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.

Perceptron Mk1.5Perceptron Remove
Claude Sonnet 5.5Anthropic Remove
Transcribe 1 ProFish Audio Remove
Muse Spark 1.3Meta Remove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs claude-sonnet-5.5 vs transcribe-1-pro vs muse-spark-1.3
Attribute	Perceptron Mk1.5perceptron-mk1.5	Claude Sonnet 5.5claude-sonnet-5.5	Transcribe 1 Protranscribe-1-pro	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0.15 / 1M	$2.00 / 1M	$0 / 1M	$1.25 / 1M
Output	$1.50 / 1M	$10.00 / 1M	$0 / 1M	$4.25 / 1M
Cache Write (5m)	$0.15 / 1M	$2.50 / 1M	Not applicable	$1.25 / 1M
Cache Write (1h)	$0.15 / 1M	$4.00 / 1M	Not applicable	$1.25 / 1M
Cache Read	$0.15 / 1M	$0.20 / 1M	Not applicable	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	36.9K	1M	N/A	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	No	Yes	No	Yes
Function Calling	No	Yes	No	Yes
JSON Mode	No	Yes	No	Yes
Streaming	Yes	Yes	No	Yes
Catalogue
Provider	Perceptron	Anthropic	Fish Audio	Meta
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	—	2026-09-24	—
Description
Summary	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.	Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <\|speaker:0\|>. Billed per second of audio.	Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.