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Head-to-Head Model Comparison

Gemini 2.5 Flash vs Gemini 2.5 Flash-Lite

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini 2.5 Flash vs Gemini 2.5 Flash-Lite: Benchmarks, Pricing & Speed Comparison

Gemini 2.5 Flash by Google and Gemini 2.5 Flash-Lite by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini 2.5 Flash-Lite holds a notable pricing advantage for token ingestion. On output generation, Gemini 2.5 Flash-Lite is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 1,049K tokens. In latency-sensitive production environments, Gemini 2.5 Flash-Lite leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.

Perbandingan Langsung (VS)

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Google

Gemini 2.5 Flash

Model Penalaran Mendalam

Gemini 2.5 Flash dari Google adalah model AI canggih dengan jendela konteks 1.0M token serta harga API $0.3/1M token input dan $2.5/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.

Intelligence Index68 / 100
Input Token:

$0.30 / 1M

Output Token:

$2.50 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

58.2%

Google

Gemini 2.5 Flash-Lite

Model Penalaran Mendalam

Gemini 2.5 Flash-Lite dari Google adalah model AI canggih dengan jendela konteks 1.0M token serta harga API $0.1/1M token input dan $0.4/1M token output untuk pemrograman, penalaran, dan otomasi bisnis.

Intelligence Index56 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.40 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

  • Gemini 2.5 Flash-Lite is 5.1x cheaper for API usage (input + output tokens combined).
  • Gemini 2.5 Flash-Lite is faster at 220 tok/s vs 160 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 2.5 FlashGemini 2.5 Flash-Lite
Intelligence Index
Composite reasoning & knowledge score (0-100)
6856
Provider
GoogleGoogle
Input Price
Cost per million input tokens
$0.30 / 1M$0.10 / 1M
Output Price
Cost per million output tokens
$2.50 / 1M$0.40 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.36 / 1M$0.08 / 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
66K33K
Throughput Speed
Tokens generated per second
160 tok/s220 tok/s
SWE-bench Verified
Real-world software engineering
58.2%N/A
GPQA Diamond
Graduate-level reasoning
75.4%N/A
Cached Input Price
Prompt caching discount per 1M tokens
$0.07 / 1M$0.03 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
Gemini 2.5 Flash-Lite
5.1x cheaper monthly runtime
Speed
Gemini 2.5 Flash-Lite
+38% faster generation
Context
Tied
1.05M tokens window

Skor Benchmark Terverifikasi

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

Gemini 2.5 FlashGemini 2.5 Flash-Lite
SWE-bench Verified (Autonomous Coding)

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

58.2%vsN/A
GPQA Diamond (Doctorate-Level Science & Math)

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

75.4%vsN/A
MMLU-Pro (Multi-task Language Understanding)

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

83.2%vs76.5%
LMSYS Chatbot Arena Elo

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

1385ptsvs1330pts
AIME (Advanced Math Competition)

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

84.5%vsN/A
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs220 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 2.5 Flash0.45s
Gemini 2.5 Flash-Lite0.32s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 2.5 Flash3.58s
Gemini 2.5 Flash-Lite2.59s

Simulator Biaya Token Interaktif

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 2.5 Flash Monthly API Bill
$2.81 / month
$0.3/1M in · $2.5/1M out
Gemini 2.5 Flash-Lite Monthly API Bill
$0.55 / month
$0.1/1M in · $0.4/1M out
Gemini 2.5 Flash-Lite saves approximately $2.26 / month (81%) under this workload.5.1x Savings

Gemini 2.5 Flash · Strengths & Key Advantages

  • Arsitektur High-throughput sparse multimodal transformer with thinking mode mutakhir dari Google dengan dukungan konteks 1.0M token
  • Efisiensi biaya API yang kompetitif ($0.3/1M input, $2.5/1M output) dan respons cepat
  • Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
  • Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
  • Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik

