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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. Kimi K2 0711 Preview SearchMoonshot AIRemove
  2. GLM 5.3 FlashZ.AIRemove
  3. Gemini 3.8 FlashGoogleRemove
kimi-k2-0711-preview-search vs glm-5.3-flash vs gemini-3.8-flash
AttributeKimi K2 0711 Preview Searchkimi-k2-0711-preview-searchGLM 5.3 Flashglm-5.3-flashGemini 3.8 Flashgemini-3.8-flash
Pricing
Input$0.165 / 1M$0.075 / 1M$0.75 / 1M
Output$0.49 / 1M$0.25 / 1M$3.75 / 1M
Cache Write (5m)$0.165 / 1M$0.075 / 1M$0.75 / 1M
Cache Write (1h)$0.165 / 1M$0.075 / 1M$0.75 / 1M
Cache Read$0.165 / 1M$0.075 / 1M$0.75 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context63K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderMoonshot AIZ.AIGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryKimi K2 Instruct is a trillion-parameter MoE model from Moonshot AI, with 32B active parameters per step. Built for strong agentic behavior, it excels at tool use, reasoning, and code generation, leading major benchmarks in coding, logic, and tool-use tasks. It supports up to 128K context and uses a specialized training setup (including MuonClip) to stabilize very large MoE training.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.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.