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.

Claude Sonnet 5.5Anthropic Remove
Perceptron Mk1.5Perceptron Remove
Gemini 3.5 TranscribeGoogle Remove
GLM 5V TurboZ.AI Remove

4 is the maximum. Remove one to add another.

claude-sonnet-5.5 vs perceptron-mk1.5 vs gemini-3.5-transcribe vs glm-5v-turbo
Attribute	Claude Sonnet 5.5claude-sonnet-5.5	Perceptron Mk1.5perceptron-mk1.5	Gemini 3.5 Transcribegemini-3.5-transcribe	GLM 5V Turboglm-5v-turbo
Pricing
Input	$2.00 / 1M	$0.15 / 1M	— Not priced per input token	$1.20 / 1M
Output	$10.00 / 1M	$1.50 / 1M	— Not priced per output token	$4.00 / 1M
Cache Write (5m)	$2.50 / 1M	$0.15 / 1M	Not applicable	$1.20 / 1M
Cache Write (1h)	$4.00 / 1M	$0.15 / 1M	Not applicable	$1.20 / 1M
Cache Read	$0.20 / 1M	$0.15 / 1M	Not applicable	$1.20 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	36.9K	98.3K	202.8K
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	No	Yes	Yes
Function Calling	Yes	No	Yes	Yes
JSON Mode	Yes	No	No	Yes
Streaming	Yes	Yes	No	Yes
Catalogue
Provider	Anthropic	Perceptron	Google	Z.AI
Category	chat	chat	voice	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	2026-09-25	2026-09-25	—
Description
Summary	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.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.	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.