Context Window

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Deepseek V3 2 251201 vs Gpt Realtime

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 V3 2 251201

Tool calling

Context window

98K

98,304 tokens · ~74K words

Model page
Openai

Model

Gpt Realtime

Tool calling

Context window

32K

32,000 tokens · ~24K 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 V3 2 25120198K
Gpt Realtime32K

Deepseek V3 2 251201 has about 3.1× the context window of the other in this pair.

Token calculator →Memory savings →

Deepseek V3 2 251201 has 207% more context capacity (98K vs 32K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Deepseek V3 2 251201. Its 98K context fits entire documents without chunking (vs 32K).

  • Long output (reports, code files)

    Use Deepseek V3 2 251201. Its 32K 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.

SpecDeepseek V3 2 251201Gpt Realtime
Context window98,304 tokens (98K)32,000 tokens (32K)
Max output tokens32,768 tokens (32K)4,096 tokens (4K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingNoYes
Batch APINoNo
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.

ProviderDeepseek V3 2 251201 inDeepseek V3 2 251201 outGpt Realtime inGpt Realtime out
Openai——$4.00/M$16.00/M
Volcengine————

Frequently asked questions

Deepseek V3 2 251201 has a larger context window: 98K tokens vs 32K. 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