Context Window

Compare

Nova Pro 1.0 vs Openai Gpt 4 1

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.

Amazon

Model

Nova Pro 1.0

Image inputTool calling

Context window

300K

300,000 tokens · ~225K words

Model page
Openai

Model

Openai Gpt 4 1

Image inputTool calling

Context window

300K

300,000 tokens · ~225K 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.

Nova Pro 1.0300K
Openai Gpt 4 1300K

Same context window size for both models.

Token calculator →Memory savings →

Nova Pro 1.0 and Openai Gpt 4 1 have identical context windows (300K tokens). Nova Pro 1.0 is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use Nova Pro 1.0. Input tokens are 60% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Openai Gpt 4 1. Its 16K max output lets you generate complete artifacts in one request.

Full specs

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

SpecNova Pro 1.0Openai Gpt 4 1
Context window300,000 tokens (300K)300,000 tokens (300K)
Max output tokens10,000 tokens (10K)16,384 tokens (16K)
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoNo
Release dateDec 2024N/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.

ProviderNova Pro 1.0 inNova Pro 1.0 outOpenai Gpt 4 1 inOpenai Gpt 4 1 out
Amazon$0.800/M$3.20/M——
Snowflake——$2.00/M$8.00/M

Frequently asked questions

Openai Gpt 4 1 has a larger context window: 300K tokens vs 300K. 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