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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. Nemotron 3.5 Super VLNVIDIARemove
  2. Gemini 3.1 Flash Image (Google AI Studio)GoogleEnterprise & partner accessRemove
  3. Muse WonderMetaRemove
nemotron-3.5-super-vl vs gemini-3.1-flash-image-aistudio vs muse-wonder
AttributeNemotron 3.5 Super VLnemotron-3.5-super-vlGemini 3.1 Flash Image (Google AI Studio)gemini-3.1-flash-image-aistudioMuse Wondermuse-wonder
Pricing
Input$0.10 / 1M—$4.00 / 1M
Output$0.40 / 1M—$20.00 / 1M
Cache Write (5m)$0.10 / 1MNot applicable$4.00 / 1M
Cache Write (1h)$0.10 / 1MNot applicable$4.00 / 1M
Cache Read$0.10 / 1MNot applicable$4.00 / 1M
Web Search$0 / 1M—$0 / 1M
Request—$0.067 / request—
Billing—Pay Per Request—
Context
Max context1MN/A1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesNoYes
JSON ModeYesNoYes
StreamingYesNoYes
Catalogue
ProviderNVIDIAGoogleMeta
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released———
Description
SummaryNVIDIA 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.Gemini 3.1 Flash Image served from Google AI Studio. Billed per image at US$0.067. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI.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.