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Gemini 3 Flash vs Gemini 3 Pro

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini 3 Flash vs Gemini 3 Pro: Benchmarks, Pricing & Speed Comparison

Gemini 3 Flash von Google und Gemini 3 Pro von Google gehören zu den führenden KI-Modellen. Gemini 3 Flash bietet ein Kontextfenster von 1.05M Tokens bei API-Preisen von $0.50/$3.00 pro 1M Tokens, während Gemini 3 Pro 2M Tokens Kontext zu $2.00/$12.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Google

Gemini 3 Flash

Ultraschnelle Echtzeit-Inferenz

Gemini 3 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 75/100 und $0.5/$3 pro 1M Token.

Intelligence Index75 / 100
Input Token:

$0.50 / 1M

Output Token:

$3.00 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

66.5%

Google

Gemini 3 Pro

Frontier-Flaggschiff-Modell

Gemini 3 Pro von Google ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 2M Kontextfenster, 118 Token/s Durchsatz, einem Intelligenz-Index von 62/100 und $2/$12 pro 1M Token.

Intelligence Index62 / 100
Input Token:

$2.00 / 1M

Output Token:

$12.00 / 1M

Context Window:

2M tokens

SWE-bench Verified:

76.2%

Wichtigste Unterschiede auf einen Blick

  • Gemini 3 Flash is 3.9x cheaper for API usage (input + output tokens combined).
  • Gemini 3 Pro has a larger context window (2M vs 1M tokens).
  • Gemini 3 Flash is faster at 180 tok/s vs 118 tok/s.
  • Gemini 3 Pro leads on SWE-bench coding (76.2% vs 66.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 3 FlashGemini 3 Pro
Intelligence Index
Composite reasoning & knowledge score (0-100)
7562
Provider
GoogleGoogle
Input Price
Cost per million input tokens
$0.50 / 1M$2.00 / 1M
Output Price
Cost per million output tokens
$3.00 / 1M$12.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.49 / 1M$1.74 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text2M tokens~3,000 A4 pages of text
Max Output Tokens
Per single request
66K66K
Throughput Speed
Tokens generated per second
180 tok/s118 tok/s
SWE-bench Verified
Real-world software engineering
66.5%76.2%
GPQA Diamond
Graduate-level reasoning
79%73.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.13 / 1M$0.20 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Gemini 3 Pro
+9.7% SWE-bench lead
Cost
Gemini 3 Flash
3.9x cheaper monthly runtime
Speed
Gemini 3 Flash
+53% faster generation
Context
Gemini 3 Pro
2M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemini 3 FlashGemini 3 Pro
SWE-bench Verified (Autonomous Coding)

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

66.5%vs76.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

79%vs73.5%
MMLU-Pro (Multi-task Language Understanding)

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

85.8%vs86.8%
LMSYS Chatbot Arena Elo

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

1425ptsvs1384pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs12.9%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs47.9%
SciCode (Scientific Computing)

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

N/Avs44.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs73%
AIME (Advanced Math Competition)

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

88%vs92.8%
Long-Context Reasoning

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

N/Avs84.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

180 tok/s vs118 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 Flash0.40s
Gemini 3 Pro1.80s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 3 Flash3.18s
Gemini 3 Pro6.04s

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 3 Flash Monthly API Bill
$3.64 / month
$0.5/1M in · $3/1M out
Gemini 3 Pro Monthly API Bill
$14.10 / month
$2/1M in · $12/1M out
Gemini 3 Flash saves approximately $10.46 / month (74%) under this workload.3.9x Savings

Gemini 3 Flash · Strengths & Key Advantages

  • 1.0M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 66.5% · GPQA Diamond 79%)
  • Hohe Generierungsgeschwindigkeit von 180 Token/Sekunde bei nur 400 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.5/$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

Gemini 3 Pro · Strengths & Key Advantages

  • 2M Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 76.2% · GPQA Diamond 73.5%)
  • Hohe Generierungsgeschwindigkeit von 118 Token/Sekunde bei nur 1800 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($2/$12 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.

MetricGemini 3 FlashGemini 3 Pro
Cost per Task
Estimated USD to finish one task
$0.95$2.40
Output Tokens / Task
Tokens generated per task
35K60K
Reasoning Tokens / Task
Thinking tokens burned per task
16K31K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 3 FlashGemini 3 Pro
Official API Model ID (SDK)models/gemini-3-flashmodels/gemini-3-pro
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2025-122025-11
Knowledge CutoffSeptember 2025January 2025
ArchitectureNext-gen sparse multimodal mixture-of-expertsSparse Multimodal 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 $0.125/1M with configurable TTLImplicit + Explicit Context Caching at $0.2/1M 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 3 Flash vs Gemini 3 Pro: Schnelle Antworten (FAQ)

Which is more intelligent, Gemini 3 Flash or Gemini 3 Pro?

Gemini 3 Flash is more intelligent. It scores 75 compared with 62 on the composite Intelligence Index.

Which model is faster?

Gemini 3 Flash is faster, generating 180 tokens per second compared with 118 tokens per second.

Which model is cheaper to run?

Gemini 3 Flash is cheaper at $0.49 per 1M tokens blended, compared with $1.74 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 3 Flash has lower latency, with a time to first token of 0.40s compared with 1.80s.

Which model has a larger context window?

Gemini 3 Pro has a larger context window, supporting 2M tokens compared with 1.05M tokens.

Fazit

Sowohl Gemini 3 Flash (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 3 Pro (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 3 Flash führt bei komplexem Reasoning und Coding, Gemini 3 Flash liefert den höheren Token-Durchsatz und Gemini 3 Flash ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 3 Flash oder Gemini 3 Pro?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 3 Flash vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Gemini 3 Flash und Gemini 3 Pro?+

Gemini 3 Flash kostet $0.50 / $3.00 pro 1M Input-/Output-Tokens, Gemini 3 Pro liegt bei $2.00 / $12.00. Beim gewichteten Gesamtpreis ist Gemini 3 Flash günstiger.

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

Gemini 3 Flash bietet 1.05M Tokens Kontext gegenüber 2M Tokens bei Gemini 3 Pro. Bei der Generierungsgeschwindigkeit liegt Gemini 3 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).