Context Window

Compare

Deepseek Reasoner vs o3 Mini High

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.

Deepseek

Model

Deepseek Reasoner

Context window

131K

131,072 tokens · ~98K words

Model page
Openai

Model

o3 Mini High

Tool calling

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.

Deepseek Reasoner131K
o3 Mini High128K

Deepseek Reasoner has about 1× the context window of the other in this pair.

Token calculator →Memory savings →

Deepseek Reasoner has 2% more context capacity (131K vs 128K tokens). Deepseek Reasoner is 74% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Deepseek Reasoner. Its 131K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Deepseek Reasoner. Input tokens are 74% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDeepseek Reasonero3 Mini High
Context window131,072 tokens (131K)128,000 tokens (128K)
Max output tokens65,536 tokens (65K)65,536 tokens (65K)
Speed tierBalancedFast
VisionNoNo
Function callingNoYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoYes
Release dateN/AFeb 2025

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.

ProviderDeepseek Reasoner inDeepseek Reasoner outo3 Mini High ino3 Mini High out
Deepseek$0.280/M$0.420/M——
Openrouter——$1.10/M$4.40/M

Frequently asked questions

Deepseek Reasoner 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.

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Use a smaller model.
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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