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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. GLM 5.3 FlashZ.AIRemove
  2. Qwen3 32BAlibabaRemove
  3. GPT-6 AstraOpenAIRemove
  4. GLM 5.3Z.AIRemove

4 is the maximum. Remove one to add another.

glm-5.3-flash vs qwen3-32b vs gpt-6-astra vs glm-5.3
AttributeGLM 5.3 Flashglm-5.3-flashQwen3 32Bqwen3-32bGPT-6 Astragpt-6-astraGLM 5.3glm-5.3
Pricing
Input$0.075 / 1M$0.10 / 1M$10.00 / 1M$1.40 / 1M
Output$0.25 / 1M$0.30 / 1M$50.00 / 1M$4.40 / 1M
Cache Write (5m)$0.075 / 1M$0.10 / 1M$10.00 / 1M$1.40 / 1M
Cache Write (1h)$0.075 / 1M$0.10 / 1M$10.00 / 1M$1.40 / 1M
Cache Read$0.075 / 1M$0.10 / 1M$10.00 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M41.0K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesNo
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderZ.AIAlibabaOpenAIZ.AI
Categorychatchatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released
Description
SummaryGLM-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.GPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.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.