Context Window

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DeepSeek V3.2 vs Sonar

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

Tool calling

Context window

164K

163,840 tokens · ~123K words

Model page
Perplexity

Model

Sonar

Image input

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 V3.2164K
Sonar128K

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

Token calculator →Memory savings →

DeepSeek V3.2 has 28% more context capacity (163K vs 128K tokens). DeepSeek V3.2 is 72% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

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

  • RAG / high-volume retrieval

    Use DeepSeek V3.2. 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 V3.2Sonar
Context window163,840 tokens (163K)128,000 tokens (128K)
Max output tokens163,840 tokens (163K)N/A
Speed tierBalancedBalanced
VisionNoYes
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateDec 2025Jan 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 V3.2 inDeepSeek V3.2 outSonar inSonar out
Aws Bedrock$0.740/M$2.22/M——
Azure$0.580/M$1.68/M——
Deepseek$0.280/M$0.400/M——
Gmi$0.280/M$0.400/M——
Novita$0.269/M$0.400/M——
Openrouter$0.280/M$0.400/M——
Perplexity——$1.00/M$1.00/M

Frequently asked questions

DeepSeek V3.2 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.
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.

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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