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Head-to-Head Model Comparison

Gemini 2.0 Flash-Lite vs Amazon Nova Lite

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 Amazon Nova Lite: Benchmarks, Pricing & Speed Comparison

Gemini 2.0 Flash-Lite von Google und Amazon Nova Lite von Amazon 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 Amazon Nova Lite 300K Tokens Kontext zu $0.06/$0.24 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Popular Matchups:
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

Amazon

Amazon Nova Lite

Nativ Multimodale KI

Amazon Nova Lite von Amazon ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 300K Kontextfenster, 135 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und $0.06/$0.24 pro 1M To

Intelligence Index38 / 100
Input Token:

$0.06 / 1M

Output Token:

$0.24 / 1M

Context Window:

300K tokens

SWE-bench Verified:

31.2%

Wichtigste Unterschiede auf einen Blick

  • Amazon Nova Lite is 1.3x cheaper for API usage (input + output tokens combined).
  • Gemini 2.0 Flash-Lite has a larger context window (1M vs 300K tokens).
  • Gemini 2.0 Flash-Lite is faster at 280 tok/s vs 135 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 2.0 Flash-LiteAmazon Nova Lite
Intelligence Index
Composite reasoning & knowledge score (0-100)
4838
Provider
GoogleAmazon
Input Price
Cost per million input tokens
$0.07 / 1M$0.06 / 1M
Output Price
Cost per million output tokens
$0.30 / 1M$0.24 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.06 / 1M$0.05 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text300K tokens~450 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
280 tok/s135 tok/s
SWE-bench Verified
Real-world software engineering
N/A31.2%
GPQA Diamond
Graduate-level reasoning
N/A42%
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
Amazon Nova Lite
1.3x cheaper monthly runtime
Speed
Gemini 2.0 Flash-Lite
+107% faster generation
Context
Gemini 2.0 Flash-Lite
1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemini 2.0 Flash-LiteAmazon Nova Lite
SWE-bench Verified (Autonomous Coding)

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

N/Avs31.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs42%
MMLU-Pro (Multi-task Language Understanding)

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

72.5%vs61.2%
LMSYS Chatbot Arena Elo

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

1305ptsvs1230pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs4%
Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs20.5%
SciCode (Scientific Computing)

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

N/Avs21%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs34.2%
AIME (Advanced Math Competition)

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

N/Avs39.5%
Long-Context Reasoning

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

N/Avs66%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

280 tok/s vs135 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
Amazon Nova Lite0.28s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 2.0 Flash-Lite2.08s
Amazon Nova Lite3.98s

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
Amazon Nova Lite Monthly API Bill
$0.33 / month
$0.06/1M in · $0.24/1M out
Amazon Nova Lite saves approximately $0.08 / month (20%) under this workload.1.3x 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

Amazon Nova Lite · Strengths & Key Advantages

  • 300K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 31.2% · GPQA Diamond 42%)
  • Hohe Generierungsgeschwindigkeit von 135 Token/Sekunde bei nur 280 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.06/$0.24 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-LiteAmazon Nova Lite
Cost per Task
Estimated USD to finish one task
$0.25$0.06
Output Tokens / Task
Tokens generated per task
6K19K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 2.0 Flash-LiteAmazon Nova Lite
Official API Model ID (SDK)models/gemini-2-0-flash-litenova-lite
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKAmazon REST API (OpenAI-Compatible)
Release Date2025-022024-12
Knowledge CutoffAugust 2024October 2024
ArchitectureDistilled lightweight multimodal transformerHigh-Speed Multimodal Foundation Transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($0.06/1M in) across full 300K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.01875/1M with configurable TTLCached input at $0.015/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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 SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationAPI Prompt Engineering & RAG 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) · 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 VerifiedAmazon Production Safety & Alignment 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, video
Output Modalitiestexttext

Gemini 2.0 Flash-Lite vs Amazon Nova Lite: Schnelle Antworten (FAQ)

Which is more intelligent, Gemini 2.0 Flash-Lite or Amazon Nova Lite?

Gemini 2.0 Flash-Lite is more intelligent. It scores 48 compared with 38 on the composite Intelligence Index.

Which model is faster?

Gemini 2.0 Flash-Lite is faster, generating 280 tokens per second compared with 135 tokens per second.

Which model is cheaper to run?

Amazon Nova Lite is cheaper at $0.05 per 1M tokens blended, compared with $0.06 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Amazon Nova Lite has lower latency, with a time to first token of 0.28s compared with 0.29s.

Which model has a larger context window?

Gemini 2.0 Flash-Lite has a larger context window, supporting 1.05M tokens compared with 300K tokens.

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 Amazon Nova Lite (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 Amazon Nova Lite 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 Amazon Nova Lite?+

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 Amazon Nova Lite?+

Gemini 2.0 Flash-Lite kostet $0.07 / $0.30 pro 1M Input-/Output-Tokens, Amazon Nova Lite liegt bei $0.06 / $0.24. Beim gewichteten Gesamtpreis ist Amazon Nova Lite günstiger.

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

Gemini 2.0 Flash-Lite bietet 1.05M Tokens Kontext gegenüber 300K Tokens bei Amazon Nova Lite. 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 Amazon, Systemkarten und unabhängige Benchmark-Auswertungen (2026).