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.

Nemotron 3.5 Super VLNVIDIA Remove
Qwen3.8 27BAlibaba Remove
Muse WonderMeta Remove
Perceptron Mk1.5Perceptron Remove

4 is the maximum. Remove one to add another.

nemotron-3.5-super-vl vs qwen3.8-27b vs muse-wonder vs perceptron-mk1.5
Attribute	Nemotron 3.5 Super VLnemotron-3.5-super-vl	Qwen3.8 27Bqwen3.8-27b	Muse Wondermuse-wonder	Perceptron Mk1.5perceptron-mk1.5
Pricing
Input	$0.10 / 1M	$0.45 / 1M	$4.00 / 1M	$0.15 / 1M
Output	$0.40 / 1M	$3.20 / 1M	$20.00 / 1M	$1.50 / 1M
Cache Write (5m)	$0.10 / 1M	$0.45 / 1M	$4.00 / 1M	$0.15 / 1M
Cache Write (1h)	$0.10 / 1M	$0.45 / 1M	$4.00 / 1M	$0.15 / 1M
Cache Read	$0.10 / 1M	$0.45 / 1M	$4.00 / 1M	$0.15 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	262K	1M	36.9K
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	Yes	No
Function Calling	Yes	Yes	Yes	No
JSON Mode	Yes	Yes	Yes	No
Streaming	Yes	Yes	Yes	Yes
Catalogue
Provider	NVIDIA	Alibaba	Meta	Perceptron
Category	chat	chat	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	—	2026-09-25
Description
Summary	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.	Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.	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.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.