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
S1Fish Audio Remove
GPT-6 Astra ProOpenAI Remove
Muse Spark 1.3Meta Remove

4 is the maximum. Remove one to add another.

perceptron-mk1.5 vs s1 vs gpt-6-astra-pro vs muse-spark-1.3
Attribute	Perceptron Mk1.5perceptron-mk1.5	S1s1	GPT-6 Astra Progpt-6-astra-pro	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0.15 / 1M	$0 / 1M	$10.00 / 1M	$1.25 / 1M
Output	$1.50 / 1M	$0 / 1M	$50.00 / 1M	$4.25 / 1M
Cache Write (5m)	$0.15 / 1M	Not applicable	$10.00 / 1M	$1.25 / 1M
Cache Write (1h)	$0.15 / 1M	Not applicable	$10.00 / 1M	$1.25 / 1M
Cache Read	$0.15 / 1M	Not applicable	$10.00 / 1M	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	36.9K	N/A	1M	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	No	No	Yes	Yes
Function Calling	No	No	Yes	Yes
JSON Mode	No	No	Yes	Yes
Streaming	Yes	No	Yes	Yes
Catalogue
Provider	Perceptron	Fish Audio	OpenAI	Meta
Category	chat	voice	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	2026-09-25	2026-07-29	—	—
Description
Summary	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	Fish Audio S1 text-to-speech. Billed per UTF-8 byte of input text.	GPT-6 Astra Pro uses the same underlying model as GPT-6 Astra, but runs with reasoning.mode set to pro for higher-quality responses on complex tasks. Optimized for deeper reasoning, greater accuracy, and more reliable multi-step execution, it is well suited for demanding coding, analysis, and agentic workflows where solution quality takes priority over speed and cost.	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.