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Head-to-Head Model Comparison

Gemini 2.0 Pro vs Gemini 2.0 Flash

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini 2.0 Pro vs Gemini 2.0 Flash: Benchmarks, Pricing & Speed Comparison

Gemini 2.0 Pro von Google und Gemini 2.0 Flash von Google gehören zu den führenden KI-Modellen. Gemini 2.0 Pro bietet ein Kontextfenster von 2M Tokens bei API-Preisen von $1.25/$5.00 pro 1M Tokens, während Gemini 2.0 Flash 1.05M Tokens Kontext zu $0.10/$0.40 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Google

Gemini 2.0 Pro

Frontier-Flaggschiff-Modell

Gemini 2.0 Pro von Google ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 2M Kontextfenster, 95 Token/s Durchsatz, einem Intelligenz-Index von 52/100 und $1.25/$5 pro 1M Token.

Intelligence Index52 / 100
Input Token:

$1.25 / 1M

Output Token:

$5.00 / 1M

Context Window:

2M tokens

SWE-bench Verified:

63.8%

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%

Wichtigste Unterschiede auf einen Blick

  • Gemini 2.0 Flash is 12.5x cheaper for API usage (input + output tokens combined).
  • Gemini 2.0 Pro has a larger context window (2M vs 1M tokens).
  • Gemini 2.0 Flash is faster at 240 tok/s vs 95 tok/s.
  • Gemini 2.0 Pro leads on SWE-bench coding (63.8% vs 42.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 2.0 ProGemini 2.0 Flash
Intelligence Index
Composite reasoning & knowledge score (0-100)
5240
Provider
GoogleGoogle
Input Price
Cost per million input tokens
$1.25 / 1M$0.10 / 1M
Output Price
Cost per million output tokens
$5.00 / 1M$0.40 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.97 / 1M$0.08 / 1M
Context Window
Maximum input + output tokens
2M tokens~3,000 A4 pages of text1.05M tokens~1,573 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
95 tok/s240 tok/s
SWE-bench Verified
Real-world software engineering
63.8%42%
GPQA Diamond
Graduate-level reasoning
71.5%55.4%
Cached Input Price
Prompt caching discount per 1M tokens
$0.31 / 1M$0.03 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Gemini 2.0 Pro
+21.8% SWE-bench lead
Cost
Gemini 2.0 Flash
12.5x cheaper monthly runtime
Speed
Gemini 2.0 Flash
+153% faster generation
Context
Gemini 2.0 Pro
2M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemini 2.0 ProGemini 2.0 Flash
SWE-bench Verified (Autonomous Coding)

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

63.8%vs42%
GPQA Diamond (Doctorate-Level Science & Math)

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

71.5%vs55.4%
MMLU-Pro (Multi-task Language Understanding)

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

79.8%vs71%
LMSYS Chatbot Arena Elo

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

1330ptsvs1302pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

11.4%vs8.9%
Terminal-Bench (Agentic Coding & Terminal Use)

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

42.1%vs27.7%
SciCode (Scientific Computing)

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

38.3%vs25.2%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65.6%vs44.9%
AIME (Advanced Math Competition)

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

82.2%vs63.7%
Long-Context Reasoning

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

71.8%vs63.9%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

95 tok/s vs240 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 Pro0.62s
Gemini 2.0 Flash0.28s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 2.0 Pro5.88s
Gemini 2.0 Flash2.36s

Interaktiver Token-Kosten-Simulator

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemini 2.0 Pro Monthly API Bill
$6.84 / month
$1.25/1M in · $5/1M out
Gemini 2.0 Flash Monthly API Bill
$0.55 / month
$0.1/1M in · $0.4/1M out
Gemini 2.0 Flash saves approximately $6.29 / month (92%) under this workload.12.5x Savings

Gemini 2.0 Pro · Strengths & Key Advantages

  • 2M Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 63.8% · GPQA Diamond 71.5%)
  • Hohe Generierungsgeschwindigkeit von 95 Token/Sekunde bei nur 620 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($1.25/$5 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 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

Token Use & Task Economics

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

MetricGemini 2.0 ProGemini 2.0 Flash
Cost per Task
Estimated USD to finish one task
$1.59$0.13
Output Tokens / Task
Tokens generated per task
60K60K
Reasoning Tokens / Task
Thinking tokens burned per task
33K33K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 2.0 ProGemini 2.0 Flash
Official API Model ID (SDK)models/gemini-2-promodels/gemini-2-flash
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2025-022024-12
Knowledge CutoffJanuary 2025August 2024
ArchitectureMultimodal Deep Reasoning Sparse MoEMultimodal Flash MoE
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM10,000 – 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 tokensStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.31/1M with configurable TTLImplicit + Explicit Context Caching at $0.025/1M with configurable TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationVertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText 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 VRAMCloud-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 ClassificationGoogle DeepMind Frontier Safety Framework VerifiedGoogle DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, vision, audio, videotext, vision, audio, video
Output Modalitiestexttext

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

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

Gemini 2.0 Pro is more intelligent. It scores 52 compared with 40 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Flash is faster, generating 240 tokens per second compared with 95 tokens per second.

Which model is cheaper to run?

Gemini 2.0 Flash is cheaper at $0.08 per 1M tokens blended, compared with $0.97 (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 0.62s.

Which model has a larger context window?

Gemini 2.0 Pro has a larger context window, supporting 2M tokens compared with 1.05M tokens.

Fazit

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

Häufig gestellte Fragen (FAQ)

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

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

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

Gemini 2.0 Pro kostet $1.25 / $5.00 pro 1M Input-/Output-Tokens, Gemini 2.0 Flash liegt bei $0.10 / $0.40. Beim gewichteten Gesamtpreis ist Gemini 2.0 Flash günstiger.

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

Gemini 2.0 Pro bietet 2M Tokens Kontext gegenüber 1.05M Tokens bei Gemini 2.0 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Offizielle Quellen: Offizielle API-Dokumentation von Google und Google, Systemkarten und unabhängige Benchmark-Auswertungen (2026).