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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. Hy4 previewTencentRemove
  2. Claude Fable 5.1AnthropicRemove
  3. Kimi K2 0711 Preview SearchMoonshot AIRemove
hy4-preview vs claude-fable-5.1 vs kimi-k2-0711-preview-search
AttributeHy4 previewhy4-previewClaude Fable 5.1claude-fable-5.1Kimi K2 0711 Preview Searchkimi-k2-0711-preview-search
Pricing
Input$0.834 / 1M$10.00 / 1M$0.165 / 1M
Output$2.50 / 1M$50.00 / 1M$0.49 / 1M
Cache Write (5m)$0.834 / 1M$12.50 / 1M$0.165 / 1M
Cache Write (1h)$0.834 / 1M$20.00 / 1M$0.165 / 1M
Cache Read$0.834 / 1M$1.00 / 1M$0.165 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M63K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderTencentAnthropicMoonshot AI
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryTencent Hy4 Preview is a Mixture-of-Experts (MoE) model from Tencent, featuring 770B total parameters with 49B activated per token. It is designed for coding agents, complex tool-driven workflows, and professional productivity tasks that require strong planning and reliable execution. Optimized for context continuity and sustained multi-step work, Hy4 Preview is well suited for long-horizon coding, agentic automation, tool orchestration, and complex real-world workflows.Claude 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.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.