Context Window

Compare

Deepseek R1 0528 Tput vs DeepSeek V3.2 Exp

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 R1 0528 Tput

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Deepseek

Model

DeepSeek V3.2 Exp

Tool calling

Context window

164K

163,840 tokens · ~123K 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 R1 0528 Tput128K
DeepSeek V3.2 Exp164K

DeepSeek V3.2 Exp has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

DeepSeek V3.2 Exp has 28% more context capacity (163K vs 128K tokens). DeepSeek V3.2 Exp is 63% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use DeepSeek V3.2 Exp. Its 163K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use DeepSeek V3.2 Exp. Input tokens are 63% 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 R1 0528 TputDeepSeek V3.2 Exp
Context window128,000 tokens (128K)163,840 tokens (163K)
Max output tokensN/A163,840 tokens (163K)
Speed tierDeepBalanced
VisionNoNo
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/ASep 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 R1 0528 Tput inDeepseek R1 0528 Tput outDeepSeek V3.2 Exp inDeepSeek V3.2 Exp out
Novita——$0.270/M$0.410/M
Openrouter——$0.200/M$0.400/M
Together Ai$0.550/M$2.19/M——

Frequently asked questions

DeepSeek V3.2 Exp has a larger context window: 163K 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