Context Window

Compare

Databricks Claude Sonnet 4 1 vs Google Gemma 4 31b

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

Databricks Claude Sonnet 4 1

Tool calling

Context window

200K

200,000 tokens · ~150K words

Model page
Google

Model

Google Gemma 4 31b

Image inputTool calling

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.

Databricks Claude Sonnet 4 1200K
Google Gemma 4 31b256K

Google Gemma 4 31b has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Google Gemma 4 31b has 28% more context capacity (256K vs 200K tokens). Google Gemma 4 31b is 95% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Google Gemma 4 31b. Its 256K context fits entire documents without chunking (vs 200K).

  • RAG / high-volume retrieval

    Use Google Gemma 4 31b. Input tokens are 95% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Google Gemma 4 31b. 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.

SpecDatabricks Claude Sonnet 4 1Google Gemma 4 31b
Context window200,000 tokens (200K)256,000 tokens (256K)
Max output tokens64,000 tokens (64K)256,000 tokens (256K)
Speed tierBalancedFast
VisionNoYes
Function callingYesYes
Extended thinkingYesYes
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.

ProviderDatabricks Claude Sonnet 4 1 inDatabricks Claude Sonnet 4 1 outGoogle Gemma 4 31b inGoogle Gemma 4 31b out
Aws Bedrock——$0.140/M$0.400/M
Databricks$3.00/M$15.00/M——

Frequently asked questions

Google Gemma 4 31b has a larger context window: 256K tokens vs 200K. 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