Skip to content

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. Qwen Plus 2025-07-28AlibabaRemove
  2. Muse WonderMetaRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
qwen-plus-2025-07-28 vs muse-wonder vs nemotron-3.5-super-vl
AttributeQwen Plus 2025-07-28qwen-plus-2025-07-28Muse Wondermuse-wonderNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$0.40 / 1M$4.00 / 1M$0.10 / 1M
Output$1.20 / 1M$20.00 / 1M$0.40 / 1M
Cache Write (5m)$0.40 / 1M$4.00 / 1M$0.10 / 1M
Cache Write (1h)$0.40 / 1M$4.00 / 1M$0.10 / 1M
Cache Read$0.40 / 1M$4.00 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAlibabaMetaNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryQwen Plus 0728 is a hybrid reasoning model built on the Qwen3 foundation, offering a 1M-token context window with a balanced mix of performance, speed, and cost efficiency.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.