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 Spark Openmuse-spark-open | Muse Wondermuse-wonder | Qwen3 30B A3B Thinkqwen3-30b-a3b-think |
|---|---|---|---|
| Pricing | |||
| Input | $1.25 / 1M | $4.00 / 1M | $0.051 / 1M |
| Output | $4.25 / 1M | $20.00 / 1M | $0.34 / 1M |
| Cache Write (5m) | $1.25 / 1M | $4.00 / 1M | $0.051 / 1M |
| Cache Write (1h) | $1.25 / 1M | $4.00 / 1M | $0.051 / 1M |
| Cache Read | $1.25 / 1M | $4.00 / 1M | $0.051 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 32.8K |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | No |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Meta | Meta | Alibaba |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | 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. | 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. | Qwen3-30B-A3B-Thinking-2507 is a 30B-parameter Mixture-of-Experts (MoE) reasoning model optimized for complex tasks that require extended, multi-step reasoning. It is purpose-built for thinking mode, where internal reasoning traces are explicitly separated from final outputs, enabling more structured and reliable problem solving. Compared to earlier Qwen3-30B variants, this release delivers notable gains across logical reasoning, mathematics, science, coding, and multilingual benchmarks, while also strengthening instruction following, tool usage, and alignment with human preferences. With improved reasoning efficiency and larger output budgets, Qwen3-30B-A3B-Thinking-2507 is well suited for advanced research, competitive problem solving, and agentic applications that demand robust long-context and structured reasoning capabilities. |