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GPT-5 Nano vs Granite 4 H Small

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
Ibm

Model

Granite 4 H Small

Tool calling

Context window

20K

20,480 tokens · ~15K 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
Granite 4 H Small20K

GPT-5 Nano has about 13.3× the context window of the other in this pair.

GPT-5 Nano has 1228% more context capacity (272K vs 20K tokens). GPT-5 Nano is 16% 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 20K).

  • RAG / high-volume retrieval

    Use GPT-5 Nano. Input tokens are 16% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use GPT-5 Nano. Its 128K 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.

SpecGPT-5 NanoGranite 4 H Small
Context window272,000 tokens (272K)20,480 tokens (20K)
Max output tokens128,000 tokens (128K)20,480 tokens (20K)
Speed tierFastBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
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 outGranite 4 H Small inGranite 4 H Small out
Azure$0.050/M$0.400/M
Ibm Watsonx$0.060/M$0.250/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 20K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

Use a smaller model.
Get better results.

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.

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