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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. Claude Sonnet 5.5AnthropicRemove
  3. Qwen3.8 2.4T A95BAlibabaRemove
nemotron-3.5-super-vl vs claude-sonnet-5.5 vs qwen3.8-2.4t-a95b
AttributeNemotron 3.5 Super VLnemotron-3.5-super-vlClaude Sonnet 5.5claude-sonnet-5.5Qwen3.8 2.4T A95Bqwen3.8-2.4t-a95b
Pricing
Input$0.10 / 1M$2.00 / 1M$1.80 / 1M
Output$0.40 / 1M$10.00 / 1M$5.40 / 1M
Cache Write (5m)$0.10 / 1M$2.50 / 1M$1.80 / 1M
Cache Write (1h)$0.10 / 1M$4.00 / 1M$1.80 / 1M
Cache Read$0.10 / 1M$0.10 / 1M$1.80 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M1M262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderNVIDIAAnthropicAlibaba
Categorychatchatchat
Charge typePay As You GoPay 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.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.Qwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.