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Compare models

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. CommandCohereRemove
  3. GLM 5.3 FlashZ.AIRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

gemini-3.8-flash vs command vs glm-5.3-flash vs glm-5.3
AttributeGemini 3.8 Flashgemini-3.8-flashCommandcommandGLM 5.3 Flashglm-5.3-flashGLM 5.3glm-5.3
Pricing
Input$0.75 / 1M$0.95 / 1M$0.075 / 1M$1.40 / 1M
Output$3.75 / 1M$1.90 / 1M$0.25 / 1M$4.40 / 1M
Cache Write (5m)$0.75 / 1M$0.95 / 1M$0.075 / 1M$1.40 / 1M
Cache Write (1h)$0.75 / 1M$0.95 / 1M$0.075 / 1M$1.40 / 1M
Cache Read$0.75 / 1M$0.95 / 1M$0.075 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M4.1K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesNo
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderGoogleCohereZ.AIZ.AI
Categorychatchatchatchat
Charge typePay As You GoPay 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-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.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.