Context Window

Compare

Ft:gpt 3 5 vs Jamba 1 5 Large

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.

Openai

Model

Ft:gpt 3 5

Context window

16K

16,385 tokens · ~12K words

Model page
Ai21

Model

Jamba 1 5 Large

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.

Ft:gpt 3 516K
Jamba 1 5 Large256K

Jamba 1 5 Large has about 15.6× the context window of the other in this pair.

Token calculator →Memory savings →

Jamba 1 5 Large has 1462% more context capacity (256K vs 16K tokens). Jamba 1 5 Large is 33% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Jamba 1 5 Large. Its 256K context fits entire documents without chunking (vs 16K).

  • RAG / high-volume retrieval

    Use Jamba 1 5 Large. Input tokens are 33% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Jamba 1 5 Large. 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.

SpecFt:gpt 3 5Jamba 1 5 Large
Context window16,385 tokens (16K)256,000 tokens (256K)
Max output tokens4,096 tokens (4K)256,000 tokens (256K)
Speed tierBalancedDeep
VisionNoNo
Function callingNoNo
Extended thinkingNoNo
Prompt cachingNoNo
Batch APIYesNo
Release dateN/AN/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.

ProviderFt:gpt 3 5 inFt:gpt 3 5 outJamba 1 5 Large inJamba 1 5 Large out
Ai21——$2.00/M$8.00/M
Google Vertex——$2.00/M$8.00/M
Openai$3.00/M$6.00/M——
Snowflake————

Frequently asked questions

Jamba 1 5 Large has a larger context window: 256K tokens vs 16K. 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