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.
| Attribute | Hy4 previewhy4-preview | Gemini 3.8 Flashgemini-3.8-flash | GPT-5.1-Codex-Maxgpt-5.1-codex-max |
|---|---|---|---|
| Pricing | |||
| Input | $0.834 / 1M | $0.75 / 1M | $0.375 / 1M |
| Output | $2.50 / 1M | $3.75 / 1M | $3.00 / 1M |
| Cache Write (5m) | $0.834 / 1M | $0.75 / 1M | $0.375 / 1M |
| Cache Write (1h) | $0.834 / 1M | $0.75 / 1M | $0.375 / 1M |
| Cache Read | $0.834 / 1M | $0.75 / 1M | $0.375 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 400K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Tencent | OpenAI | |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Tencent 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. | Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality. | GPT-5.1 Codex Max is OpenAI's advanced agentic coding model, built for long-running, high-context development work. Using an upgraded 5.1 reasoning stack and training on real engineering workflows, it delivers faster performance, stronger reasoning, and better token efficiency across the full software lifecycle. |