Context Window

Compare

Claude Opus 4.7 (Fast) vs Jamba Mini 1 7

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.

Anthropic

Model

Claude Opus 4.7 (Fast)

Image inputTool calling

Context window

1M

1,000,000 tokens · ~750K words

Model page
Ai21

Model

Jamba Mini 1 7

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.

Claude Opus 4.7 (Fast)1M
Jamba Mini 1 7256K

Claude Opus 4.7 (Fast) has about 3.9× the context window of the other in this pair.

Token calculator →Memory savings →

Claude Opus 4.7 (Fast) has 290% more context capacity (1000K vs 256K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude Opus 4.7 (Fast). Its 1000K context fits entire documents without chunking (vs 256K).

  • Long output (reports, code files)

    Use Jamba Mini 1 7. 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.

SpecClaude Opus 4.7 (Fast)Jamba Mini 1 7
Context window1,000,000 tokens (1000K)256,000 tokens (256K)
Max output tokens128,000 tokens (128K)256,000 tokens (256K)
Speed tierDeepFast
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APIYesNo
Release dateMay 2026N/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.

ProviderClaude Opus 4.7 (Fast) inClaude Opus 4.7 (Fast) outJamba Mini 1 7 inJamba Mini 1 7 out
Ai21——$0.200/M$0.400/M

Frequently asked questions

Claude Opus 4.7 (Fast) has a larger context window: 1000K tokens vs 256K. 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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Use a smaller model.
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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