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

4 is the maximum. Remove one to add another.

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