Context Window

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GPT-5 Nano vs Nvidia Nemotron 3 5 Lightning 30b A3b

This page is context-first: how much text each model can take in one request. Full specs adds capabilities and limits; the pricing matrix below is only about $/million tokens from hosts that list both models.

Openai

Model

GPT-5 Nano

Image inputTool calling

Context window

272K

272,000 tokens · ~204K words

Model page
Nvidia

Model

Nvidia Nemotron 3 5 Lightning 30b A3b

Context window

66K

65,536 tokens · ~49K words

Model page

Context window · side by side

Bar length is relative to the larger of the two windows (100% = max of this pair). This is not pricing.

GPT-5 Nano272K
Nvidia Nemotron 3 5 Lightning 30b A3b66K

GPT-5 Nano has about 4.2× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-5 Nano has 315% more context capacity (272K vs 65K tokens). GPT-5 Nano is 66% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-5 Nano. Its 272K context fits entire documents without chunking (vs 65K).

  • RAG / high-volume retrieval

    Use GPT-5 Nano. Input tokens are 66% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecGPT-5 NanoNvidia Nemotron 3 5 Lightning 30b A3b
Context window272,000 tokens (272K)65,536 tokens (65K)
Max output tokens128,000 tokens (128K)N/A
Speed tierFastFast
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateAug 2025N/A

Pricing matrix

Dollar rates only: hosts that list both models, per 1M tokens. For how much text fits, use the context section above — not this table.

ProviderGPT-5 Nano inGPT-5 Nano outNvidia Nemotron 3 5 Lightning 30b A3b inNvidia Nemotron 3 5 Lightning 30b A3b out
Azure$0.050/M$0.400/M——
Gmi——$0.150/M$1.00/M
Openai$0.050/M$0.400/M——
Openrouter$0.050/M$0.400/M——
Replicate$0.050/M$0.400/M——

Frequently asked questions

GPT-5 Nano has a larger context window: 272K tokens vs 65K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

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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
With Mem0~20K tokens sent
Key memories
Current turn

80% less to send — works with any model