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.

GPT-6 AstraOpenAI Remove
Perceptron Mk1.5Perceptron Remove
S2 ProFish Audio Remove
Muse Spark 1.3Meta Remove

4 is the maximum. Remove one to add another.

gpt-6-astra vs perceptron-mk1.5 vs s2-pro vs muse-spark-1.3
Attribute	GPT-6 Astragpt-6-astra	Perceptron Mk1.5perceptron-mk1.5	S2 Pros2-pro	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$10.00 / 1M	$0.15 / 1M	$0 / 1M	$1.25 / 1M
Output	$50.00 / 1M	$1.50 / 1M	$0 / 1M	$4.25 / 1M
Cache Write (5m)	$10.00 / 1M	$0.15 / 1M	Not applicable	$1.25 / 1M
Cache Write (1h)	$10.00 / 1M	$0.15 / 1M	Not applicable	$1.25 / 1M
Cache Read	$10.00 / 1M	$0.15 / 1M	Not applicable	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	36.9K	N/A	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	No	No	Yes
Function Calling	Yes	No	No	Yes
JSON Mode	Yes	No	No	Yes
Streaming	Yes	Yes	No	Yes
Catalogue
Provider	OpenAI	Perceptron	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	2026-07-29	—
Description
Summary	GPT-6 Astra is OpenAI's flagship model for demanding end-to-end professional work, designed for advanced analysis, software engineering, deep research, scientific tasks, and document creation. It is particularly strong in long-horizon agentic workflows, including tasks that require sustained reasoning, tool orchestration, and computer and browser use, making it well suited for complex autonomous workflows and production-grade knowledge work.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	Fish Audio S2 Pro text-to-speech. Billed per UTF-8 byte of input text.	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.