Context Window

Compare

Claude Sonnet 4 vs Gemma 3 4b It Gguf

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 Sonnet 4

Image inputTool calling

Context window

410K

409,600 tokens · ~307K words

Model page
Google

Model

Gemma 3 4b It Gguf

Tool calling

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.

Claude Sonnet 4410K
Gemma 3 4b It Gguf128K

Claude Sonnet 4 has about 3.2× the context window of the other in this pair.

Token calculator →Memory savings →

Claude Sonnet 4 has 220% more context capacity (409K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Claude Sonnet 4. Its 409K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Claude Sonnet 4. Its 32K 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 Sonnet 4Gemma 3 4b It Gguf
Context window409,600 tokens (409K)128,000 tokens (128K)
Max output tokens32,000 tokens (32K)8,192 tokens (8K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APIYesNo
Release dateMay 2025N/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 Sonnet 4 inClaude Sonnet 4 outGemma 3 4b It Gguf inGemma 3 4b It Gguf out
Gmi$3.00/M$15.00/M——
Google Vertex$3.00/M$15.00/M——
Lemonade————
Openrouter$3.00/M$15.00/M——

Frequently asked questions

Claude Sonnet 4 has a larger context window: 409K tokens vs 128K. 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