de saída de 8K tokens (SWE-bench Verified 24.5% · GPQA Diamond 38.2%)
Taxa de transferência rápida de 160 tokens/segundo com latência inicial (TTFT) de 250 ms
Economia de API competitiva ($0.0375/$0.15 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas

Considerations & Trade-offs

Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Ministral 8B · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 32.5% · GPQA Diamond 41.5%)
Taxa de transferência rápida de 110 tokens/segundo com latência inicial (TTFT) de 330 ms
Economia de API competitiva ($0.1/$0.1 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas

Considerations & Trade-offs

Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Token Use & Task Economics

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

Metric	Gemini 1.5 Flash-8B	Ministral 8B
Cost per Task Estimated USD to finish one task	$0.04	$0.08
Output Tokens / Task Tokens generated per task	18K	21K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	Gemini 1.5 Flash-8B	Ministral 8B
Official API Model ID (SDK)	models/gemini-1-5-flash-8b	ministral-8b-latest
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Mistral AI REST API (OpenAI-Compatible)
Release Date	2024-10	2024-10
Knowledge Cutoff	May 2024	October 2024
Architecture	High-Throughput Multimodal Transformer	Compact Dense Transformer with Interleaved Sliding Window
Parameters	8B	8B
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Flat rate ($0.1/1M in) across full 128K window
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.01/1M with configurable TTL	Cached input at $0.05/1M 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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	API Prompt Engineering & RAG Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	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	Google DeepMind Frontier Safety Framework Verified	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, audio, video	text
Output Modalities	text	text

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

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

Ministral 8B is more intelligent. It scores 38 compared with 37 on the composite Intelligence Index.

Which model is faster?

Gemini 1.5 Flash-8B is faster, generating 160 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

Gemini 1.5 Flash-8B is cheaper at $0.03 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 1.5 Flash-8B has lower latency, with a time to first token of 0.25s compared with 0.33s.

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.

Veredito

Tanto Gemini 1.5 Flash-8B (Indicado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, arquiteturas RAG corporativas e integração escalável via API.) quanto Ministral 8B (Indicado para máxima economia por token e alto desempenho em escala de produção, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Ministral 8B lidera em inteligência geral e programação, Gemini 1.5 Flash-8B oferece geração mais rápida e Gemini 1.5 Flash-8B apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Gemini 1.5 Flash-8B ou Ministral 8B?+

Nas avaliações de inteligência e código, Ministral 8B leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Gemini 1.5 Flash-8B ou Ministral 8B?+

O Gemini 1.5 Flash-8B custa $0.04 / $0.15 por 1M de tokens (entrada/saída) e o Ministral 8B custa $0.10 / $0.10. No custo combinado, Gemini 1.5 Flash-8B é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Gemini 1.5 Flash-8B suporta 1.05M tokens de contexto contra 128K tokens do Ministral 8B, enquanto Gemini 1.5 Flash-8B entrega maior velocidade de geração.

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)

Gemini 1.5 Flash-8B vs Ministral 8B

Troque e Compare Qualquer Modelo

Gemini 1.5 Flash-8B

Ministral 8B

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Gemini 1.5 Flash-8B · Strengths & Key Advantages

Considerations & Trade-offs

Ministral 8B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

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

Veredito

Perguntas Frequentes (FAQ)

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