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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. GLM 5.3Z.AIRemove
  3. Claude Opus 4.5AnthropicRemove
hy4-preview vs glm-5.3 vs claude-opus-4-5-20251101
AttributeHy4 previewhy4-previewGLM 5.3glm-5.3Claude Opus 4.5claude-opus-4-5-20251101
Pricing
Input$0.834 / 1M$1.40 / 1M$4.00 / 1M
Output$2.50 / 1M$4.40 / 1M$20.00 / 1M
Cache Write (5m)$0.834 / 1M$1.40 / 1M$5.00 / 1M
Cache Write (1h)$0.834 / 1M$1.40 / 1M$8.00 / 1M
Cache Read$0.834 / 1M$1.40 / 1M$0.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M200K
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderTencentZ.AIAnthropic
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.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.Claude Opus 4.5 is Anthropic's frontier reasoning model, built for complex engineering, agent workflows, and long computer-use tasks. It offers strong multimodal skills, better security against prompt injection, and flexible effort controls — including a Verbosity setting to trade speed vs. depth and token use. With advanced tool use, long-context handling, and support for coordinated multi-agent setups, it excels at research, debugging, multi-step planning, and UI/spreadsheet automation while improving reliability, alignment, and efficiency over earlier Opus versions.