Context Window

Compare

Granite 4.2 8B vs Kimi K2 7 Code Highspeed

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.

Ibm

Model

Granite 4.2 8B

Tool calling

Context window

131K

131,072 tokens · ~98K words

Model page
Moonshot

Model

Kimi K2 7 Code Highspeed

Context window

262K

262,144 tokens · ~197K 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.

Granite 4.2 8B131K
Kimi K2 7 Code Highspeed262K

Kimi K2 7 Code Highspeed has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Kimi K2 7 Code Highspeed has 100% more context capacity (262K vs 131K tokens). Granite 4.2 8B is 96% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Kimi K2 7 Code Highspeed. Its 262K context fits entire documents without chunking (vs 131K).

  • RAG / high-volume retrieval

    Use Granite 4.2 8B. Input tokens are 96% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGranite 4.2 8BKimi K2 7 Code Highspeed
Context window131,072 tokens (131K)262,144 tokens (262K)
Max output tokens117,964 tokens (117K)N/A
Speed tierFastBalanced
VisionNoNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateAug 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.

ProviderGranite 4.2 8B inGranite 4.2 8B outKimi K2 7 Code Highspeed inKimi K2 7 Code Highspeed out
Gmi——$1.90/M$8.00/M
Openrouter$0.060/M$0.250/M——

Frequently asked questions

Kimi K2 7 Code Highspeed 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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Use a smaller model.
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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.

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