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.
- Muse Spark OpenMetaRemove
- Gemini 4 ArgonGoogleRemove
- MiniMax M1 (Extended)MiniMaxRemove
- Muse WonderMetaRemove
4 is the maximum. Remove one to add another.
| Attribute | Muse Spark Openmuse-spark-open | Gemini 4 Argongemini-4-argon | MiniMax M1 (Extended)minimax-m1:extended | Muse Wondermuse-wonder |
|---|---|---|---|---|
| Pricing | ||||
| Input | $1.25 / 1M | $2.00 / 1M | $0.55 / 1M | $4.00 / 1M |
| Output | $4.25 / 1M | $10.00 / 1M | $2.20 / 1M | $20.00 / 1M |
| Cache Write (5m) | $1.25 / 1M | $2.00 / 1M | $0.55 / 1M | $4.00 / 1M |
| Cache Write (1h) | $1.25 / 1M | $2.00 / 1M | $0.55 / 1M | $4.00 / 1M |
| Cache Read | $1.25 / 1M | $2.00 / 1M | $0.55 / 1M | $4.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | ||||
| Max context | 1M | 1M | 128K | 1M |
| Max output | N/A | N/A | N/A | N/A |
| Capabilities | ||||
| Vision | Yes | Yes | No | Yes |
| Function Calling | Yes | Yes | No | Yes |
| JSON Mode | Yes | Yes | No | Yes |
| Streaming | Yes | Yes | No | Yes |
| Catalogue | ||||
| Provider | Meta | MiniMax | Meta | |
| Category | chat | chat | chat | chat |
| Charge type | Pay As You Go | 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. | 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. | — | 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. |