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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.

  1. Muse WonderMetaRemove
  2. MiniMax M1 (Extended)MiniMaxRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
  4. Muse Spark OpenMetaRemove

4 is the maximum. Remove one to add another.

muse-wonder vs minimax-m1:extended vs nemotron-3.5-super-vl:free vs muse-spark-open
AttributeMuse Wondermuse-wonderMiniMax M1 (Extended)minimax-m1:extendedNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeMuse Spark Openmuse-spark-open
Pricing
Input$4.00 / 1M$0.55 / 1M$0 / 1M$1.25 / 1M
Output$20.00 / 1M$2.20 / 1M$0 / 1M$4.25 / 1M
Cache Write (5m)$4.00 / 1M$0.55 / 1M—$1.25 / 1M
Cache Write (1h)$4.00 / 1M$0.55 / 1M—$1.25 / 1M
Cache Read$4.00 / 1M$0.55 / 1M$0 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M—$0 / 1M
Cache Write——$0 / 1M—
Context
Max context1M128K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeYesNoYesYes
StreamingYesNoYesYes
Catalogue
ProviderMetaMiniMaxNVIDIAMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoFreePay As You Go
Released————
Description
SummaryMuse 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 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.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.