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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. Grok 4 FastxAIRemove
  3. GLM 5.3 FlashZ.AIRemove
gpt-6-astra vs grok-4-fast vs glm-5.3-flash
AttributeGPT-6 Astragpt-6-astraGrok 4 Fastgrok-4-fastGLM 5.3 Flashglm-5.3-flash
Pricing
Input$10.00 / 1M$0.07 / 1M$0.075 / 1M
Output$50.00 / 1M$0.175 / 1M$0.25 / 1M
Cache Write (5m)$10.00 / 1M$0.07 / 1M$0.075 / 1M
Cache Write (1h)$10.00 / 1M$0.07 / 1M$0.075 / 1M
Cache Read$10.00 / 1M$0.07 / 1M$0.075 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M2M1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIxAIZ.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.Grok 4 Fast is xAI's cost-efficient multimodal model with a massive 2M-token context window. It’s available in both reasoning and non-reasoning modes, allowing developers to toggle deeper thinking when needed. Designed for scalable performance, it balances speed, capability, and price — with reasoning controllable via the reasoning_enabled API parameter.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.