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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. MiniMax M3MiniMaxRemove
  2. GLM 5.3Z.AIRemove
minimax-m3 vs glm-5.3
AttributeMiniMax M3minimax-m3GLM 5.3glm-5.3
Pricing
Input$0.30 / 1M$1.40 / 1M
Output$1.20 / 1M$4.40 / 1M
Cache Write (5m)$0.30 / 1M$1.40 / 1M
Cache Write (1h)$0.30 / 1M$1.40 / 1M
Cache Read$0.30 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M1M
Max outputN/AN/A
Capabilities
VisionYesNo
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderMiniMaxZ.AI
Categorychatchat
Charge typePay As You GoPay As You Go
Released——
Description
SummaryMiniMax-M3 is a multimodal foundation model from MiniMax, supporting text, image, and video inputs with text output and a 1M-token context window. It is designed for long-horizon agentic workflows, coding, and tool-driven task execution, enabling sustained reasoning across complex tasks. Built on MiniMax Sparse Attention (MSA), the model dramatically improves long-context efficiency by replacing full attention with KV-block selection, reducing compute costs at 1M-token contexts while maintaining strong performance. Trained as a native multimodal model and optimized for multi-turn, production-style collaboration, MiniMax-M3 excels at extended, multi-step workflows rather than single-turn interactions.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.