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.

S1Fish Audio Remove
Muse Spark 1.3Meta Remove
Transcribe 1 ProFish Audio Remove
GPT-6 Astra ProOpenAI Remove

4 is the maximum. Remove one to add another.

s1 vs muse-spark-1.3 vs transcribe-1-pro vs gpt-6-astra-pro
Attribute	S1s1	Muse Spark 1.3muse-spark-1.3	Transcribe 1 Protranscribe-1-pro	GPT-6 Astra Progpt-6-astra-pro
Pricing
Input	$0 / 1M	$1.25 / 1M	$0 / 1M	$10.00 / 1M
Output	$0 / 1M	$4.25 / 1M	$0 / 1M	$50.00 / 1M
Cache Write (5m)	Not applicable	$1.25 / 1M	Not applicable	$10.00 / 1M
Cache Write (1h)	Not applicable	$1.25 / 1M	Not applicable	$10.00 / 1M
Cache Read	Not applicable	$1.25 / 1M	Not applicable	$10.00 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	N/A	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	No	Yes	No	Yes
Catalogue
Provider	Fish Audio	Meta	Fish Audio	OpenAI
Category	voice	chat	voice	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	2026-07-29	—	2026-09-24	—
Description
Summary	Fish Audio S1 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.	Fish Audio Transcribe 1 Pro speech-to-text with speaker labels; transcripts include speaker tags such as <\|speaker:0\|>. Billed per second of audio.	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.