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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

GPT-6.1 SolOpenAI Remove
Gemini Embedding 2Google Remove
Gemini 3.5 TranscribeGoogle Remove

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gpt-6.1-sol vs gemini-embedding-2-preview vs gemini-3.5-transcribe
Attribute	GPT-6.1 Solgpt-6.1-sol	Gemini Embedding 2gemini-embedding-2-preview	Gemini 3.5 Transcribegemini-3.5-transcribe
Pricing
Input	$2.00 / 1M	$0.60 / 1M	— Not priced per input token
Output	$10.00 / 1M	$2.40 / 1M	— Not priced per output token
Cache Write (5m)	$2.00 / 1M	$0.60 / 1M	Not applicable
Cache Write (1h)	$2.00 / 1M	$0.60 / 1M	Not applicable
Cache Read	$2.00 / 1M	$0.60 / 1M	Not applicable
Web Search	$0 / 1M	—	$0 / 1M
Context
Max context	1M	8.2K	98.3K
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	Yes
Function Calling	Yes	Yes	Yes
JSON Mode	Yes	No	No
Streaming	Yes	No	No
Catalogue
Provider	OpenAI	Google	Google
Category	chat	embedding	voice
Charge type	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	—	2026-09-25
Description
Summary	GPT-6.1 Sol is an upgraded high-end model in OpenAI's GPT-6 series, positioned below the flagship GPT-6 Astra. It is optimized for agentic coding, computer use, document-heavy professional work, and multi-step business automation, delivering near-Astra-level capability at significantly lower cost. Compared with GPT-6 Sol, it offers improved factual reliability and stronger adherence to explicit constraints and user intent, making it well suited for complex, long-running agentic workflows where accurate and dependable execution is critical.	Gemini Embedding 2 is Google's advanced text embedding model designed for high-accuracy semantic representation across large-scale retrieval and understanding tasks. It converts text into dense vector embeddings optimized for semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems. Built for production use, it offers strong multilingual support, improved semantic similarity accuracy, and efficient embedding generation, making it well suited for large knowledge indexing pipelines and enterprise-scale retrieval applications.	Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.