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.

Gemini 3.5 TranscribeGoogle Remove
GPT-6 AstraOpenAI Remove
Aura-2Deepgram Remove
Muse Spark 1.3Meta Remove

4 is the maximum. Remove one to add another.

gemini-3.5-transcribe vs gpt-6-astra vs aura-2 vs muse-spark-1.3
Attribute	Gemini 3.5 Transcribegemini-3.5-transcribe	GPT-6 Astragpt-6-astra	Aura-2aura-2	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0 / 1M	$10.00 / 1M	$0 / 1M	$1.25 / 1M
Output	$0 / 1M	$50.00 / 1M	$0 / 1M	$4.25 / 1M
Cache Write (5m)	Not applicable	$10.00 / 1M	Not applicable	$1.25 / 1M
Cache Write (1h)	Not applicable	$10.00 / 1M	Not applicable	$1.25 / 1M
Cache Read	Not applicable	$10.00 / 1M	Not applicable	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	98.3K	1M	N/A	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	No	Yes
Function Calling	Yes	Yes	No	Yes
JSON Mode	No	Yes	No	Yes
Streaming	No	Yes	No	Yes
Catalogue
Provider	Google	OpenAI	Deepgram	Meta
Category	voice	chat	voice	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	2026-09-25	—	2026-07-16	—
Description
Summary	Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.	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.	Deepgram Aura-2 text-to-speech for real-time voice applications. Billed per input character.	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.