Context Window

Compare

Gemma 4 31B vs Qwen3 1p7b

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
Alibaba

Model

Qwen3 1p7b

Context window

131K

131,072 tokens · ~98K 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
Qwen3 1p7b131K

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

Token calculator →Memory savings →

Gemma 4 31B has 100% more context capacity (262K vs 131K tokens). Qwen3 1p7b is 73% cheaper on input.

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 131K).

  • RAG / high-volume retrieval

    Use Qwen3 1p7b. Input tokens are 73% 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 4 31BQwen3 1p7b
Context window262,144 tokens (262K)131,072 tokens (131K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierFastFast
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
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 outQwen3 1p7b inQwen3 1p7b out
Fireworks——$0.100/M$0.100/M
Sambanova$0.380/M$1.15/M——

Frequently asked questions

Gemma 4 31B has a larger context window: 262K tokens vs 131K. 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.

Calculate your savingsLearn about Mem0

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