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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Gemini 2.5 FlashGoogleRemove
  2. Gemini 3.7 FlashGoogleRemove
  3. Gemini 3.8 FlashGoogleRemove
gemini-2.5-flash vs gemini-3.7-flash vs gemini-3.8-flash
AttributeGemini 2.5 Flashgemini-2.5-flashGemini 3.7 Flashgemini-3.7-flashGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.075 / 1M$0.375 / 1M$0.75 / 1M
Output$0.625 / 1M$1.88 / 1M$3.75 / 1M
Cache Write (5m)$0.075 / 1M$0.375 / 1M$0.75 / 1M
Cache Write (1h)$0.075 / 1M$0.375 / 1M$0.75 / 1M
Cache Read$0.075 / 1M$0.375 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.0M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleGoogleGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 2.5 Flash is Google's main high-performance model for complex reasoning, coding, math, and scientific tasks. It has built-in “thinking” features that help it produce more accurate, context-aware answers.Gemini 3.7 Flash is Google's fast multimodal model designed for agentic workflows, coding, and complex multi-step reasoning. It combines responsive inference with reliable problem-solving capabilities, making it well suited for interactive and production-scale applications. Optimized for speed and dependable multi-step execution, Gemini 3.7 Flash is a strong choice for coding assistants, autonomous agents, and high-throughput workflows that require both low latency and capable reasoning.Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.