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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. GPT-6 AstraOpenAIRemove
  2. Gemini 2.5 Flash LiteGoogleRemove
  3. GLM 5.3 FlashZ.AIRemove
gpt-6-astra vs gemini-2.5-flash-lite vs glm-5.3-flash
AttributeGPT-6 Astragpt-6-astraGemini 2.5 Flash Litegemini-2.5-flash-liteGLM 5.3 Flashglm-5.3-flash
Pricing
Input$10.00 / 1M$0.15 / 1M$0.075 / 1M
Output$50.00 / 1M$0.60 / 1M$0.25 / 1M
Cache Write (5m)$10.00 / 1M$0.15 / 1M$0.075 / 1M
Cache Write (1h)$10.00 / 1M$0.15 / 1M$0.075 / 1M
Cache Read$10.00 / 1M$0.15 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1.0M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIGoogleZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryGPT-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.Gemini 2.5 Flash-Lite is a smaller, low-latency model focused on speed and cost efficiency. It delivers faster generation and better benchmark performance than earlier Flash models. Thinking mode is off by default for maximum speed, but developers can enable it when they want deeper reasoning at a higher cost.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.