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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 3.8 FlashGoogleRemove
  2. GLM 5.3Z.AIRemove
  3. Gemini 2.5 Flash Preview 09-2025GoogleRemove
gemini-3.8-flash vs glm-5.3 vs gemini-2.5-flash-preview-09-2025
AttributeGemini 3.8 Flashgemini-3.8-flashGLM 5.3glm-5.3Gemini 2.5 Flash Preview 09-2025gemini-2.5-flash-preview-09-2025
Pricing
Input$0.75 / 1M$1.40 / 1M$0.15 / 1M
Output$3.75 / 1M$4.40 / 1M$1.25 / 1M
Cache Write (5m)$0.75 / 1M$1.40 / 1M$0.15 / 1M
Cache Write (1h)$0.75 / 1M$1.40 / 1M$0.15 / 1M
Cache Read$0.75 / 1M$1.40 / 1M$0.15 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1.0M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleZ.AIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 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.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.Gemini 2.5 Flash Preview (Sept 2025) is Google's high-performance workhorse model built for advanced reasoning, coding, math, and scientific tasks. With built-in “thinking” capabilities, it delivers more accurate, context-aware answers across complex problems.