Context Window

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Deepseek V3p2 vs Sonar Deep Research

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 V3p2

Tool calling

Context window

164K

163,840 tokens · ~123K words

Model page
Perplexity

Model

Sonar Deep Research

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 V3p2164K
Sonar Deep Research128K

Deepseek V3p2 has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Deepseek V3p2 has 28% more context capacity (163K vs 128K tokens). Deepseek V3p2 is 72% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Deepseek V3p2. Its 163K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Deepseek V3p2. Input tokens are 72% 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 V3p2Sonar Deep Research
Context window163,840 tokens (163K)128,000 tokens (128K)
Max output tokens163,840 tokens (163K)N/A
Speed tierBalancedBalanced
VisionNoNo
Function callingYesNo
Extended thinkingYesYes
Prompt cachingNoNo
Batch APINoNo
Release dateN/AMar 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 V3p2 inDeepseek V3p2 outSonar Deep Research inSonar Deep Research out
Fireworks$0.560/M$1.68/M——
Perplexity——$2.00/M$8.00/M

Frequently asked questions

Deepseek V3p2 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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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