Context Window

Compare

DeepSeek V3.2 Exp vs Meta Llama 3 70b Instruct

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 Exp

Tool calling

Context window

164K

163,840 tokens · ~123K words

Model page
Meta

Model

Meta Llama 3 70b Instruct

Context window

8K

8,192 tokens · ~6K 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 Exp164K
Meta Llama 3 70b Instruct8K

DeepSeek V3.2 Exp has about 20× the context window of the other in this pair.

Token calculator →Memory savings →

DeepSeek V3.2 Exp has 1900% more context capacity (163K vs 8K tokens). DeepSeek V3.2 Exp is 69% 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 8K).

  • RAG / high-volume retrieval

    Use DeepSeek V3.2 Exp. Input tokens are 69% 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 V3.2 ExpMeta Llama 3 70b Instruct
Context window163,840 tokens (163K)8,192 tokens (8K)
Max output tokens163,840 tokens (163K)N/A
Speed tierBalancedDeep
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateSep 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 V3.2 Exp inDeepSeek V3.2 Exp outMeta Llama 3 70b Instruct inMeta Llama 3 70b Instruct out
Novita$0.270/M$0.410/M——
Openrouter$0.270/M$0.410/M——
Together Ai——$0.880/M$0.880/M

Frequently asked questions

DeepSeek V3.2 Exp has a larger context window: 163K tokens vs 8K. 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