Context Window

Compare

Gemma 7b It vs Google Gemma 3 27b It

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

Gemma 7b It

Context window

8K

8,192 tokens · ~6K words

Model page
Google

Model

Google Gemma 3 27b It

Image input

Context window

128K

128,000 tokens · ~96K 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.

Gemma 7b It8K
Google Gemma 3 27b It128K

Google Gemma 3 27b It has about 15.6× the context window of the other in this pair.

Token calculator →Memory savings →

Google Gemma 3 27b It has 1462% more context capacity (128K vs 8K tokens). Gemma 7b It is 78% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Google Gemma 3 27b It. Its 128K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Gemma 7b It. Input tokens are 78% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGemma 7b ItGoogle Gemma 3 27b It
Context window8,192 tokens (8K)128,000 tokens (128K)
Max output tokens8,192 tokens (8K)8,192 tokens (8K)
Speed tierFastFast
VisionNoYes
Function callingNoNo
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.

ProviderGemma 7b It inGemma 7b It outGoogle Gemma 3 27b It inGoogle Gemma 3 27b It out
Anyscale$0.150/M$0.150/M——
Aws Bedrock——$0.230/M$0.380/M
Fireworks$0.200/M$0.200/M——
Groq$0.050/M$0.080/M——

Frequently asked questions

Google Gemma 3 27b It has a larger context window: 128K 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