Context Window

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DeepSeek V3.1 vs Jamba Large 1 6

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
Ai21

Model

Jamba Large 1 6

Context window

256K

256,000 tokens · ~192K 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
Jamba Large 1 6256K

Jamba Large 1 6 has about 1.6× the context window of the other in this pair.

Token calculator →Memory savings →

Jamba Large 1 6 has 56% more context capacity (256K vs 163K tokens). DeepSeek V3.1 is 90% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Jamba Large 1 6. Its 256K context fits entire documents without chunking (vs 163K).

  • RAG / high-volume retrieval

    Use DeepSeek V3.1. Input tokens are 90% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Jamba Large 1 6. Its 256K max output lets you generate complete artifacts in one request.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecDeepSeek V3.1Jamba Large 1 6
Context window163,840 tokens (163K)256,000 tokens (256K)
Max output tokens163,840 tokens (163K)256,000 tokens (256K)
Speed tierBalancedDeep
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
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 outJamba Large 1 6 inJamba Large 1 6 out
Ai21——$2.00/M$8.00/M
Openrouter$0.200/M$0.800/M——

Frequently asked questions

Jamba Large 1 6 has a larger context window: 256K 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.

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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