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Gemma 4 31B vs Writer Palmyra X4

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 4 31B

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Google

Model

Writer Palmyra X4

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.

Gemma 4 31B262K
Writer Palmyra X4128K

Gemma 4 31B has about 2× the context window of the other in this pair.

Gemma 4 31B has 104% more context capacity (262K vs 128K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Gemma 4 31B. Its 262K context fits entire documents without chunking (vs 128K).

  • Long output (reports, code files)

    Use Gemma 4 31B. Its 131K 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.

SpecGemma 4 31BWriter Palmyra X4
Context window262,144 tokens (262K)128,000 tokens (128K)
Max output tokens131,072 tokens (131K)8,192 tokens (8K)
Speed tierFastBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingNoNo
Batch APINoNo
Release dateApr 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.

ProviderGemma 4 31B inGemma 4 31B outWriter Palmyra X4 inWriter Palmyra X4 out
Aws Bedrock$2.50/M$10.00/M

Frequently asked questions

Gemma 4 31B has a larger context window: 262K 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.

Powered by Mem0

Use a smaller model.
Get better results.

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.

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