Context Window

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Gemini Omni Flash Preview vs Jamba

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.

Google

Model

Gemini Omni Flash Preview

Image input

Context window

1.0M

1,048,576 tokens · ~786K words

Model page
Ai21

Model

Jamba

Context window

70K

70,000 tokens · ~53K 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.

Gemini Omni Flash Preview1.0M
Jamba70K

Gemini Omni Flash Preview has about 15× the context window of the other in this pair.

Token calculator →Memory savings →

Gemini Omni Flash Preview has 1397% more context capacity (1048K vs 70K tokens). Jamba is 66% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemini Omni Flash Preview. Its 1048K context fits entire documents without chunking (vs 70K).

  • RAG / high-volume retrieval

    Use Jamba. Input tokens are 66% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Gemini Omni Flash Preview. Its 65K 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.

SpecGemini Omni Flash PreviewJamba
Context window1,048,576 tokens (1048K)70,000 tokens (70K)
Max output tokens65,535 tokens (65K)4,096 tokens (4K)
Speed tierFastBalanced
VisionYesNo
Function callingNoNo
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
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.

ProviderGemini Omni Flash Preview inGemini Omni Flash Preview outJamba inJamba out
Azure——$0.500/M$0.700/M
Google$1.50/M$9.00/M——
Google Vertex$1.50/M$9.00/M——
Snowflake————

Frequently asked questions

Gemini Omni Flash Preview has a larger context window: 1048K tokens vs 70K. 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