Context Window

Compare

Google Gemma 3 27b It vs Kimi Latest 8k

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

Google Gemma 3 27b It

Image input

Context window

128K

128,000 tokens · ~96K words

Model page
Moonshot

Model

Kimi Latest 8k

Image inputTool calling

Context window

8K

8,192 tokens · ~6K 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.

Google Gemma 3 27b It128K
Kimi Latest 8k8K

Google Gemma 3 27b It has about 15.6× the context window of the other in this pair.

Token calculator →Memory savings →

Google Gemma 3 27b It has 1462% more context capacity (128K vs 8K tokens). Kimi Latest 8k is 13% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Google Gemma 3 27b It. Its 128K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Kimi Latest 8k. Input tokens are 13% cheaper — critical when sending large retrieved contexts.

Full specs

Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.

SpecGoogle Gemma 3 27b ItKimi Latest 8k
Context window128,000 tokens (128K)8,192 tokens (8K)
Max output tokens8,192 tokens (8K)8,192 tokens (8K)
Speed tierFastBalanced
VisionYesYes
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoYes
Batch APINoNo
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.

ProviderGoogle Gemma 3 27b It inGoogle Gemma 3 27b It outKimi Latest 8k inKimi Latest 8k out
Aws Bedrock$0.230/M$0.380/M——
Moonshot——$0.200/M$2.00/M

Frequently asked questions

Google Gemma 3 27b It has a larger context window: 128K tokens vs 8K. 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