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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 5.3Z.AIRemove
gemini-3.1-flash-lite-image-vertex vs gemini-3.5-transcribe vs glm-5.3
AttributeGemini 3.1 Flash Lite Image (Google Vertex AI)gemini-3.1-flash-lite-image-vertexGemini 3.5 Transcribegemini-3.5-transcribeGLM 5.3glm-5.3
Pricing
Request$0.0336 / request——
BillingPay Per Request——
Cache Write (5m)Not applicableNot applicable$1.40 / 1M
Cache Write (1h)Not applicableNot applicable$1.40 / 1M
Cache ReadNot applicableNot applicable$1.40 / 1M
Input—— Not priced per input token$1.40 / 1M
Output—— Not priced per output token$4.40 / 1M
Web Search—$0 / 1M$0 / 1M
Context
Max contextN/A98.3K1M
Max outputN/AN/AN/A
Capabilities
VisionYesYesNo
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-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution.