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.

Aura-2Deepgram Remove
Gemini 3.5 TranscribeGoogle Remove
Claude Sonnet 5.5Anthropic Remove
Muse Spark 1.3Meta Remove

4 is the maximum. Remove one to add another.

aura-2 vs gemini-3.5-transcribe vs claude-sonnet-5.5 vs muse-spark-1.3
Attribute	Aura-2aura-2	Gemini 3.5 Transcribegemini-3.5-transcribe	Claude Sonnet 5.5claude-sonnet-5.5	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0 / 1M	$0 / 1M	$2.00 / 1M	$1.25 / 1M
Output	$0 / 1M	$0 / 1M	$10.00 / 1M	$4.25 / 1M
Cache Write (5m)	Not applicable	Not applicable	$2.50 / 1M	$1.25 / 1M
Cache Write (1h)	Not applicable	Not applicable	$4.00 / 1M	$1.25 / 1M
Cache Read	Not applicable	Not applicable	$0.20 / 1M	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	N/A	98.3K	1M	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	No	Yes	Yes	Yes
Function Calling	No	Yes	Yes	Yes
JSON Mode	No	No	Yes	Yes
Streaming	No	No	Yes	Yes
Catalogue
Provider	Deepgram	Google	Anthropic	Meta
Category	voice	voice	chat	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	2026-07-16	2026-09-25	—	—
Description
Summary	Deepgram Aura-2 text-to-speech for real-time voice applications. Billed per input character.	Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output 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.	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.