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Gemini 2.0 Flash-Lite vs Gemini 1.5 Flash-8B

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 Flash-Lite vs Gemini 1.5 Flash-8B: Benchmarks, Pricing & Speed Comparison

Gemini 2.0 Flash-Lite von Google und Gemini 1.5 Flash-8B von Google gehören zu den führenden KI-Modellen. Gemini 2.0 Flash-Lite bietet ein Kontextfenster von 1.05M Tokens bei API-Preisen von $0.07/$0.30 pro 1M Tokens, während Gemini 1.5 Flash-8B 1.05M Tokens Kontext zu $0.04/$0.15 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Google

Gemini 2.0 Flash-Lite

Kompaktes Effizienz-Modell

Gemini 2.0 Flash-Lite von Google ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 1.0M Kontextfenster, 280 Token/s Durchsatz, einem Intelligenz-Index von 48/100 und $0.075/$0.3 pro 1

Intelligence Index48 / 100
Input Token:

$0.07 / 1M

Output Token:

$0.30 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Google

Gemini 1.5 Flash-8B

Nativ Multimodale KI

Gemini 1.5 Flash-8B von Google ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 1.0M Kontextfenster, 160 Token/s Durchsatz, einem Intelligenz-Index von 37/100 und $0.0375/$0.15 pro

Intelligence Index37 / 100
Input Token:

$0.04 / 1M

Output Token:

$0.15 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

24.5%

Wichtigste Unterschiede auf einen Blick

  • Gemini 1.5 Flash-8B is 2.0x cheaper for API usage (input + output tokens combined).
  • Gemini 2.0 Flash-Lite is faster at 280 tok/s vs 160 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 2.0 Flash-LiteGemini 1.5 Flash-8B
Intelligence Index
Composite reasoning & knowledge score (0-100)
4837
Provider
GoogleGoogle
Input Price
Cost per million input tokens
$0.07 / 1M$0.04 / 1M
Output Price
Cost per million output tokens
$0.30 / 1M$0.15 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.06 / 1M$0.03 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 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
280 tok/s160 tok/s
SWE-bench Verified
Real-world software engineering
N/A24.5%
GPQA Diamond
Graduate-level reasoning
N/A38.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.02 / 1M$0.01 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Gemini 1.5 Flash-8B
2.0x cheaper monthly runtime
Speed
Gemini 2.0 Flash-Lite
+75% 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 Flash-LiteGemini 1.5 Flash-8B
SWE-bench Verified (Autonomous Coding)

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

N/Avs24.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs38.2%
MMLU-Pro (Multi-task Language Understanding)

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

72.5%vs58.6%
LMSYS Chatbot Arena Elo

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

1305ptsvs1220pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs3.5%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs16.5%
SciCode (Scientific Computing)

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

N/Avs17.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs31.4%
AIME (Advanced Math Competition)

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

N/Avs34.5%
Long-Context Reasoning

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

N/Avs69.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

280 tok/s vs160 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 Flash-Lite0.29s
Gemini 1.5 Flash-8B0.25s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 2.0 Flash-Lite2.08s
Gemini 1.5 Flash-8B3.38s

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 Flash-Lite Monthly API Bill
$0.41 / month
$0.075/1M in · $0.3/1M out
Gemini 1.5 Flash-8B Monthly API Bill
$0.21 / month
$0.0375/1M in · $0.15/1M out
Gemini 1.5 Flash-8B saves approximately $0.20 / month (50%) under this workload.2.0x Savings

Gemini 2.0 Flash-Lite · Strengths & Key Advantages

  • 1.0M Kontextfenster und bis zu 8K maximale Output-Token (MMLU-Pro 72.5% · Arena Elo 1305)
  • Hohe Generierungsgeschwindigkeit von 280 Token/Sekunde bei nur 290 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

Gemini 1.5 Flash-8B · Strengths & Key Advantages

  • 1.0M Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 24.5% · GPQA Diamond 38.2%)
  • Hohe Generierungsgeschwindigkeit von 160 Token/Sekunde bei nur 250 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.0375/$0.15 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 Flash-LiteGemini 1.5 Flash-8B
Cost per Task
Estimated USD to finish one task
$0.25$0.04
Output Tokens / Task
Tokens generated per task
6K18K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 2.0 Flash-LiteGemini 1.5 Flash-8B
Official API Model ID (SDK)models/gemini-2-0-flash-litemodels/gemini-1-5-flash-8b
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2025-022024-10
Knowledge CutoffAugust 2024May 2024
ArchitectureDistilled lightweight multimodal transformerHigh-Throughput Multimodal Transformer
ParametersConfidential8B
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 $0.01875/1M with configurable TTLImplicit + Explicit Context Caching at $0.01/1M 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 Flash-Lite vs Gemini 1.5 Flash-8B: Schnelle Antworten (FAQ)

Which is more intelligent, Gemini 2.0 Flash-Lite or Gemini 1.5 Flash-8B?

Gemini 2.0 Flash-Lite is more intelligent. It scores 48 compared with 37 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Flash-Lite is faster, generating 280 tokens per second compared with 160 tokens per second.

Which model is cheaper to run?

Gemini 1.5 Flash-8B is cheaper at $0.03 per 1M tokens blended, compared with $0.06 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 1.5 Flash-8B has lower latency, with a time to first token of 0.25s compared with 0.29s.

Fazit

Sowohl Gemini 2.0 Flash-Lite (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 1.5 Flash-8B (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 2.0 Flash-Lite führt bei komplexem Reasoning und Coding, Gemini 2.0 Flash-Lite liefert den höheren Token-Durchsatz und Gemini 1.5 Flash-8B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 2.0 Flash-Lite oder Gemini 1.5 Flash-8B?+

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

Wie unterscheiden sich die API-Preise von Gemini 2.0 Flash-Lite und Gemini 1.5 Flash-8B?+

Gemini 2.0 Flash-Lite kostet $0.07 / $0.30 pro 1M Input-/Output-Tokens, Gemini 1.5 Flash-8B liegt bei $0.04 / $0.15. Beim gewichteten Gesamtpreis ist Gemini 1.5 Flash-8B günstiger.

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

Gemini 2.0 Flash-Lite bietet 1.05M Tokens Kontext gegenüber 1.05M Tokens bei Gemini 1.5 Flash-8B. Bei der Generierungsgeschwindigkeit liegt Gemini 2.0 Flash-Lite 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).