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 | Qwen3 Next 80B A3B Instructqwen3-next-80b-a3b-instruct |
|---|---|---|
| Pricing | ||
| Input | $4.00 / 1M | $0.10 / 1M |
| Output | $20.00 / 1M | $0.40 / 1M |
| Cache Write (5m) | $4.00 / 1M | $0.10 / 1M |
| Cache Write (1h) | $4.00 / 1M | $0.10 / 1M |
| Cache Read | $4.00 / 1M | $0.10 / 1M |
| Web Search | $0 / 1M | $0 / 1M |
| Context | ||
| Max context | 1M | 262.1K |
| Max output | N/A | N/A |
| Capabilities | ||
| Vision | Yes | No |
| Function Calling | Yes | Yes |
| JSON Mode | Yes | Yes |
| Streaming | Yes | Yes |
| Catalogue | ||
| Provider | Meta | Alibaba |
| Category | chat | 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. | Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model optimized for fast, stable answers without exposing chain-of-thought. It handles complex reasoning, code generation, knowledge QA, and multilingual tasks with strong alignment and formatting reliability. Designed for high throughput and stability on ultra-long inputs and multi-turn conversations, it’s well suited for RAG, tool use, and agent workflows where consistent final outputs matter. It delivers near–large-model performance with efficient inference, making it a strong choice for production assistants and long-context problem solving. |