Context Window

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Gemma 4 31B vs Kimi K2 0711

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
Moonshot

Model

Kimi K2 0711

Tool calling

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
Kimi K2 0711131K

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). Gemma 4 31B is 24% 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 Gemma 4 31B. Input tokens are 24% 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 31BKimi K2 0711
Context window262,144 tokens (262K)131,072 tokens (131K)
Max output tokens131,072 tokens (131K)131,072 tokens (131K)
Speed tierFastBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateApr 2026Jul 2025

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 outKimi K2 0711 inKimi K2 0711 out
Deepinfra——$0.500/M$2.00/M
Fireworks——$0.600/M$2.50/M
Hyperbolic——$2.00/M$2.00/M
Novita——$0.570/M$2.30/M
Sambanova$0.380/M$1.15/M——
Together Ai——$1.00/M$3.00/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.

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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