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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. GLM 5.3Z.AIRemove
  3. Kimi K2 0711 Preview SearchMoonshot AIRemove
gpt-6-astra vs glm-5.3 vs kimi-k2-0711-preview-search
AttributeGPT-6 Astragpt-6-astraGLM 5.3glm-5.3Kimi K2 0711 Preview Searchkimi-k2-0711-preview-search
Pricing
Input$10.00 / 1M$1.40 / 1M$0.165 / 1M
Output$50.00 / 1M$4.40 / 1M$0.49 / 1M
Cache Write (5m)$10.00 / 1M$1.40 / 1M$0.165 / 1M
Cache Write (1h)$10.00 / 1M$1.40 / 1M$0.165 / 1M
Cache Read$10.00 / 1M$1.40 / 1M$0.165 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M63K
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderOpenAIZ.AIMoonshot 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.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.Kimi K2 Instruct is a trillion-parameter MoE model from Moonshot AI, with 32B active parameters per step. Built for strong agentic behavior, it excels at tool use, reasoning, and code generation, leading major benchmarks in coding, logic, and tool-use tasks. It supports up to 128K context and uses a specialized training setup (including MuonClip) to stabilize very large MoE training.