Context Window

Compare

Learnlm 1 5 Pro Experimental vs Moonshot V1 8k Vision Preview

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

Learnlm 1 5 Pro Experimental

Image inputTool calling

Context window

33K

32,767 tokens · ~25K words

Model page
Moonshot

Model

Moonshot V1 8k Vision Preview

Image inputTool calling

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.

Learnlm 1 5 Pro Experimental33K
Moonshot V1 8k Vision Preview8K

Learnlm 1 5 Pro Experimental has about 4× the context window of the other in this pair.

Token calculator →Memory savings →

Learnlm 1 5 Pro Experimental has 299% more context capacity (32K vs 8K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Learnlm 1 5 Pro Experimental. Its 32K context fits entire documents without chunking (vs 8K).

Full specs

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

SpecLearnlm 1 5 Pro ExperimentalMoonshot V1 8k Vision Preview
Context window32,767 tokens (32K)8,192 tokens (8K)
Max output tokens8,192 tokens (8K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingNoNo
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.

ProviderLearnlm 1 5 Pro Experimental inLearnlm 1 5 Pro Experimental outMoonshot V1 8k Vision Preview inMoonshot V1 8k Vision Preview out
Google————
Moonshot——$0.200/M$2.00/M

Frequently asked questions

Learnlm 1 5 Pro Experimental has a larger context window: 32K 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.

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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