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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. Qwen3.5 Plus 2026-02-15AlibabaRemove
  2. Nemotron 3.5 Super VLNVIDIARemove
qwen3.5-plus-02-15 vs nemotron-3.5-super-vl
AttributeQwen3.5 Plus 2026-02-15qwen3.5-plus-02-15Nemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$0.26 / 1M$0.10 / 1M
Output$1.56 / 1M$0.40 / 1M
Cache Write (5m)$0.26 / 1M$0.10 / 1M
Cache Write (1h)$0.26 / 1M$0.10 / 1M
Cache Read$0.26 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context1M1M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderAlibabaNVIDIA
Categorychatchat
Charge typePay As You GoPay As You Go
Released——
Description
SummaryQwen3.5 Vision-Language Plus models are part of the native multimodal Qwen3.5 series, built on a hybrid architecture that combines linear attention mechanisms with sparse Mixture-of-Experts (MoE) designs to improve inference efficiency at scale. Across a wide range of evaluations, the series demonstrates performance comparable to leading state-of-the-art models. Compared with the Qwen3 generation, the 3.5 Plus models deliver significant improvements in both pure-text reasoning and multimodal understanding, making them well suited for applications that require strong performance across language, vision, and agent-based tasks.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.