Head-to-Head Model Comparison

Gemini 2.0 Flash vs GPT-4o mini

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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Gemini 2.0 FlashProfile

GPT-4o miniProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 2.0 Flash

Inferência Ultrarrápida

O Gemini 2.0 Flash da Google é um modelo de IA projetado para respostas em tempo real com latência ultrabaixa e roteamento de alto volume, oferecendo janela de contexto de 1.0M, velocidade de 240 tokens/s, índice de inteligência de 40/100 e

Intelligence Index40 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.40 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

42%

OpenAI

GPT-4o mini

Modelo Leve e Econômico

O GPT-4o mini da OpenAI 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 140 tokens/s, índice de inteligência de 30/100 e tarifa de $0.1

Intelligence Index30 / 100

Input Token:

$0.15 / 1M

Output Token:

$0.60 / 1M

Context Window:

128K tokens

SWE-bench Verified:

24.5%

Principais Diferenças em Resumo

Gemini 2.0 Flash is 1.6x cheaper for API usage (input + output tokens combined).
Gemini 2.0 Flash has a larger context window (1M vs 128K tokens).
Gemini 2.0 Flash is faster at 240 tok/s vs 140 tok/s.
Gemini 2.0 Flash leads on SWE-bench coding (42.0% vs 24.5%).

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

Feature	Gemini 2.0 Flash	GPT-4o mini
Intelligence Index Composite reasoning & knowledge score (0-100)	40	30
Provider	Google	OpenAI
Input Price Cost per million input tokens	$0.10 / 1M	$0.15 / 1M
Output Price Cost per million output tokens	$0.40 / 1M	$0.60 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.08 / 1M	$0.14 / 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	16K
Throughput Speed Tokens generated per second	240 tok/s	140 tok/s
SWE-bench Verified Real-world software engineering	42%	24.5%
GPQA Diamond Graduate-level reasoning	55.4%	40.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.07 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Gemini 2.0 Flash

+17.5% SWE-bench lead

Cost

Gemini 2.0 Flash

1.6x cheaper monthly runtime

Speed

Gemini 2.0 Flash

+71% faster generation

Context

Gemini 2.0 Flash

1.05M tokens window

Pontuações de Benchmarks Verificadas

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

Gemini 2.0 FlashGPT-4o mini

SWE-bench Verified (Autonomous Coding)

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

42%vs24.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

55.4%vs40.2%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs58.5%

LMSYS Chatbot Arena Elo

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

1302ptsvs1228pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

8.9%vs6.4%

Terminal-Bench (Agentic Coding & Terminal Use)

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

27.7%vs16.2%

SciCode (Scientific Computing)

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

25.2%vs14.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

44.9%vs28.3%

AIME (Advanced Math Competition)

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

63.7%vs46.2%

Long-Context Reasoning

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

63.9%vs52.7%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs140 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 2.0 Flash0.28s

GPT-4o mini0.31s

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

Gemini 2.0 Flash2.36s

GPT-4o mini3.88s

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 2.0 Flash Monthly API Bill

$0.55 / month

$0.1/1M in · $0.4/1M out

GPT-4o mini Monthly API Bill

$0.88 / month

$0.15/1M in · $0.6/1M out

Gemini 2.0 Flash saves approximately $0.33 / month (38%) under this workload.1.6x Savings

Gemini 2.0 Flash · Strengths & Key Advantages

Janela de contexto de 1.0M com limite máximo de saída de 8K tokens (SWE-bench Verified 42% · GPQA Diamond 55.4%)
Taxa de transferência rápida de 240 tokens/segundo com latência inicial (TTFT) de 280 ms
Economia de API competitiva ($0.1/$0.4 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

GPT-4o mini · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 16K tokens (SWE-bench Verified 24.5% · GPQA Diamond 40.2%)
Taxa de transferência rápida de 140 tokens/segundo com latência inicial (TTFT) de 310 ms
Economia de API competitiva ($0.15/$0.6 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 2.0 Flash	GPT-4o mini
Cost per Task Estimated USD to finish one task	$0.13	$0.16
Output Tokens / Task Tokens generated per task	60K	45K
Reasoning Tokens / Task Thinking tokens burned per task	33K	0K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	Gemini 2.0 Flash	GPT-4o mini
Official API Model ID (SDK)	models/gemini-2-flash	gpt-4o-mini
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	OpenAI Responses API & Chat Completions v1
Release Date	2024-12	2024-07
Knowledge Cutoff	August 2024	October 2023
Architecture	Multimodal Flash MoE	Small Multimodal Dense
Parameters	Confidential	Confidential
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 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 ($0.15/1M in) across full 128K window
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.025/1M with configurable TTL	Automatic prefix caching at $0.075/1M (no extra cache-write fee)
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
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) · 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	Google DeepMind Frontier Safety Framework 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, vision, audio, video	text, vision
Output Modalities	text	text

Gemini 2.0 Flash vs GPT-4o mini: Respostas Rápidas (FAQ)

Which is more intelligent, Gemini 2.0 Flash or GPT-4o mini?

Gemini 2.0 Flash is more intelligent. It scores 40 compared with 30 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Flash is faster, generating 240 tokens per second compared with 140 tokens per second.

Which model is cheaper to run?

Gemini 2.0 Flash is cheaper at $0.08 per 1M tokens blended, compared with $0.14 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 2.0 Flash has lower latency, with a time to first token of 0.28s compared with 0.31s.

Which model has a larger context window?

Gemini 2.0 Flash has a larger context window, supporting 1.05M tokens compared with 128K tokens.

Veredito

Tanto Gemini 2.0 Flash (Indicado para respostas em tempo real com latência ultrabaixa e roteamento de alto volume, arquiteturas RAG corporativas e integração escalável via API.) quanto GPT-4o mini (Indicado para máxima economia por token e alto desempenho em escala de produção, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Gemini 2.0 Flash lidera em inteligência geral e programação, Gemini 2.0 Flash oferece geração mais rápida e Gemini 2.0 Flash apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Gemini 2.0 Flash ou GPT-4o mini?+

Nas avaliações de inteligência e código, Gemini 2.0 Flash leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Gemini 2.0 Flash ou GPT-4o mini?+

O Gemini 2.0 Flash custa $0.10 / $0.40 por 1M de tokens (entrada/saída) e o GPT-4o mini custa $0.15 / $0.60. No custo combinado, Gemini 2.0 Flash é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Gemini 2.0 Flash suporta 1.05M tokens de contexto contra 128K tokens do GPT-4o mini, enquanto Gemini 2.0 Flash 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 2.0 Flash vs GPT-4o mini

Troque e Compare Qualquer Modelo

Gemini 2.0 Flash

GPT-4o mini

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Gemini 2.0 Flash · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4o mini · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Gemini 2.0 Flash vs GPT-4o mini: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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