Head-to-Head Model Comparison

Gemini 3.5 Flash vs Gemini Deep Research

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.

Gemini 3.5 FlashProfile

Gemini Deep ResearchProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 3.5 Flash

Modelo Insignia de Frontera

Gemini 3.5 Flash de Google es un modelo de IA diseñado para razonamiento empresarial de frontera, programación avanzada y flujos de producción, con ventana de contexto de 1.0M, velocidad de 190 tokens/s, índice de inteligencia de 82/100 y t

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 Agéntica y Uso de Herramientas

Gemini Deep Research de Google es un modelo de IA diseñado para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, con ventana de contexto de 1.0M, velocidad de 60 tokens/s, índice de inteligencia de 91

Intelligence Index91 / 100

Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Diferencias Clave de un Vistazo

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 →

Característica	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

¿Quién gana en cada área?

At-a-glance decision framework

Código

Evaluations ongoing

Costo

Gemini 3.5 Flash

11.5x cheaper monthly runtime

Velocidad

Gemini 3.5 Flash

+217% faster generation

Contexto

Tied

1.05M tokens window

Puntuaciones 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

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

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 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)

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

Ventana de contexto de 1.0M con hasta 66K tokens de salida máxima (SWE-bench Verified 70.2% · GPQA Diamond 81%)
Rendimiento ágil de 190 tokens/segundo con latencia inicial (TTFT) de solo 360 ms
Economía de API competitiva ($0.75/$3.75 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 Deep Research · Strengths & Key Advantages

Ventana de contexto de 1.0M con hasta 66K tokens de salida máxima (GPQA Diamond 85% · MMLU-Pro 89.5%)
Rendimiento ágil de 60 tokens/segundo con latencia inicial (TTFT) de solo 1500 ms
Economía de API competitiva ($10/$40 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	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

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Gemini 3.5 Flash vs Gemini Deep Research: Respuestas 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.

Veredicto

Tanto Gemini 3.5 Flash (Ideal para razonamiento empresarial de frontera, programación avanzada y flujos de producción, sistemas RAG empresariales y automatización mediante API.) como Gemini Deep Research (Ideal para flujos de trabajo agénticos autónomos, llamada a herramientas y automatización compleja, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Gemini Deep Research lidera en inteligencia general y programación, Gemini 3.5 Flash ofrece mayor velocidad de generación de tokens y Gemini 3.5 Flash resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Gemini 3.5 Flash o Gemini Deep Research?+

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

¿Cuánto cuesta la API de Gemini 3.5 Flash frente a Gemini Deep Research?+

Gemini 3.5 Flash cuesta $0.75 / $3.75 por millón de tokens de entrada/salida, mientras que Gemini Deep Research cuesta $10.00 / $40.00. En costo combinado, Gemini 3.5 Flash es la opción más económica.

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

Gemini 3.5 Flash cuenta con 1.05M tokens de contexto frente a 1.05M tokens de Gemini Deep Research. En velocidad de inferencia, Gemini 3.5 Flash 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)

Característica	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

Cambia y Compara Cualquier Modelo

Gemini 3.5 Flash

Gemini Deep Research

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos 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

Especificaciones Técnicas

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

Veredicto

Preguntas frecuentes

Related reading