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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 Pro DeepSearchGoogleRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. GLM 5.3 FlashZ.AIRemove
gemini-2.5-pro-deepsearch vs gemini-3.8-flash vs glm-5.3-flash
AttributeGemini 2.5 Pro DeepSearchgemini-2.5-pro-deepsearchGemini 3.8 Flashgemini-3.8-flashGLM 5.3 Flashglm-5.3-flash
Pricing
Input$7.00 / 1M$0.75 / 1M$0.075 / 1M
Output$56.00 / 1M$3.75 / 1M$0.25 / 1M
Cache Write (5m)$7.00 / 1M$0.75 / 1M$0.075 / 1M
Cache Write (1h)$7.00 / 1M$0.75 / 1M$0.075 / 1M
Cache Read$7.00 / 1M$0.75 / 1M$0.075 / 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
ProviderGoogleGoogleZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 2.5 Pro is Google's top reasoning model for coding, math, and scientific work. It uses built-in “thinking” to deliver more accurate, context-aware answers and ranks at the top of major benchmarks like LMArena, showing strong alignment and problem-solving ability.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.GLM-5.3-Flash is Z.AI's efficient native multimodal model, designed for coding and long-horizon agentic workflows. It combines strong multimodal capabilities with an architecture optimized for responsive, cost-efficient task execution. Built on a hybrid sparse and linear attention architecture, GLM-5.3-Flash maintains accurate long-context behavior while reducing computational overhead, making it well suited for coding agents, extended multi-step tasks, and scalable production workloads.