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 | Muse Wondermuse-wonder | GLM 5.3glm-5.3 | Universal-3.5 Prouniversal-3-5-pro |
|---|---|---|---|
| Pricing | |||
| Input | $4.00 / 1M | $1.40 / 1M | — Not priced per input token |
| Output | $20.00 / 1M | $4.40 / 1M | — Not priced per output token |
| Cache Write (5m) | $4.00 / 1M | $1.40 / 1M | Not applicable |
| Cache Write (1h) | $4.00 / 1M | $1.40 / 1M | Not applicable |
| Cache Read | $4.00 / 1M | $1.40 / 1M | Not applicable |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | N/A |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | No |
| Function Calling | Yes | Yes | No |
| JSON Mode | Yes | Yes | No |
| Streaming | Yes | Yes | No |
| Catalogue | |||
| Provider | Meta | Z.AI | AssemblyAI |
| Category | chat | chat | voice |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | 2026-09-22 |
| Description | |||
| Summary | 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. | 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. | AssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio. |