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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Nemotron 3.5 Lightning (Free)NVIDIARemove
  2. Muse Spark OpenMetaRemove
  3. Muse WonderMetaRemove
nemotron-3.5-lightning:free vs muse-spark-open vs muse-wonder
AttributeNemotron 3.5 Lightning (Free)nemotron-3.5-lightning:freeMuse Spark Openmuse-spark-openMuse Wondermuse-wonder
Pricing
Input$0 / 1M$1.25 / 1M$4.00 / 1M
Output$0 / 1M$4.25 / 1M$20.00 / 1M
Cache Write$0 / 1M——
Cache Read$0 / 1M$1.25 / 1M$4.00 / 1M
Cache Write (5m)—$1.25 / 1M$4.00 / 1M
Cache Write (1h)—$1.25 / 1M$4.00 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderNVIDIAMetaMeta
Categorychatchatchat
Charge typeFreePay As You GoPay As You Go
Released———
Description
SummaryNVIDIA 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.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.