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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. KAT-Coder-Pro V1KwaipilotRemove
  3. DeepSeek V4.1 Flash 1017DeepSeekRemove
nemotron-3.5-super-vl vs kat-coder-pro vs deepseek-v4.1-flash-1017
AttributeNemotron 3.5 Super VLnemotron-3.5-super-vlKAT-Coder-Pro V1kat-coder-proDeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017
Pricing
Input$0.10 / 1M$0.005 / 1M$0.10 / 1M
Output$0.40 / 1M$0.005 / 1M$0.50 / 1M
Cache Write (5m)$0.10 / 1M$0.005 / 1M$0.10 / 1M
Cache Write (1h)$0.10 / 1M$0.005 / 1M$0.10 / 1M
Cache Read$0.10 / 1M$0.005 / 1M$0.10 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M256K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderNVIDIAKwaipilotDeepSeek
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.KAT-Coder-Pro V1 is KwaiKAT’s most advanced agentic coding model, built specifically for real-world software engineering. It achieves a 73.4% solve rate on SWE-Bench Verified and is optimized for tool use, multi-turn workflows, instruction following, and generalization through a multi-stage training pipeline that includes SFT, RFT, and large-scale agentic RL.DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis.