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. Gemini 3.5 TranscribeGoogleRemove
  3. Qwen3.8 27BAlibabaRemove
gemini-3.1-flash-lite-image-vertex vs gemini-3.5-transcribe vs qwen3.8-27b
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexGemini 3.5 Transcribegemini-3.5-transcribeQwen3.8 27Bqwen3.8-27b
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicableNot applicable$0.45 / 1M
Cache Write (1h)Not applicableNot applicable$0.45 / 1M
Cache ReadNot applicableNot applicable$0.45 / 1M
Input—— Not priced per input token$0.45 / 1M
Output—— Not priced per output token$3.20 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A98.3K262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoNoYes
StreamingNoNoYes
Catalogue
ProviderGoogleGoogleAlibaba
Categoryimagevoicechat
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.Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output 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.