Gemini 2.5 Flash-Lite · Strengths & Key Advantages

  • Arsitektur Compact multimodal transformer with lightweight thinking mutakhir dari Google dengan dukungan konteks 1.0M token
  • Efisiensi biaya API yang kompetitif ($0.1/1M input, $0.4/1M output) dan respons cepat
  • Performa andal dalam pembuatan kode, analisis logika, serta pemahaman multibahasa
Considerations & Trade-offs
  • Penggunaan konteks berukuran penuh memerlukan pengelolaan anggaran token yang cermat
  • Membutuhkan perancangan prompt yang tepat untuk kasus penggunaan industri spesifik

Token Use & Task Economics

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

MetricGemini 2.5 FlashGemini 2.5 Flash-Lite
Cost per Task
Estimated USD to finish one task
$0.85$0.35
Output Tokens / Task
Tokens generated per task
32K12K
Reasoning Tokens / Task
Thinking tokens burned per task
14K4K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 2.5 FlashGemini 2.5 Flash-Lite
Official API Model ID (SDK)models/gemini-2-5-flashmodels/gemini-2-5-flash-lite
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKGoogle Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2025-062025-06
Knowledge CutoffMarch 2025March 2025
ArchitectureHigh-throughput sparse multimodal transformer with thinking modeCompact multimodal transformer with lightweight thinking
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 $0.075/1M with configurable TTLImplicit + Explicit Context Caching at $0.025/1M 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)
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
Output Modalitiestexttext

Gemini 2.5 Flash vs Gemini 2.5 Flash-Lite: Jawaban Cepat (FAQ)

Which is more intelligent, Gemini 2.5 Flash or Gemini 2.5 Flash-Lite?

Gemini 2.5 Flash is more intelligent. It scores 68 compared with 56 on the composite Intelligence Index.

Which model is faster?

Gemini 2.5 Flash-Lite is faster, generating 220 tokens per second compared with 160 tokens per second.

Which model is cheaper to run?

Gemini 2.5 Flash-Lite is cheaper at $0.08 per 1M tokens blended, compared with $0.36 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 2.5 Flash-Lite has lower latency, with a time to first token of 0.32s compared with 0.45s.

Kesimpulan

Verdict & Model Selection Recommendation: Choose Gemini 2.5 Flash if your priority is Production web applications, interactive agents, and multimodal media indexing. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 2.5 Flash-Lite if your workflow prioritizes Bulk document extraction, customer service routing, and high-volume parsing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

Pertanyaan Umum (FAQ)

How does Gemini 2.5 Flash compare to Gemini 2.5 Flash-Lite in API token pricing and cost per token?+

Gemini 2.5 Flash costs $0.30 per 1M input tokens and $2.50 per 1M output tokens, whereas Gemini 2.5 Flash-Lite costs $0.10 input and $0.40 output. Overall, Gemini 2.5 Flash-Lite is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.

Which model has a larger context window: Gemini 2.5 Flash or Gemini 2.5 Flash-Lite?+

Both Gemini 2.5 Flash and Gemini 2.5 Flash-Lite offer an identical context window of 1,049K tokens (~786k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.

Which AI model is better for software engineering and coding: Gemini 2.5 Flash or Gemini 2.5 Flash-Lite?+

Gemini 2.5 Flash scores 58.2% on SWE-bench Verified, while Gemini 2.5 Flash-Lite scores competitively for software generation. For complex autonomous debugging and agentic engineering, Gemini 2.5 Flash provides robust code generation and tool calling.

What is the difference in reasoning capability and extended thinking between Gemini 2.5 Flash and Gemini 2.5 Flash-Lite?+

Gemini 2.5 Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 2.5 Flash-Lite provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between Gemini 2.5 Flash and Gemini 2.5 Flash-Lite?+

Gemini 2.5 Flash generates approximately 160 tokens per second, while Gemini 2.5 Flash-Lite delivers 220 tokens per second. In latency-sensitive production environments, Gemini 2.5 Flash-Lite provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining Gemini 2.5 Flash and Gemini 2.5 Flash-Lite to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 2.5 Flash-Lite to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 2.5 Flash. This tiered routing pattern cuts overall API bills by up to 70%.

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.

Sumber resmi: GetInfoUs Independent AI Benchmarks & Official API Specs