NvidiabalancedVision

Nemotron 3.5 Content Safety (free)

NVIDIA Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, fine-tuned from Google Gemma-3-4B. It moderates both inputs to and responses from LLMs and VLMs, accepting...

128K context·~96K words·8K max output

View context details Compare with Jp Anthropic Claude Sonnet 4 6

Context window128Ktokens

Max output8Ktokens

Context window

This model accepts 128K tokens in one request (~96K words of text).

Context window size128K tokens

4K32K128K1M10M

What fits in one request

Short document
About 1,500 words of text
Fits
Long document
About 37K words of text
Fits
Small codebase
About 150K words of text
Won't fit
Full novel
About 375K words of text
Won't fit

Specifications

Context size, pricing, and release info in one place.

Context window: 128,000 tokens (128K)
Max output tokens: 8,192 tokens (8K)
Speed tier: balanced
Provider: Nvidia
Release date: Jun 2026

Capabilities

See which features this model supports, such as vision, tools, and streaming.

Supported (3)

Vision: Accepts image inputs alongside text; Supported
Extended thinking: Shows its chain-of-thought reasoning; Supported
Streaming: Returns tokens as they are generated; Supported

Not supported (5)

Tool use: Can call external tools and APIs; Not supported
Function calling: Structured function call interface; Not supported
Web search: Can browse the web during a request; Not supported
Batch API: Process many requests asynchronously; Not supported
Prompt caching: Reuse repeated prompt prefixes cheaply; Not supported

Best for

Jump to a guide or ranking that matches each workload.

Reasoning Multimodal Long documents RAG

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Frequently asked questions

Short answers about context size and how this model behaves.

Nemotron 3.5 Content Safety (free) has a context window of 128K tokens (128,000 tokens). This covers most professional use cases including large code files, lengthy reports, and long conversation histories.

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Mem0 gives your AI long-term memory so you stop re-sending context on every call. That means you can use a smaller, faster, cheaper model — and still get better answers.

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Example: a multi-turn chat session

Without Mem0~128K tokens sent

Full history

Repeated info

Old context

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Key memories

Current turn

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