Context Window

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GPT-4.1 Nano vs Openai Gpt 4o Mini

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-4.1 Nano

Image inputTool calling

Context window

1.0M

1,047,576 tokens · ~786K words

Model page
Openai

Model

Openai Gpt 4o Mini

Context window

128K

128,000 tokens · ~96K 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-4.1 Nano1.0M
Openai Gpt 4o Mini128K

GPT-4.1 Nano has about 8.2× the context window of the other in this pair.

Token calculator →Memory savings →

GPT-4.1 Nano has 718% more context capacity (1047K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use GPT-4.1 Nano. Its 1047K context fits entire documents without chunking (vs 128K).

Full specs

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

SpecGPT-4.1 NanoOpenai Gpt 4o Mini
Context window1,047,576 tokens (1047K)128,000 tokens (128K)
Max output tokens32,768 tokens (32K)N/A
Speed tierFastFast
VisionYesNo
Function callingYesNo
Extended thinkingNoNo
Prompt cachingYesNo
Batch APINoYes
Release dateApr 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-4.1 Nano inGPT-4.1 Nano outOpenai Gpt 4o Mini inOpenai Gpt 4o Mini out
Azure$0.100/M$0.400/M——
Gradient————
Openai$0.100/M$0.400/M——
Openrouter$0.100/M$0.400/M——
Replicate$0.100/M$0.400/M——

Frequently asked questions

GPT-4.1 Nano has a larger context window: 1047K tokens vs 128K. 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