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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. Gemini 4 ArgonGoogleRemove
qwen3.5-397b-a17b vs gemini-4-argon
AttributeQwen3.5 397B A17Bqwen3.5-397b-a17bGemini 4 Argongemini-4-argon
Pricing
Input$0.55 / 1M$2.00 / 1M
Output$3.50 / 1M$10.00 / 1M
Cache Write (5m)$0.55 / 1M$2.00 / 1M
Cache Write (1h)$0.55 / 1M$2.00 / 1M
Cache Read$0.55 / 1M$2.00 / 1M
Web Search$0 / 1M$0 / 1M
Context
Max context256K1M
Max outputN/AN/A
Capabilities
VisionYesYes
Function CallingYesYes
JSON ModeYesYes
StreamingYesYes
Catalogue
ProviderAlibabaGoogle
Categorychatchat
Charge typePay 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.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.