Context Window

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DeepSeek V3.1 vs Deepseek V3 1

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 V3.1

Tool calling

Context window

164K

163,840 tokens · ~123K words

Model page
Deepseek

Model

Deepseek V3 1

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 V3.1164K
Deepseek V3 1164K

Same context window size for both models.

Token calculator →Memory savings →

DeepSeek V3.1 and Deepseek V3 1 have identical context windows (163K tokens). DeepSeek V3.1 is 25% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • RAG / high-volume retrieval

    Use DeepSeek V3.1. Input tokens are 25% 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 V3.1Deepseek V3 1
Context window163,840 tokens (163K)163,840 tokens (163K)
Max output tokens163,840 tokens (163K)163,840 tokens (163K)
Speed tierBalancedBalanced
VisionNoNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateAug 2025N/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 V3.1 inDeepSeek V3.1 outDeepseek V3 1 inDeepseek V3 1 out
Baseten——$0.500/M$1.50/M
Deepinfra——$0.270/M$1.00/M
Novita——$0.270/M$1.00/M
Openrouter$0.200/M$0.800/M——
Replicate——$0.672/M$2.02/M
Sambanova——$3.00/M$4.50/M
Together Ai——$0.600/M$1.70/M

Frequently asked questions

Deepseek V3 1 has a larger context window: 163K tokens vs 163K. For long documents, large codebases, or extended agent sessions, the larger context window reduces the need to chunk inputs or summarize history.

Powered by Mem0

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.

Calculate your savingsLearn about Mem0

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