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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. Claude Opus 4.5AnthropicRemove
  2. Nemotron 3.5 Super VLNVIDIARemove
claude-opus-4-5-20251101 vs nemotron-3.5-super-vl
AttributeClaude Opus 4.5claude-opus-4-5-20251101Nemotron 3.5 Super VLnemotron-3.5-super-vl
Pricing
Input$5.00 / 1M$0.10 / 1M
Output$25.00 / 1M$0.40 / 1M
Cache Write (5m)$6.25 / 1M$0.10 / 1M
Cache Write (1h)$10.00 / 1M$0.10 / 1M
Cache Read$0.50 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context200K1M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderAnthropicNVIDIA
Categorychatchat
Charge typePay As You GoPay As You Go
Released——
Description
SummaryClaude Opus 4.5 is Anthropic's frontier reasoning model, built for complex engineering, agent workflows, and long computer-use tasks. It offers strong multimodal skills, better security against prompt injection, and flexible effort controls — including a Verbosity setting to trade speed vs. depth and token use. With advanced tool use, long-context handling, and support for coordinated multi-agent setups, it excels at research, debugging, multi-step planning, and UI/spreadsheet automation while improving reliability, alignment, and efficiency over earlier Opus versions.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.