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 | Gemini 4 Argongemini-4-argon | Qwen3 VL 8B Thinkingqwen3-vl-8b-thinking |
|---|---|---|---|
| Pricing | |||
| Input | $4.00 / 1M | $2.00 / 1M | $0.18 / 1M |
| Output | $20.00 / 1M | $10.00 / 1M | $2.10 / 1M |
| Cache Write (5m) | $4.00 / 1M | $2.00 / 1M | $0.18 / 1M |
| Cache Write (1h) | $4.00 / 1M | $2.00 / 1M | $0.18 / 1M |
| Cache Read | $4.00 / 1M | $2.00 / 1M | $0.18 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 256K |
| 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 | Alibaba | |
| 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. | Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response. | Qwen3-VL-8B-Thinking is the reasoning-focused version of the Qwen3-VL-8B multimodal model, built for advanced visual and textual reasoning across images, documents, and video. It adds deeper vision-language fusion and deliberate reasoning paths, supports very long context (256K–1M tokens), and excels at STEM problem solving, causal analysis, and multi-step video understanding — while retaining strong OCR, multilingual capability, and high-quality text generation. |