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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. Perceptron Mk1.5PerceptronRemove
  2. Gemini 3.1 Flash Image (Google Vertex AI)GoogleEnterprise & partner accessRemove
  3. Qwen3.8 27BAlibabaRemove
perceptron-mk1.5 vs gemini-3.1-flash-image-vertex vs qwen3.8-27b
AttributePerceptron Mk1.5perceptron-mk1.5Gemini 3.1 Flash Image (Google Vertex AI)gemini-3.1-flash-image-vertexQwen3.8 27Bqwen3.8-27b
Pricing
Input$0.15 / 1M—$0.45 / 1M
Output$1.50 / 1M—$3.20 / 1M
Cache Write (5m)$0.15 / 1MNot applicable$0.45 / 1M
Cache Write (1h)$0.15 / 1MNot applicable$0.45 / 1M
Cache Read$0.15 / 1MNot applicable$0.45 / 1M
Web Search$0 / 1M—$0 / 1M
Request—$0.067 / request—
Billing—Pay Per Request—
Context
Max context36.9KN/A262K
Max outputN/AN/AN/A
Capabilities
VisionNoYesYes
Function CallingNoNoYes
JSON ModeNoNoYes
StreamingYesNoYes
Catalogue
ProviderPerceptronGoogleAlibaba
Categorychatimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released2026-09-25——
Description
SummaryPerceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Gemini 3.1 Flash Image served from Google Vertex AI. Billed per image at US$0.067. 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 2K. A request for a larger size, such as 4K or 4096x4096, returns a 2K 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, for example 4096x4096->2048x2048.Qwen3.8 27B is an open-weight dense vision-language model from Qwen, designed for coding, professional knowledge work, research, and multimodal interaction. It combines strong text and visual understanding with capabilities optimized for sustained, real-world agentic tasks. The model supports flexible thinking modes that can be enabled for deeper reasoning or disabled for faster execution, making it well suited for long-running agents, multimodal workflows, coding assistants, and cost-conscious self-hosted deployments.