Head-to-Head Model Comparison

Gemini 3.5 Flash vs Gemini Deep Research

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Gemini 3.5 FlashProfile

Gemini Deep ResearchProfile

Multi-Model Arena Stack:

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Popular Matchups:

Google

Gemini 3.5 Flash

Frontier-Flaggschiff-Modell

Gemini 3.5 Flash von Google ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 1.0M Kontextfenster, 190 Token/s Durchsatz, einem Intelligenz-Index von 82/100 und $0.75/$3.75 pro 1

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

Autonomes Agenten-Modell

Gemini Deep Research von Google ist ein KI-Modell für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung mit 1.0M Kontextfenster, 60 Token/s Durchsatz, einem Intelligenz-Index von 91/100 und $10/$40 pro 1

Intelligence Index91 / 100

Input Token:

$10.00 / 1M

Output Token:

$40.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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

1.0M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 70.2% · GPQA Diamond 81%)
Hohe Generierungsgeschwindigkeit von 190 Token/Sekunde bei nur 360 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.75/$3.75 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe

Considerations & Trade-offs

Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Gemini Deep Research · Strengths & Key Advantages

1.0M Kontextfenster und bis zu 66K maximale Output-Token (GPQA Diamond 85% · MMLU-Pro 89.5%)
Hohe Generierungsgeschwindigkeit von 60 Token/Sekunde bei nur 1500 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($10/$40 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe

Considerations & Trade-offs

Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

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

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Gemini 3.5 Flash vs Gemini Deep Research: Schnelle Antworten (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.

Fazit

Sowohl Gemini 3.5 Flash (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini Deep Research (Optimal für autonome Agenten-Workflows, zuverlässiges Tool-Calling und mehrstufige Automatisierung, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini Deep Research führt bei komplexem Reasoning und Coding, Gemini 3.5 Flash liefert den höheren Token-Durchsatz und Gemini 3.5 Flash ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 3.5 Flash oder Gemini Deep Research?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini Deep Research vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Gemini 3.5 Flash und Gemini Deep Research?+

Gemini 3.5 Flash kostet $0.75 / $3.75 pro 1M Input-/Output-Tokens, Gemini Deep Research liegt bei $10.00 / $40.00. Beim gewichteten Gesamtpreis ist Gemini 3.5 Flash günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Gemini 3.5 Flash bietet 1.05M Tokens Kontext gegenüber 1.05M Tokens bei Gemini Deep Research. Bei der Generierungsgeschwindigkeit liegt Gemini 3.5 Flash vorn.

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

Beliebige Modelle wechseln & vergleichen

Gemini 3.5 Flash

Gemini Deep Research

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Gemini 3.5 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Gemini Deep Research · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Gemini 3.5 Flash vs Gemini Deep Research: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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