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Openai Gpt 4o Mini vs Phi 4 Mini Reasoning

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

Openai Gpt 4o Mini

Context window

128K

128,000 tokens · ~96K words

Model page
Microsoft

Model

Phi 4 Mini Reasoning

Tool calling

Context window

131K

131,072 tokens · ~98K 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.

Openai Gpt 4o Mini128K
Phi 4 Mini Reasoning131K

Phi 4 Mini Reasoning has about 1× the context window of the other in this pair.

Phi 4 Mini Reasoning has 2% more context capacity (131K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Phi 4 Mini Reasoning. Its 131K 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.

SpecOpenai Gpt 4o MiniPhi 4 Mini Reasoning
Context window128,000 tokens (128K)131,072 tokens (131K)
Max output tokensN/A4,096 tokens (4K)
Speed tierFastFast
VisionNoNo
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
Release dateN/AN/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.

ProviderOpenai Gpt 4o Mini inOpenai Gpt 4o Mini outPhi 4 Mini Reasoning inPhi 4 Mini Reasoning out
Azure$0.080/M$0.320/M
Gradient

Frequently asked questions

Phi 4 Mini Reasoning has a larger context window: 131K 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.

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