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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. Universal-3.5 ProAssemblyAIRemove
  2. Nemotron 3.5 Super VLNVIDIARemove
  3. Qwen3.8 27BAlibabaRemove
universal-3-5-pro vs nemotron-3.5-super-vl vs qwen3.8-27b
AttributeUniversal-3.5 Prouniversal-3-5-proNemotron 3.5 Super VLnemotron-3.5-super-vlQwen3.8 27Bqwen3.8-27b
Pricing
Input— Not priced per input token$0.10 / 1M$0.45 / 1M
Output— Not priced per output token$0.40 / 1M$3.20 / 1M
Cache Write (5m)Not applicable$0.10 / 1M$0.45 / 1M
Cache Write (1h)Not applicable$0.10 / 1M$0.45 / 1M
Cache ReadNot applicable$0.10 / 1M$0.45 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M262K
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderAssemblyAINVIDIAAlibaba
Categoryvoicechatchat
Charge typePay As You GoPay As You GoPay As You Go
Released2026-09-22——
Description
SummaryAssemblyAI Universal-3.5 Pro speech-to-text. Billed per second of audio.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.