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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-FlashAlibabaRemove
  2. Muse Spark OpenMetaRemove
  3. Nemotron 3.5 Super VLNVIDIARemove
qwen3.5-flash-02-23 vs muse-spark-open vs nemotron-3.5-super-vl
AttributeQwen3.5-Flashqwen3.5-flash-02-23Muse Spark Openmuse-spark-openNemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$0.10 / 1M$1.25 / 1M$0.10 / 1M
Output$0.40 / 1M$4.25 / 1M$0.40 / 1M
Cache Write (5m)$0.10 / 1M$1.25 / 1M$0.10 / 1M
Cache Write (1h)$0.10 / 1M$1.25 / 1M$0.10 / 1M
Cache Read$0.10 / 1M$1.25 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAlibabaMetaNVIDIA
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryQwen3.5 Vision-Language Flash models are built on a hybrid architecture that combines linear attention mechanisms with a sparse Mixture-of-Experts (MoE) design to achieve higher inference efficiency. Compared with the Qwen3 generation, the 3.5 Flash models deliver significant improvements in both pure-text reasoning and multimodal understanding. Optimized for fast response times, they strike a strong balance between inference speed and overall performance, making them well suited for real-time multimodal and agent-based applications.Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential.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.