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.
- mai-ds-r1:freeNot in the catalogueRemove
- Hy4 previewTencentRemove
- Gemini 3.5 TranscribeGoogleRemove
- Muse WonderMetaRemove
4 is the maximum. Remove one to add another.
| Attribute | mai-ds-r1:freeNot in the catalogue | Hy4 previewhy4-preview | Gemini 3.5 Transcribegemini-3.5-transcribe | Muse Wondermuse-wonder |
|---|---|---|---|---|
| Pricing | ||||
| Input | — | $0.834 / 1M | — Not priced per input token | $4.00 / 1M |
| Output | — | $2.50 / 1M | — Not priced per output token | $20.00 / 1M |
| Cache Write (5m) | — | $0.834 / 1M | Not applicable | $4.00 / 1M |
| Cache Write (1h) | — | $0.834 / 1M | Not applicable | $4.00 / 1M |
| Cache Read | — | $0.834 / 1M | Not applicable | $4.00 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | — | 1M | 98.3K | 1M |
| Max output | — | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | — | Yes | Yes | Yes |
| Function Calling | — | Yes | Yes | Yes |
| JSON Mode | — | Yes | No | Yes |
| Streaming | — | Yes | No | Yes |
| Catalogue | ||||
| Provider | — | Tencent | Meta | |
| Category | — | chat | voice | chat |
| Charge type | — | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | 2026-09-25 | — |
| 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. | Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token. | Muse Wonder is Meta’s frontier multimodal reasoning model, designed for complex personal and professional agentic workflows. It supports text, image, video, audio, and PDF inputs with text output and features a 1M-token context window for sustained reasoning across long-running tasks. Optimized for autonomous execution and multi-agent orchestration, Muse Wonder can plan, delegate to subagents, coordinate parallel tool use, recover from errors, and operate browsers and applications across desktop and mobile environments. With cross-conversation context retrieval and its Active Focus training approach, it is well suited for complex software engineering, multi-file refactoring, computer use, and end-to-end workflows requiring persistence, adaptability, and minimal supervision. |