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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 V3.2 SpecialeDeepSeekRemove
  2. GLM 5.3Z.AIRemove
  3. GLM 5.3 FlashZ.AIRemove
deepseek-v3.2-speciale vs glm-5.3 vs glm-5.3-flash
AttributeDeepSeek V3.2 Specialedeepseek-v3.2-specialeGLM 5.3glm-5.3GLM 5.3 Flashglm-5.3-flash
Pricing
Input$0.28 / 1M$1.40 / 1M$0.075 / 1M
Output$0.40 / 1M$4.40 / 1M$0.25 / 1M
Cache Write (5m)$0.28 / 1M$1.40 / 1M$0.075 / 1M
Cache Write (1h)$0.28 / 1M$1.40 / 1M$0.075 / 1M
Cache Read$0.28 / 1M$1.40 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context163.8K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekZ.AIZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryDeepSeek-V3.2-Speciale is a high-compute edition of V3.2 built for top-tier reasoning and agent performance. Using DeepSeek Sparse Attention and extensive reinforcement learning, it surpasses GPT-5 on tough reasoning benchmarks and approaches Gemini 3 Pro–level capability, while still remaining strong at coding and tool use. It also draws on a large agent-training pipeline to boost reliability and generalization in interactive environments.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.