Head-to-Head Model Comparison

Gemini 2.0 Flash-Lite vs Amazon Nova Lite

Benchmarks, API Token Pricing, Context Window & Speed Comparison

由 GetInfoUs 研究团队审核最后审核日期:October 2026编辑政策
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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

Google 的 Gemini 2.0 Flash-Lite 与 Amazon 的 Amazon Nova Lite 是当前备受关注的两款主力 AI 模型。Gemini 2.0 Flash-Lite 提供 1.05M Token 上下文窗口,API 定价为每百万 Token 输入 $0.07 / 输出 $0.30;而 Amazon Nova Lite 提供 300K Token 上下文窗口,定价为每百万 Token 输入 $0.06 / 输出 $0.24。以下为两者的基准测试、延迟与成本全面对比。

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Google

Gemini 2.0 Flash-Lite

轻量级高性价比模型

Google 推出的 Gemini 2.0 Flash-Lite 是一款兼顾极低 API 调用成本与高吞吐效率的轻量级大模型,具备 1.0M 上下文窗口、280 Token/秒生成速度与 48/100 综合智能指数(每百万 Token $0.075 输入 / $0.3 输出)。

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

原生多模态视觉模型

Amazon 推出的 Amazon Nova Lite 是一款原生支持图文多模态理解、视觉图表解析与跨模态推理的大模型,具备 300K 上下文窗口、135 Token/秒生成速度与 38/100 综合智能指数(每百万 Token $0.06 输入 / $0.24 输出)。

Intelligence Index38 / 100
Input Token:

$0.06 / 1M

Output Token:

$0.24 / 1M

Context Window:

300K tokens

SWE-bench Verified:

31.2%

核心差异一览

  • 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

各项指标谁更胜一筹?

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

权威基准测试得分对比

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

延迟与响应速度

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

实时 Token 成本模拟器

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

  • 提供 1.0M 上下文窗口与最高 8K 输出容量,核心基准表现优异(MMLU-Pro 72.5% · Arena Elo 1305)
  • 高达 280 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 290 毫秒
  • 极具竞争力的 API 定价(每百万 Token $0.075 输入 / $0.3 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
  • 在超长链路的前沿科研推导任务上,深度略逊于顶配重型旗舰推理模型

Amazon Nova Lite · Strengths & Key Advantages

  • 提供 300K 上下文窗口与最高 8K 输出容量,核心基准表现优异(SWE-bench Verified 31.2% · GPQA Diamond 42%)
  • 高达 135 Token/秒的生成吞吐量,首字延迟 (TTFT) 仅约 280 毫秒
  • 极具竞争力的 API 定价(每百万 Token $0.06 输入 / $0.24 输出),原生支持提示词缓存、函数调用与结构化 JSON 输出
Considerations & Trade-offs
  • 闭源专有云端模型,不支持下载权重进行离线本地物理隔离部署
  • 在超长链路的前沿科研推导任务上,深度略逊于顶配重型旗舰推理模型

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

技术规格参数

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: 常见问题解答 (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.

综合结论

综合来看,Gemini 2.0 Flash-Lite(适合:兼顾极低 API 调用成本与高吞吐效率的轻量级任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)与 Amazon Nova Lite(适合:原生支持图文多模态理解、视觉图表解析与跨模态推理的任务、企业级 RAG 检索增强生成、自动化智能体工作流与高可靠 API 集成。)各具优势:Gemini 2.0 Flash-Lite 在综合智能与编程基准测试中表现更强,Gemini 2.0 Flash-Lite 拥有更快的 Token 生成速度,而 Amazon Nova Lite 则在规模化 API 部署中具备更高的成本效益。

常见问题 (FAQ)

在编程和深度推理方面,Gemini 2.0 Flash-Lite 和 Amazon Nova Lite 哪个更强?+

在综合智能指数与编程基准评测中,Gemini 2.0 Flash-Lite 表现占优。建议结合上方的 SWE-bench Verified 与 GPQA Diamond 跑分根据具体场景选择。

Gemini 2.0 Flash-Lite 与 Amazon Nova Lite 的 API 价格哪个更便宜?+

Gemini 2.0 Flash-Lite 的 API 价格为输入 $0.07 / 输出 $0.30(每百万 Token),Amazon Nova Lite 为输入 $0.06 / 输出 $0.24。综合混合成本来看,Amazon Nova Lite 更具性价比。

哪款模型拥有更大的上下文窗口和更快的响应速度?+

Gemini 2.0 Flash-Lite 支持 1.05M Token 上下文窗口,而 Amazon Nova Lite 支持 300K Token。在流式生成速度方面,Gemini 2.0 Flash-Lite 的吞吐表现更快。

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

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.

数据来源: Google 与 Amazon 官方 API 文档、模型发布说明及独立基准测试报告 (2026)。