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 | Jina Embeddings V2 Base DEjina-embeddings-v2-base-de | Muse Wondermuse-wonder |
|---|---|---|
| Pricing | ||
| Input | $0.05 / 1M | $4.00 / 1M |
| Output | — Not priced per output token | $20.00 / 1M |
| Cache Write (5m) | $0.05 / 1M | $4.00 / 1M |
| Cache Write (1h) | $0.05 / 1M | $4.00 / 1M |
| Cache Read | $0.05 / 1M | $4.00 / 1M |
| Web Search | — | $0 / 1M |
| Context | ||
| Max context | 8.2K | 1M |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | No | Yes |
| Function Calling | No | Yes |
| JSON Mode | No | Yes |
| Streaming | No | Yes |
| Catalogue | ||
| Provider | Jina AI | Meta |
| Category | embedding | chat |
| Charge type | Pay As You Go | Pay As You Go |
| Released | — | — |
| 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. |