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Apertis

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. Perceptron Mk1.5PerceptronRemove
  3. Claude Sonnet 5.5AnthropicRemove
  4. GLM 5V TurboZ.AIRemove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs perceptron-mk1.5 vs claude-sonnet-5.5 vs glm-5v-turbo
AttributeGemini 3.5 Transcribegemini-3.5-transcribePerceptron Mk1.5perceptron-mk1.5Claude Sonnet 5.5claude-sonnet-5.5GLM 5V Turboglm-5v-turbo
Pricing
Input— Not priced per input token$0.15 / 1M$2.00 / 1M$1.20 / 1M
Output— Not priced per output token$1.50 / 1M$10.00 / 1M$4.00 / 1M
Cache Write (5m)Not applicable$0.15 / 1M$2.50 / 1M$1.20 / 1M
Cache Write (1h)Not applicable$0.15 / 1M$4.00 / 1M$1.20 / 1M
Cache ReadNot applicable$0.15 / 1M$0.20 / 1M$1.20 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M$0 / 1M
Context
Max context98.3K36.9K1M202.8K
Max outputN/AN/AN/AN/A
Capabilities
VisionYesNoYesYes
Function CallingYesNoYesYes
JSON ModeNoNoYesYes
StreamingNoYesYesYes
Catalogue
ProviderGooglePerceptronAnthropicZ.AI
Categoryvoicechatchatchat
Charge typePay As You GoPay As You GoPay As You GoPay As You Go
Released2026-09-252026-09-25——
Description
SummaryGoogle Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.Claude Sonnet 5.5 is Anthropic's Sonnet-class model for well-scoped everyday work, serving as a direct upgrade to Sonnet 5. It excels at feature development, bug fixing, and creating polished documents, presentations, and spreadsheets, while offering clearer writing and communication than its predecessor.GLM-5V-Turbo is Z.ai's first native multimodal agent foundation model, designed for vision-based coding and agent-driven workflows. It natively supports image, video, and text inputs, enabling integrated multimodal reasoning and execution. The model excels at long-horizon planning, complex coding, and multi-step task execution, and works seamlessly with agents to complete the full loop of “perceive → plan → execute”, making it well suited for advanced multimodal automation and real-world agent systems.