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.

Muse Spark 1.3Meta Remove
Transcribe 1 ProFish Audio Remove
S2 ProFish Audio Remove
DeepSeek V4.1 FlashDeepSeek Remove

4 is the maximum. Remove one to add another.

muse-spark-1.3 vs transcribe-1-pro vs s2-pro vs deepseek-v4.1-flash
Attribute	Muse Spark 1.3muse-spark-1.3	Transcribe 1 Protranscribe-1-pro	S2 Pros2-pro	DeepSeek V4.1 Flashdeepseek-v4.1-flash
Pricing
Input	$1.25 / 1M	$0 / 1M	$0 / 1M	$0.30 / 1M
Output	$4.25 / 1M	$0 / 1M	$0 / 1M	$1.20 / 1M
Cache Write (5m)	$1.25 / 1M	Not applicable	Not applicable	$0.30 / 1M
Cache Write (1h)	$1.25 / 1M	Not applicable	Not applicable	$0.30 / 1M
Cache Read	$1.25 / 1M	Not applicable	Not applicable	$0.30 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	N/A	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	No	No	Yes
Catalogue
Provider	Meta	Fish Audio	Fish Audio	DeepSeek
Category	chat	voice	voice	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	2026-09-24	2026-07-29	—
Description
Summary	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.	Fish Audio S2 Pro text-to-speech. Billed per UTF-8 byte of input text.	DeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.