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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 5.3Z.AIRemove
  2. Gemini 3.8 FlashGoogleRemove
  3. GPT-5.1-Codex-MaxOpenAIRemove
glm-5.3 vs gemini-3.8-flash vs gpt-5.1-codex-max
AttributeGLM 5.3glm-5.3Gemini 3.8 Flashgemini-3.8-flashGPT-5.1-Codex-Maxgpt-5.1-codex-max
Pricing
Input$1.40 / 1M$0.75 / 1M$0.375 / 1M
Output$4.40 / 1M$3.75 / 1M$3.00 / 1M
Cache Write (5m)$1.40 / 1M$0.75 / 1M$0.375 / 1M
Cache Write (1h)$1.40 / 1M$0.75 / 1M$0.375 / 1M
Cache Read$1.40 / 1M$0.75 / 1M$0.375 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M400K
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderZ.AIGoogleOpenAI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGLM-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.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.GPT-5.1 Codex Max is OpenAI's advanced agentic coding model, built for long-running, high-context development work. Using an upgraded 5.1 reasoning stack and training on real engineering workflows, it delivers faster performance, stronger reasoning, and better token efficiency across the full software lifecycle.