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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. Universal-3.5 ProAssemblyAIRemove
  2. Muse WonderMetaRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
universal-3-5-pro vs muse-wonder vs nemotron-3.5-super-vl:free
AttributeUniversal-3.5 Prouniversal-3-5-proMuse Wondermuse-wonderNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free
Pricing
Input— Not priced per input token$4.00 / 1M$0 / 1M
Output— Not priced per output token$20.00 / 1M$0 / 1M
Cache Write (5m)Not applicable$4.00 / 1M—
Cache Write (1h)Not applicable$4.00 / 1M—
Cache ReadNot applicable$4.00 / 1M$0 / 1M
Web Search$0 / 1M$0 / 1M—
Cache Write——$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderAssemblyAIMetaNVIDIA
Categoryvoicechatchat
Charge typePay As You GoPay As You GoFree
Released2026-09-22——
Description
SummaryAssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio.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 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.