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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. Universal-3.5 ProAssemblyAIRemove
  2. Nemotron 3.5 Super VLNVIDIARemove
  3. GLM 5.3Z.AIRemove
universal-3-5-pro vs nemotron-3.5-super-vl vs glm-5.3
AttributeUniversal-3.5 Prouniversal-3-5-proNemotron 3.5 Super VLnemotron-3.5-super-vlGLM 5.3glm-5.3
Pricing
Input— Not priced per input token$0.10 / 1M$1.40 / 1M
Output— Not priced per output token$0.40 / 1M$4.40 / 1M
Cache Write (5m)Not applicable$0.10 / 1M$1.40 / 1M
Cache Write (1h)Not applicable$0.10 / 1M$1.40 / 1M
Cache ReadNot applicable$0.10 / 1M$1.40 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionNoYesNo
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderAssemblyAINVIDIAZ.AI
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.GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.