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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.

Nemotron 3.5 Super VL (Free)NVIDIA Remove
Gemini 3.8 FlashGoogle Remove

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DeepSeek V4.1 Flash 1017
Nemotron 3.5 Super VL
Muse Spark Open
Gemini 4 Argon
Muse Wonder
Gemini 3.1 Flash Lite Image (Google Vertex AI)
Gemini 3.1 Flash Lite Image (Google AI Studio)Enterprise & partner access
Gemini 3.1 Flash Image (Google Vertex AI)Enterprise & partner access

nemotron-3.5-super-vl:free vs gemini-3.8-flash
Attribute	Nemotron 3.5 Super VL (Free)nemotron-3.5-super-vl:free	Gemini 3.8 Flashgemini-3.8-flash
Pricing
Input	$0 / 1M	$0.75 / 1M
Output	$0 / 1M	$3.75 / 1M
Cache Write	$0 / 1M	—
Cache Read	$0 / 1M	$0.75 / 1M
Cache Write (5m)	—	$0.75 / 1M
Cache Write (1h)	—	$0.75 / 1M
Web Search	—	$0 / 1M
Context
Max context	1M	1M
Max output	N/A	N/A
Capabilities
Vision	Yes	Yes
Function Calling	Yes	Yes
JSON Mode	Yes	Yes
Streaming	Yes	Yes
Catalogue
Provider	NVIDIA	Google
Category	chat	chat
Charge type	Free	Pay As You Go
Released	—	—
Description
Summary	NVIDIA Nemotron 3.5 Super VL is an open multimodal Mixture-of-Experts (MoE) model featuring 120B total parameters with 12B activated per token. Built on a hybrid Mamba–Transformer architecture with multi-token prediction, it supports text, images, documents, GUI screenshots, and video inputs, along with a 1M-token context window for complex, long-horizon workflows. Optimized for multimodal reasoning, visual understanding, and agentic tool use, the model supports configurable reasoning modes and token budgets. It is well suited for document intelligence, web research, coding validation, customer service, GUI analysis, and video understanding, enabling efficient, long-running multimodal agent applications.	Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality.