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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 Flash Preview 09-2025GoogleRemove
  2. GLM 5.3Z.AIRemove
  3. GLM 5.3 FlashZ.AIRemove
gemini-2.5-flash-preview-09-2025 vs glm-5.3 vs glm-5.3-flash
AttributeGemini 2.5 Flash Preview 09-2025gemini-2.5-flash-preview-09-2025GLM 5.3glm-5.3GLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.15 / 1M$1.40 / 1M$0.075 / 1M
Output$1.25 / 1M$4.40 / 1M$0.25 / 1M
Cache Write (5m)$0.15 / 1M$1.40 / 1M$0.075 / 1M
Cache Write (1h)$0.15 / 1M$1.40 / 1M$0.075 / 1M
Cache Read$0.15 / 1M$1.40 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1.0M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderGoogleZ.AIZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGemini 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.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.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.