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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.

Gemma 4 26B A4B (Free)Google Remove
Gemini 3.5 TranscribeGoogle Remove
GPT-6.1 SolOpenAI Remove

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gemma-4-26b-a4b-it:free vs gemini-3.5-transcribe vs gpt-6.1-sol
Attribute	Gemma 4 26B A4B (Free)gemma-4-26b-a4b-it:free	Gemini 3.5 Transcribegemini-3.5-transcribe	GPT-6.1 Solgpt-6.1-sol
Pricing
Input	$0 / 1M	— Not priced per input token	$2.00 / 1M
Output	$0 / 1M	— Not priced per output token	$10.00 / 1M
Cache Write	$0 / 1M	—	—
Cache Read	$0 / 1M	Not applicable	$2.00 / 1M
Cache Write (5m)	—	Not applicable	$2.00 / 1M
Cache Write (1h)	—	Not applicable	$2.00 / 1M
Web Search	—	$0 / 1M	$0 / 1M
Context
Max context	262.1K	98.3K	1M
Max output	N/A	N/A	N/A
Capabilities
Vision	Yes	Yes	Yes
Function Calling	Yes	Yes	Yes
JSON Mode	Yes	No	Yes
Streaming	Yes	No	Yes
Catalogue
Provider	Google	Google	OpenAI
Category	chat	voice	chat
Charge type	Free	Pay As You Go	Pay As You Go
Released	—	2026-09-25	—
Description
Summary	Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind, featuring 25.2B total parameters with only 3.8B activated per token—delivering near 31B-class quality at a fraction of the compute cost. It supports multimodal inputs including text, images, and video (up to 60s at 1fps). The model includes a 256K token context window, native function calling, configurable thinking/reasoning modes, and structured output support. Released under the Apache 2.0 license, it is well suited for efficient, production-ready multimodal and agentic applications.	Google Gemini 3.5 Transcribe speech-to-text. Billed per input and output token.	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.