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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. Claude Fable 5.1AnthropicRemove
  2. GLM 5.3Z.AIRemove
  3. GLM 4.7 FlashZ.AIRemove
claude-fable-5.1 vs glm-5.3 vs glm-4.7-flash
AttributeClaude Fable 5.1claude-fable-5.1GLM 5.3glm-5.3GLM 4.7 Flashglm-4.7-flash
Pricing
Input$10.00 / 1M$1.40 / 1M$0.06 / 1M
Output$50.00 / 1M$4.40 / 1M$0.40 / 1M
Cache Write (5m)$12.50 / 1M$1.40 / 1M$0.06 / 1M
Cache Write (1h)$20.00 / 1M$1.40 / 1M$0.06 / 1M
Cache Read$1.00 / 1M$1.40 / 1M$0.06 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M200K
Max outputN/AN/AN/A
Capabilities
VisionYesNoNo
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAnthropicZ.AIZ.AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryClaude Fable 5.1 is an upgraded version of Fable 5, delivering broad improvements with particularly strong gains in agentic coding, long-running workflows, and professional knowledge work. It excels at large code refactors, front-end and visual code generation, financial analysis, and complex analytical tasks. Compared with Fable 5, it also produces more concise plans and summaries while maintaining strong performance across extended tasks, making it a natural upgrade for existing Fable workflows and a strong option alongside Opus 5 for reasoning-intensive applications.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.GLM-4.7-Flash is a state-of-the-art 30B-class model designed to strike a strong balance between performance and efficiency. It is specifically optimized for agentic coding scenarios, with enhanced capabilities in code generation, long-horizon task planning, and tool-based collaboration. Among open-source models of comparable size, GLM-4.7-Flash has achieved leading results on multiple public benchmark leaderboards, establishing itself as a competitive and practical choice for advanced developer and agent workflows.