Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Gemini 2.0 Flash-LiteProfile

Gemini 1.5 Flash-8BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Google

Gemini 2.0 Flash-Lite

Model Ringan & Hemat Biaya

Gemini 2.0 Flash-Lite dari Google adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 1.0M, kecepatan 280 token/detik, indeks kecerdasan 48/100, serta

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

AI Multimodal Asli

Gemini 1.5 Flash-8B dari Google adalah model AI yang dirancang untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, menghadirkan jendela konteks 1.0M, kecepatan 160 token/detik, indeks kecerdasan 37/100, serta ta

Intelligence Index37 / 100

Input Token:

$0.04 / 1M

Output Token:

$0.15 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

24.5%

Perbedaan Utama Sekilas

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 →

Feature	Gemini 2.0 Flash-Lite	Gemini 1.5 Flash-8B
Intelligence Index Composite reasoning & knowledge score (0-100)	48	37
Provider	Google	Google
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 text	1.05M tokens~1,573 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	280 tok/s	160 tok/s
SWE-bench Verified Real-world software engineering	N/A	24.5%
GPQA Diamond Graduate-level reasoning	N/A	38.2%
Cached Input Price Prompt caching discount per 1M tokens	$0.02 / 1M	$0.01 / 1M

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% 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

Jendela konteks 1.0M dengan kapasitas output maksimum hingga 8K token (MMLU-Pro 72.5% · Arena Elo 1305)
Kecepatan generasi tinggi 280 token/detik dengan latensi token pertama (TTFT) hanya 290 ms
Skema harga API kompetitif (tarif $0.075/$0.3 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Gemini 1.5 Flash-8B · Strengths & Key Advantages

Jendela konteks 1.0M dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 24.5% · GPQA Diamond 38.2%)
Kecepatan generasi tinggi 160 token/detik dengan latensi token pertama (TTFT) hanya 250 ms
Skema harga API kompetitif (tarif $0.0375/$0.15 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Token Use & Task Economics

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

Metric	Gemini 2.0 Flash-Lite	Gemini 1.5 Flash-8B
Cost per Task Estimated USD to finish one task	$0.25	$0.04
Output Tokens / Task Tokens generated per task	6K	18K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Gemini 2.0 Flash-Lite vs Gemini 1.5 Flash-8B: Jawaban Cepat (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.

Kesimpulan

Baik Gemini 2.0 Flash-Lite (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Gemini 1.5 Flash-8B (Sangat cocok untuk pemahaman multimodal asli (teks & visual) serta analisis dokumen kompleks, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Gemini 2.0 Flash-Lite unggul dalam penalaran dan pemrograman, Gemini 2.0 Flash-Lite memberikan kecepatan generasi token lebih tinggi, dan Gemini 1.5 Flash-8B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Gemini 2.0 Flash-Lite atau Gemini 1.5 Flash-8B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Gemini 2.0 Flash-Lite memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Gemini 2.0 Flash-Lite vs Gemini 1.5 Flash-8B?+

Gemini 2.0 Flash-Lite mengenakan biaya $0.07 / $0.30 per 1M token input/output, sedangkan Gemini 1.5 Flash-8B sebesar $0.04 / $0.15. Secara keseluruhan, Gemini 1.5 Flash-8B lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Gemini 2.0 Flash-Lite mendukung konteks 1.05M token dibandingkan 1.05M token pada Gemini 1.5 Flash-8B, sementara Gemini 2.0 Flash-Lite unggul dalam kecepatan output token per detik.

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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Feature	Gemini 2.0 Flash-Lite	Gemini 1.5 Flash-8B
Official API Model ID (SDK)	models/gemini-2-0-flash-lite	models/gemini-1-5-flash-8b
API Protocol	Google Gemini API (generateContent) + OpenAI-Compatible SDK	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2025-02	2024-10
Knowledge Cutoff	August 2024	May 2024
Architecture	Distilled lightweight multimodal transformer	High-Throughput Multimodal Transformer
Parameters	Confidential	8B
Enterprise Rate Limits (RPM / TPM)	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM	10,000 – 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 tokens	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Implicit + Explicit Context Caching at $0.01875/1M with configurable TTL	Implicit + Explicit Context Caching at $0.01/1M with configurable TTL
Computer Use / GUI Agent	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool Calling	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema	Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & Sandbox	Built-in Google Search Grounding + Managed Python Code Execution Sandbox	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	Vertex AI Supervised Adapter Tuning & Context Distillation	Vertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token Billing	Text 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 VRAM	Cloud-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 Classification	Google DeepMind Frontier Safety Framework Verified	Google DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision, audio, video	text, vision, audio, video
Output Modalities	text	text

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

Ganti & Bandingkan Model Apa Saja

Gemini 2.0 Flash-Lite

Gemini 1.5 Flash-8B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Gemini 2.0 Flash-Lite · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 1.5 Flash-8B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Gemini 2.0 Flash-Lite vs Gemini 1.5 Flash-8B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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