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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 3.5 FlashGoogleRemove
  3. GPT-6 Astra ProOpenAIRemove
glm-5.3 vs gemini-3.5-flash vs gpt-6-astra-pro
AttributeGLM 5.3glm-5.3Gemini 3.5 Flashgemini-3.5-flashGPT-6 Astra Progpt-6-astra-pro
Pricing
Input$1.40 / 1M$1.50 / 1M$10.00 / 1M
Output$4.40 / 1M$9.00 / 1M$50.00 / 1M
Cache Write (5m)$1.40 / 1M$1.50 / 1M$10.00 / 1M
Cache Write (1h)$1.40 / 1M$1.50 / 1M$10.00 / 1M
Cache Read$1.40 / 1M$1.50 / 1M$10.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
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 3.5 Flash is Google's high-efficiency multimodal model, delivering near-Pro level performance in coding and reasoning at Flash-tier speed and cost. It supports text, image, video, audio, and PDF inputs, making it well suited for diverse multimodal workflows. Optimized for coding proficiency and parallel agentic execution, the model defaults to medium thinking effort for faster, cost-efficient responses while supporting configurable thinking levels (minimal, low, medium, high) for fine-grained cost–performance control.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.