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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. DeepSeek V4.1 FlashDeepSeekRemove
  2. Kimi K2 0711 Preview SearchMoonshot AIRemove
  3. GLM 5.3 FlashZ.AIRemove
deepseek-v4.1-flash vs kimi-k2-0711-preview-search vs glm-5.3-flash
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashKimi K2 0711 Preview Searchkimi-k2-0711-preview-searchGLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.30 / 1M$0.165 / 1M$0.075 / 1M
Output$1.20 / 1M$0.49 / 1M$0.25 / 1M
Cache Write (5m)$0.30 / 1M$0.165 / 1M$0.075 / 1M
Cache Write (1h)$0.30 / 1M$0.165 / 1M$0.075 / 1M
Cache Read$0.30 / 1M$0.165 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M63K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekMoonshot AIZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryDeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.Kimi 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.