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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. Perceptron Mk1.5PerceptronRemove
  2. Muse Glimmer 30BMetaRemove
  3. Nemotron 3.5 Super VL (Free)NVIDIARemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs muse-glimmer-30b vs nemotron-3.5-super-vl:free vs muse-wonder
AttributePerceptron Mk1.5perceptron-mk1.5Muse Glimmer 30Bmuse-glimmer-30bNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeMuse Wondermuse-wonder
Pricing
Input$0.15 / 1M$0.35 / 1M$0 / 1M$4.00 / 1M
Output$1.50 / 1M$1.50 / 1M$0 / 1M$20.00 / 1M
Cache Write (5m)$0.15 / 1M$0.35 / 1M—$4.00 / 1M
Cache Write (1h)$0.15 / 1M$0.35 / 1M—$4.00 / 1M
Cache Read$0.15 / 1M$0.35 / 1M$0 / 1M$4.00 / 1M
Web Search$0 / 1M$0 / 1M—$0 / 1M
Cache Write——$0 / 1M—
Context
Max context36.9K131K1M1M
Max outputN/AN/AN/AN/A
Capabilities
VisionNoYesYesYes
Function CallingNoYesYesYes
JSON ModeNoYesYesYes
StreamingYesYesYesYes
Catalogue
ProviderPerceptronMetaNVIDIAMeta
Categorychatchatchatchat
Charge typePay As You GoPay As You GoFreePay As You Go
Released2026-09-25———
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It combines strong multi-step reasoning, reliable tool use, failure recovery, image understanding, and multilingual support across 100+ languages. Designed for long-horizon agentic and coding workflows, Muse Glimmer 30B offers a practical balance of capability and deployment efficiency, making it well suited for local coding assistants, multimodal agents, and production workflows that require sustained autonomous execution.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 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.