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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 AI Studio)GoogleEnterprise & partner accessRemove
  2. Perceptron Mk1.5PerceptronRemove
  3. Qwen3.8 27BAlibabaRemove
gemini-3.1-flash-lite-image-aistudio vs perceptron-mk1.5 vs qwen3.8-27b
AttributeGemini 3.1 Flash Lite Image (Google AI Studio)gemini-3.1-flash-lite-image-aistudioPerceptron Mk1.5perceptron-mk1.5Qwen3.8 27Bqwen3.8-27b
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$0.15 / 1M$0.45 / 1M
Cache Write (1h)Not applicable$0.15 / 1M$0.45 / 1M
Cache ReadNot applicable$0.15 / 1M$0.45 / 1M
Input—$0.15 / 1M$0.45 / 1M
Output—$1.50 / 1M$3.20 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A36.9K262K
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingNoNoYes
JSON ModeNoNoYes
StreamingNoYesYes
Catalogue
ProviderGooglePerceptronAlibaba
Categoryimagechatchat
Charge typePay Per RequestPay As You GoPay As You Go
Released—2026-09-25—
Description
SummaryGemini 3.1 Flash Lite Image served from Google AI Studio. 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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.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.