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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. Gemini 3.5 TranscribeGoogleRemove
  2. Nemotron 3.5 Super VL (Free)NVIDIARemove
  3. Qwen3.8 27BAlibabaRemove
  4. Muse WonderMetaRemove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs nemotron-3.5-super-vl:free vs qwen3.8-27b vs muse-wonder
AttributeGemini 3.5 Transcribegemini-3.5-transcribeNemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:freeQwen3.8 27Bqwen3.8-27bMuse Wondermuse-wonder
Pricing
Input— Not priced per input token$0 / 1M$0.45 / 1M$4.00 / 1M
Output— Not priced per output token$0 / 1M$3.20 / 1M$20.00 / 1M
Cache Write (5m)Not applicable—$0.45 / 1M$4.00 / 1M
Cache Write (1h)Not applicable—$0.45 / 1M$4.00 / 1M
Cache ReadNot applicable$0 / 1M$0.45 / 1M$4.00 / 1M
Web Search$0 / 1M—$0 / 1M$0 / 1M
Cache Write—$0 / 1M——
Context
Max context98.3K1M262K1M
Max outputN/AN/AN/AN/A
Capabilities
VisionYesYesYesYes
Function CallingYesYesYesYes
JSON ModeNoYesYesYes
StreamingNoYesYesYes
Catalogue
ProviderGoogleNVIDIAAlibabaMeta
Categoryvoicechatchatchat
Charge typePay As You GoFreePay As You GoPay As You Go
Released2026-09-25———
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.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.