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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 Vertex AI)GoogleRemove
  2. Gemini 3.5 TranscribeGoogleRemove
  3. GLM 5Z.AIRemove
gemini-3.1-flash-lite-image-vertex vs gemini-3.5-transcribe vs glm-5
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexGemini 3.5 Transcribegemini-3.5-transcribeGLM 5glm-5
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicableNot applicable$0.60 / 1M
Cache Write (1h)Not applicableNot applicable$0.60 / 1M
Cache ReadNot applicableNot applicable$0.60 / 1M
Input—— Not priced per input token$0.60 / 1M
Output—— Not priced per output token$2.20 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A98.3K202.8K
Max outputN/AN/AN/A
Capabilities
VisionYesYesYes
Function CallingNoYesYes
JSON ModeNoNoYes
StreamingNoNoYes
Catalogue
ProviderGoogleGoogleZ.AI
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.GLM-5 is Z.AI's flagship open-source foundation model, engineered for complex systems design and long-horizon agent workflows. Built with expert developers in mind, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With strong agentic planning, deep backend reasoning, and iterative self-correction capabilities, GLM-5 extends beyond traditional code generation to support full-system construction and autonomous execution, making it well suited for advanced engineering and agent-driven development environments.