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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. Gemini 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
  2. Qwen3.8 MaxAlibabaRemove
  3. Gemini 4 ArgonGoogleRemove
gemini-3.1-flash-lite-image-vertex vs qwen3.8-max vs gemini-4-argon
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexQwen3.8 Maxqwen3.8-maxGemini 4 Argongemini-4-argon
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$2.00 / 1M$2.00 / 1M
Cache Write (1h)Not applicable$2.00 / 1M$2.00 / 1M
Cache ReadNot applicable$2.00 / 1M$2.00 / 1M
Input—$2.00 / 1M$2.00 / 1M
Output—$6.00 / 1M$10.00 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A1M1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoYesYes
StreamingNoYesYes
Catalogue
ProviderGoogleAlibabaGoogle
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released———
Description
SummaryGemini 3.1 Flash Lite Image served from Google Vertex AI. Billed per image at US$0.0336. Call it through /v1/chat/completions with stream set to false; the image is returned in the message content as a base64 data URI. Maximum output resolution is 1K. A request for a larger size returns a 1K image with the same aspect ratio at the same price. When a size is reduced, the response carries an X-Image-Resolution-Capped header with the requested and delivered size.Qwen3.8 Max is the flagship model in Alibaba's Qwen3.8 series and the general-availability successor to Qwen3.8 Max Preview. It is a multimodal reasoning model designed for complex tasks across reasoning, visual understanding, coding, and agentic workflows. As the production-ready top tier of the Qwen3.8 family, it is well suited for advanced problem solving, multimodal analysis, software engineering, and long-running tool-driven 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.