Head-to-Head Model Comparison

Gemini 3.5 Flash vs Gemini Deep Research

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

Partilhar

Troque e Compare Qualquer Modelo

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Gemini 3.5 FlashProfile

Gemini Deep ResearchProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 3.5 Flash

Modelo Principal de Fronteira

O Gemini 3.5 Flash da Google é um modelo de IA projetado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, oferecendo janela de contexto de 1.0M, velocidade de 190 tokens/s, índice de inteligência de 82/10

Intelligence Index82 / 100

Input Token:

$0.75 / 1M

Output Token:

$3.75 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

70.2%

Google

Gemini Deep Research

IA Agêntica e Uso de Ferramentas

O Gemini Deep Research da Google é um modelo de IA projetado para fluxos de trabalho agênticos autônomos, chamada de ferramentas e automação, oferecendo janela de contexto de 1.0M, velocidade de 60 tokens/s, índice de inteligência de 91/100

Intelligence Index91 / 100

Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Principais Diferenças em Resumo

Gemini 3.5 Flash is 11.5x cheaper for API usage (input + output tokens combined).
Gemini 3.5 Flash is faster at 190 tok/s vs 60 tok/s.

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

Feature	Gemini 3.5 Flash	Gemini Deep Research
Intelligence Index Composite reasoning & knowledge score (0-100)	82	91
Provider	Google	Google
Input Price Cost per million input tokens	$0.75 / 1M	$10.00 / 1M
Output Price Cost per million output tokens	$3.75 / 1M	$40.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.66 / 1M	$7.75 / 1M
Context Window Maximum input + output tokens	1.05M tokens~1,573 A4 pages of text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	66K	66K
Throughput Speed Tokens generated per second	190 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	70.2%	N/A
GPQA Diamond Graduate-level reasoning	81%	85%
Cached Input Price Prompt caching discount per 1M tokens	$0.19 / 1M	$2.50 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Gemini 3.5 Flash

11.5x cheaper monthly runtime

Speed

Gemini 3.5 Flash

+217% faster generation

Context

Tied

1.05M tokens window

Pontuações de Benchmarks Verificadas

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

Gemini 3.5 FlashGemini Deep Research

SWE-bench Verified (Autonomous Coding)

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

70.2%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

81%vs85%

MMLU-Pro (Multi-task Language Understanding)

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

87.1%vs89.5%

LMSYS Chatbot Arena Elo

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

1450ptsvs1495pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs17.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

190 tok/s vs60 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 3.5 Flash0.36s

Gemini Deep Research1.50s

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

Gemini 3.5 Flash2.99s

Gemini Deep Research9.83s

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 3.5 Flash Monthly API Bill

$4.78 / month

$0.75/1M in · $3.75/1M out

Gemini Deep Research Monthly API Bill

$54.75 / month

$10/1M in · $40/1M out

Gemini 3.5 Flash saves approximately $49.97 / month (91%) under this workload.11.5x Savings

Gemini 3.5 Flash · Strengths & Key Advantages

Janela de contexto de 1.0M com limite máximo de saída de 66K tokens (SWE-bench Verified 70.2% · GPQA Diamond 81%)
Taxa de transferência rápida de 190 tokens/segundo com latência inicial (TTFT) de 360 ms
Economia de API competitiva ($0.75/$3.75 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

Gemini Deep Research · Strengths & Key Advantages

Janela de contexto de 1.0M com limite máximo de saída de 66K tokens (GPQA Diamond 85% · MMLU-Pro 89.5%)
Taxa de transferência rápida de 60 tokens/segundo com latência inicial (TTFT) de 1500 ms
Economia de API competitiva ($10/$40 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
Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos

Token Use & Task Economics

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

Metric	Gemini 3.5 Flash	Gemini Deep Research
Cost per Task Estimated USD to finish one task	$1.05	$8.50
Output Tokens / Task Tokens generated per task	38K	50K
Reasoning Tokens / Task Thinking tokens burned per task	18K	35K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Gemini 3.5 Flash vs Gemini Deep Research: Respostas Rápidas (FAQ)

Which is more intelligent, Gemini 3.5 Flash or Gemini Deep Research?

Gemini Deep Research is more intelligent. It scores 91 compared with 82 on the composite Intelligence Index.

Which model is faster?

Gemini 3.5 Flash is faster, generating 190 tokens per second compared with 60 tokens per second.

Which model is cheaper to run?

Gemini 3.5 Flash is cheaper at $0.66 per 1M tokens blended, compared with $7.75 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3.5 Flash has lower latency, with a time to first token of 0.36s compared with 1.50s.

Veredito

Tanto Gemini 3.5 Flash (Indicado para raciocínio corporativo de fronteira, programação avançada e cargas de produção, arquiteturas RAG corporativas e integração escalável via API.) quanto Gemini Deep Research (Indicado para fluxos de trabalho agênticos autônomos, chamada de ferramentas e automação, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Gemini Deep Research lidera em inteligência geral e programação, Gemini 3.5 Flash oferece geração mais rápida e Gemini 3.5 Flash apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Gemini 3.5 Flash ou Gemini Deep Research?+

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

Qual modelo tem a API mais barata, Gemini 3.5 Flash ou Gemini Deep Research?+

O Gemini 3.5 Flash custa $0.75 / $3.75 por 1M de tokens (entrada/saída) e o Gemini Deep Research custa $10.00 / $40.00. No custo combinado, Gemini 3.5 Flash é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Gemini 3.5 Flash suporta 1.05M tokens de contexto contra 1.05M tokens do Gemini Deep Research, enquanto Gemini 3.5 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)

Feature	Gemini 3.5 Flash	Gemini Deep Research
Official API Model ID (SDK)	models/gemini-3-5-flash	models/gemini-deep-research
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2026-05	2026-04
Knowledge Cutoff	January 2026	Real-time Google Search Integration
Architecture	Optimized Search-Grade Multimodal MoE	Agentic recursive research and synthesis engine
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) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.1875/1M with configurable TTL	Implicit + Explicit Context Caching at $2.5/1M with configurable TTL
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	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	Vertex AI Supervised Adapter Tuning & Context 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) · 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	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, vision, audio, video	text, vision
Output Modalities	text	text

Gemini 3.5 Flash vs Gemini Deep Research

Troque e Compare Qualquer Modelo

Gemini 3.5 Flash

Gemini Deep Research

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Gemini 3.5 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Gemini Deep Research · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Gemini 3.5 Flash vs Gemini Deep Research: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

Related reading