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 | Nemotron 3.5 Content Safety (Free)nemotron-3.5-content-safety:free |
|---|---|---|---|
| Pricing | |||
| Input | $1.25 / 1M | $4.00 / 1M | $0 / 1M |
| Output | $4.25 / 1M | $20.00 / 1M | $0 / 1M |
| Cache Write (5m) | $1.25 / 1M | $4.00 / 1M | — |
| Cache Write (1h) | $1.25 / 1M | $4.00 / 1M | — |
| Cache Read | $1.25 / 1M | $4.00 / 1M | $0 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Cache Write | — | — | $0 / 1M |
| Context | |||
| Max context | 1M | 1M | 128K |
| 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 | Meta | NVIDIA |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Free |
| 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. | NVIDIA Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, designed for content moderation, safety classification, and AI policy enforcement. Supporting text and image inputs with text output, it evaluates both user prompts and model responses, providing safe/unsafe classifications, safety category labels, and optional reasoning traces. Fine-tuned from Gemma-3-4B and supporting 12 languages with a 128K-token context window, the model is well suited for prompt moderation, response filtering, content classification, and enterprise safety pipelines. As part of the NVIDIA Nemotron family, it offers a configurable reasoning mode and integrates easily into agentic AI systems requiring robust guardrails and compliance controls. |