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 | Universal-3.5 Prouniversal-3-5-pro | Nemotron 3.5 Lightning (Free)nemotron-3.5-lightning:free |
|---|---|---|---|
| Pricing | |||
| Input | $1.25 / 1M | — Not priced per input token | $0 / 1M |
| Output | $4.25 / 1M | — Not priced per output token | $0 / 1M |
| Cache Write (5m) | $1.25 / 1M | Not applicable | — |
| Cache Write (1h) | $1.25 / 1M | Not applicable | — |
| Cache Read | $1.25 / 1M | Not applicable | $0 / 1M |
| Web Search | $0 / 1M | $0 / 1M | — |
| Cache Write | — | — | $0 / 1M |
| Context | |||
| Max context | 1M | N/A | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | Yes | No | Yes |
| JSON Mode | Yes | No | Yes |
| Streaming | Yes | No | Yes |
| Catalogue | |||
| Provider | Meta | AssemblyAI | NVIDIA |
| Category | chat | voice | chat |
| Charge type | Pay As You Go | Pay As You Go | Free |
| Released | — | 2026-09-22 | — |
| 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. | AssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio. | NVIDIA Nemotron 3.5 Lightning is an open Mixture-of-Experts (MoE) model with 30B total parameters and 3B active per token, optimized for high-throughput agentic workloads and efficient inference. Its lightweight active compute and open design make it well suited for specialized agents, domain-specific customization, and scalable production deployments where speed, cost efficiency, and adaptability are key. |