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 VL 8B Instructqwen3-vl-8b-instruct | Muse Spark Openmuse-spark-open |
|---|---|---|---|
| Pricing | |||
| Input | $4.00 / 1M | $0.09 / 1M | $1.25 / 1M |
| Output | $20.00 / 1M | $0.345 / 1M | $4.25 / 1M |
| Cache Write (5m) | $4.00 / 1M | $0.09 / 1M | $1.25 / 1M |
| Cache Write (1h) | $4.00 / 1M | $0.09 / 1M | $1.25 / 1M |
| Cache Read | $4.00 / 1M | $0.09 / 1M | $1.25 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 262.1K | 1M |
| 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 | Meta | Alibaba | Meta |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | 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-VL-8B-Instruct is a multimodal model for precise reasoning across text, images, and video. With improved fusion architectures (Interleaved-MRoPE, DeepStack, and text-timestamp alignment), it supports long-context understanding up to 1M tokens and handles tasks like document parsing, VQA, spatial reasoning, and GUI control. It delivers LLM-level text comprehension, stronger OCR across 32 languages, and robust performance across diverse visual conditions. | Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential. |