Skip to content

Compare models

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. Perceptron Mk1.5PerceptronRemove
  3. Qwen3.8 2.4T A95BAlibabaRemove
gemini-3.1-flash-lite-image-vertex vs perceptron-mk1.5 vs qwen3.8-2.4t-a95b
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexPerceptron Mk1.5perceptron-mk1.5Qwen3.8 2.4T A95Bqwen3.8-2.4t-a95b
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicable$0.15 / 1M$1.80 / 1M
Cache Write (1h)Not applicable$0.15 / 1M$1.80 / 1M
Cache ReadNot applicable$0.15 / 1M$1.80 / 1M
Input—$0.15 / 1M$1.80 / 1M
Output—$1.50 / 1M$5.40 / 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 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.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Qwen3.8 2.4T A95B is Qwen's open-weight sparse Mixture-of-Experts (MoE) model and the open-weight counterpart to Qwen3.8 Max. It features 2.4T total parameters with 95B activated per token, combining frontier-scale capacity with efficient sparse inference. Designed for coding, research, complex reasoning, and agentic workflows, the model is well suited for demanding long-horizon tasks and advanced autonomous systems while providing the flexibility and customization benefits of open weights.