Head-to-Head Model Comparison

Gemini 3.5 Flash vs Gemini Deep Research

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

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

Modello Ammiraglia di Frontiera

Gemini 3.5 Flash di Google è un modello di IA progettato per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, con finestra di contesto da 1.0M, velocità di 190 token/s, indice di intelligenza di

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 Agentica e Strumenti Autonomi

Gemini Deep Research di Google è un modello di IA progettato per i workflow agentici autonomi, il tool calling e l’automazione multi-step, con finestra di contesto da 1.0M, velocità di 60 token/s, indice di intelligenza di 91/100 e tariffa

Intelligence Index91 / 100

Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Differenze Chiave a Colpo d’Occhio

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

Chi vince in ogni 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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 1.0M con capacità massima di output fino a 66K token (SWE-bench Verified 70.2% · GPQA Diamond 81%)
Throughput elevato di 190 token/secondo con latenza al primo token (TTFT) di soli 360 ms
Economia API competitiva ($0.75/$3.75 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Gemini Deep Research · Strengths & Key Advantages

Finestra di contesto da 1.0M con capacità massima di output fino a 66K token (GPQA Diamond 85% · MMLU-Pro 89.5%)
Throughput elevato di 60 token/secondo con latenza al primo token (TTFT) di soli 1500 ms
Economia API competitiva ($10/$40 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

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

Specifiche Tecniche

Architecture, release timeline, capabilities, and modalities.

Gemini 3.5 Flash vs Gemini Deep Research: Risposte Rapide (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.

Verdetto

Sia Gemini 3.5 Flash (Ideale per il ragionamento aziendale di frontiera, la programmazione avanzata e i carichi di produzione, sistemi RAG aziendali e integrazioni API scalabili.) sia Gemini Deep Research (Ideale per i workflow agentici autonomi, il tool calling e l’automazione multi-step, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Gemini Deep Research guida nei benchmark di ragionamento e programmazione, Gemini 3.5 Flash garantisce una generazione più rapida e Gemini 3.5 Flash offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Gemini 3.5 Flash o Gemini Deep Research?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Gemini Deep Research. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Gemini 3.5 Flash e Gemini Deep Research?+

Gemini 3.5 Flash costa $0.75 / $3.75 per milione di token di input/output, mentre Gemini Deep Research costa $10.00 / $40.00. Nel costo combinato, Gemini 3.5 Flash risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Gemini 3.5 Flash supporta 1.05M token di contesto contro 1.05M token di Gemini Deep Research, mentre Gemini 3.5 Flash offre una velocità di generazione superiore.

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

Cambia e Confronta Qualsiasi Modello

Gemini 3.5 Flash

Gemini Deep Research

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Gemini 3.5 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Gemini Deep Research · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Gemini 3.5 Flash vs Gemini Deep Research: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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