Context Window

Compare

Granite 3 3 8b vs Kimi K2 Instruct 0905

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 3 3 8b

Tool calling

Context window

8K

8,192 tokens · ~6K words

Model page
Moonshot

Model

Kimi K2 Instruct 0905

Tool calling

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 3 3 8b8K
Kimi K2 Instruct 0905262K

Kimi K2 Instruct 0905 has about 32× the context window of the other in this pair.

Token calculator →Memory savings →

Kimi K2 Instruct 0905 has 3100% more context capacity (262K vs 8K tokens). Granite 3 3 8b is 60% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Kimi K2 Instruct 0905. Its 262K context fits entire documents without chunking (vs 8K).

  • RAG / high-volume retrieval

    Use Granite 3 3 8b. Input tokens are 60% 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 3 3 8bKimi K2 Instruct 0905
Context window8,192 tokens (8K)262,144 tokens (262K)
Max output tokensN/A262,144 tokens (262K)
Speed tierFastBalanced
VisionNoNo
Function callingYesYes
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.

ProviderGranite 3 3 8b inGranite 3 3 8b outKimi K2 Instruct 0905 inKimi K2 Instruct 0905 out
Baseten——$0.600/M$2.50/M
Deepinfra——$0.500/M$2.00/M
Fireworks——$0.600/M$2.50/M
Groq——$1.00/M$3.00/M
Ibm Watsonx$0.200/M$0.200/M——
Replicate$0.030/M$0.250/M——
Together Ai——$1.00/M$3.00/M

Frequently asked questions

Kimi K2 Instruct 0905 has a larger context window: 262K 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.

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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

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