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 3.5 TranscribeGoogleRemove
  2. Gemini 3.1 Flash Lite Image (Google Vertex AI)GoogleRemove
  3. Qwen3.8 27BAlibabaRemove
gemini-3.5-transcribe vs gemini-3.1-flash-lite-image-vertex vs qwen3.8-27b
AttributeGemini 3.5 Transcribegemini-3.5-transcribeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexQwen3.8 27Bqwen3.8-27b
Pricing
Input— Not priced per input token—$0.45 / 1M
Output— Not priced per output token—$3.20 / 1M
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
Web Search$0 / 1M—$0 / 1M
Request—$0.0336 / request—
Billing—Pay Per Request—
Context
Max context98.3KN/A262K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingYesNoYes
JSON ModeNoNoYes
StreamingNoNoYes
Catalogue
ProviderGoogleGoogleAlibaba
Categoryvoiceimagechat
Charge typePay As You GoPay Per RequestPay As You Go
Released2026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Gemini 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 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.