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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. Qwen3 Next 80B A3B InstructAlibabaRemove
nemotron-3.5-super-vl vs qwen3-next-80b-a3b-instruct
AttributeNemotron 3.5 Super VLnemotron-3.5-super-vlQwen3 Next 80B A3B Instructqwen3-next-80b-a3b-instruct
Pricing
Input$0.10 / 1M$0.10 / 1M
Output$0.40 / 1M$0.40 / 1M
Cache Write (5m)$0.10 / 1M$0.10 / 1M
Cache Write (1h)$0.10 / 1M$0.10 / 1M
Cache Read$0.10 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M262.1K
Max outputN/AN/A
Capabilities
VisionYesNo
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderNVIDIAAlibaba
Categorychatchat
Charge typePay As You GoPay 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.Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model optimized for fast, stable answers without exposing chain-of-thought. It handles complex reasoning, code generation, knowledge QA, and multilingual tasks with strong alignment and formatting reliability. Designed for high throughput and stability on ultra-long inputs and multi-turn conversations, it’s well suited for RAG, tool use, and agent workflows where consistent final outputs matter. It delivers near–large-model performance with efficient inference, making it a strong choice for production assistants and long-context problem solving.