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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. GLM 4.7 FlashZ.AIRemove
  2. GLM 5.3Z.AIRemove
  3. GPT-6 Astra ProOpenAIRemove
glm-4.7-flash vs glm-5.3 vs gpt-6-astra-pro
AttributeGLM 4.7 Flashglm-4.7-flashGLM 5.3glm-5.3GPT-6 Astra Progpt-6-astra-pro
Pricing
Input$0.06 / 1M$1.40 / 1M$10.00 / 1M
Output$0.40 / 1M$4.40 / 1M$50.00 / 1M
Cache Write (5m)$0.06 / 1M$1.40 / 1M$10.00 / 1M
Cache Write (1h)$0.06 / 1M$1.40 / 1M$10.00 / 1M
Cache Read$0.06 / 1M$1.40 / 1M$10.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context200K1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIZ.AIOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGLM-4.7-Flash is a state-of-the-art 30B-class model designed to strike a strong balance between performance and efficiency. It is specifically optimized for agentic coding scenarios, with enhanced capabilities in code generation, long-horizon task planning, and tool-based collaboration. Among open-source models of comparable size, GLM-4.7-Flash has achieved leading results on multiple public benchmark leaderboards, establishing itself as a competitive and practical choice for advanced developer and agent workflows.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.GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.