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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 397B A17BAlibabaRemove
  2. Nemotron 3.5 Super VLNVIDIARemove
  3. Gemini 4 ArgonGoogleRemove
qwen3.5-397b-a17b vs nemotron-3.5-super-vl vs gemini-4-argon
AttributeQwen3.5 397B A17Bqwen3.5-397b-a17bNemotron 3.5 Super VLnemotron-3.5-super-vlGemini 4 Argongemini-4-argon
Pricing
Input$0.55 / 1M$0.10 / 1M$2.00 / 1M
Output$3.50 / 1M$0.40 / 1M$10.00 / 1M
Cache Write (5m)$0.55 / 1M$0.10 / 1M$2.00 / 1M
Cache Write (1h)$0.55 / 1M$0.10 / 1M$2.00 / 1M
Cache Read$0.55 / 1M$0.10 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context256K1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderAlibabaNVIDIAGoogle
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released———
Description
SummaryQwen3.5-397B-A17B is a native vision-language model built on a hybrid architecture that combines linear attention mechanisms with a sparse Mixture-of-Experts (MoE) design to achieve higher inference efficiency at large scale. It delivers state-of-the-art performance across a broad range of tasks, including language understanding, logical reasoning, code generation, agent-based workflows, image and video understanding, and GUI interaction. With strong coding and agent capabilities, Qwen3.5-397B-A17B demonstrates robust generalization across diverse multimodal and agentic scenarios, making it well suited for advanced applications that require integrated reasoning across text, vision, and interactive environments.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.Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response.