Head-to-Head Model Comparison

Gemini 2.0 Flash vs Gemini 1.5 Flash

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Gemini 2.0 FlashProfile

Gemini 1.5 FlashProfile

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Popular Matchups:

Google

Gemini 2.0 Flash

Ultraschnelle Echtzeit-Inferenz

Gemini 2.0 Flash von Google ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 1.0M Kontextfenster, 240 Token/s Durchsatz, einem Intelligenz-Index von 40/100 und $0.1/$0.4 pro 1M Token.

Intelligence Index40 / 100

Input Token:

$0.10 / 1M

Output Token:

$0.40 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

42%

Google

Gemini 1.5 Flash

Ultraschnelle Echtzeit-Inferenz

Gemini 1.5 Flash von Google ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 1.0M Kontextfenster, 180 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und $0.075/$0.3 pro 1M Token.

Intelligence Index38 / 100

Input Token:

$0.07 / 1M

Output Token:

$0.30 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

20%

Wichtigste Unterschiede auf einen Blick

Gemini 1.5 Flash is 1.3x cheaper for API usage (input + output tokens combined).
Gemini 2.0 Flash is faster at 240 tok/s vs 180 tok/s.
Gemini 2.0 Flash leads on SWE-bench coding (42.0% vs 20.0%).

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

Feature	Gemini 2.0 Flash	Gemini 1.5 Flash
Intelligence Index Composite reasoning & knowledge score (0-100)	40	38
Provider	Google	Google
Input Price Cost per million input tokens	$0.10 / 1M	$0.07 / 1M
Output Price Cost per million output tokens	$0.40 / 1M	$0.30 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.08 / 1M	$0.06 / 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	8K	8K
Throughput Speed Tokens generated per second	240 tok/s	180 tok/s
SWE-bench Verified Real-world software engineering	42%	20%
GPQA Diamond Graduate-level reasoning	55.4%	42.1%
Cached Input Price Prompt caching discount per 1M tokens	$0.03 / 1M	$0.02 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Gemini 2.0 Flash

+22.0% SWE-bench lead

Cost

Gemini 1.5 Flash

1.3x cheaper monthly runtime

Speed

Gemini 2.0 Flash

+33% faster generation

Context

Tied

1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemini 2.0 FlashGemini 1.5 Flash

SWE-bench Verified (Autonomous Coding)

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

42%vs20%

GPQA Diamond (Doctorate-Level Science & Math)

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

55.4%vs42.1%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs69.8%

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%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

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

27.7%vsN/A

SciCode (Scientific Computing)

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

25.2%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

44.9%vsN/A

AIME (Advanced Math Competition)

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

63.7%vsN/A

Long-Context Reasoning

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

63.9%vs82.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

240 tok/s vs180 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 2.0 Flash0.28s

Gemini 1.5 Flash0.32s

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

Gemini 2.0 Flash2.36s

Gemini 1.5 Flash3.10s

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 2.0 Flash Monthly API Bill

$0.55 / month

$0.1/1M in · $0.4/1M out

Gemini 1.5 Flash Monthly API Bill

$0.41 / month

$0.075/1M in · $0.3/1M out

Gemini 1.5 Flash saves approximately $0.14 / month (25%) under this workload.1.3x Savings

Gemini 2.0 Flash · Strengths & Key Advantages

1.0M Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 42% · GPQA Diamond 55.4%)
Hohe Generierungsgeschwindigkeit von 240 Token/Sekunde bei nur 280 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.1/$0.4 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 1.5 Flash · Strengths & Key Advantages

1.0M Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 20% · GPQA Diamond 42.1%)
Hohe Generierungsgeschwindigkeit von 180 Token/Sekunde bei nur 320 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.075/$0.3 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

Token Use & Task Economics

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

Metric	Gemini 2.0 Flash	Gemini 1.5 Flash
Cost per Task Estimated USD to finish one task	$0.13	$0.20
Output Tokens / Task Tokens generated per task	60K	24K
Reasoning Tokens / Task Thinking tokens burned per task	33K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Gemini 2.0 Flash vs Gemini 1.5 Flash: Schnelle Antworten (FAQ)

Which is more intelligent, Gemini 2.0 Flash or Gemini 1.5 Flash?

Gemini 2.0 Flash is more intelligent. It scores 40 compared with 38 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Flash is faster, generating 240 tokens per second compared with 180 tokens per second.

Which model is cheaper to run?

Gemini 1.5 Flash is cheaper at $0.06 per 1M tokens blended, compared with $0.08 (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 0.32s.

Fazit

Sowohl Gemini 2.0 Flash (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 1.5 Flash (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 2.0 Flash führt bei komplexem Reasoning und Coding, Gemini 2.0 Flash liefert den höheren Token-Durchsatz und Gemini 1.5 Flash ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 2.0 Flash oder Gemini 1.5 Flash?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 2.0 Flash vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Gemini 2.0 Flash und Gemini 1.5 Flash?+

Gemini 2.0 Flash kostet $0.10 / $0.40 pro 1M Input-/Output-Tokens, Gemini 1.5 Flash liegt bei $0.07 / $0.30. Beim gewichteten Gesamtpreis ist Gemini 1.5 Flash günstiger.

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

Gemini 2.0 Flash bietet 1.05M Tokens Kontext gegenüber 1.05M Tokens bei Gemini 1.5 Flash. Bei der Generierungsgeschwindigkeit liegt Gemini 2.0 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 2.0 Flash	Gemini 1.5 Flash
Official API Model ID (SDK)	models/gemini-2-flash	models/gemini-1-5-flash
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2024-12	2024-05
Knowledge Cutoff	August 2024	May 2024
Architecture	Multimodal Flash MoE	Distilled Sparse MoE Multimodal
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 $0.025/1M with configurable TTL	Implicit + Explicit Context Caching at $0.01875/1M 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)
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, audio, video
Output Modalities	text	text

Gemini 2.0 Flash vs Gemini 1.5 Flash

Beliebige Modelle wechseln & vergleichen

Gemini 2.0 Flash

Gemini 1.5 Flash

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Gemini 2.0 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 1.5 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Gemini 2.0 Flash vs Gemini 1.5 Flash: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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