Context Window

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Kimi K2p7 Code Fast vs Llama 3 1 405b

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.

Moonshot

Model

Kimi K2p7 Code Fast

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Meta

Model

Llama 3 1 405b

Context window

4K

4,096 tokens · ~3K 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.

Kimi K2p7 Code Fast262K
Llama 3 1 405b4K

Kimi K2p7 Code Fast has about 64× the context window of the other in this pair.

Token calculator →Memory savings →

Kimi K2p7 Code Fast has 6300% more context capacity (262K vs 4K tokens). Kimi K2p7 Code Fast is 45% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Kimi K2p7 Code Fast. Its 262K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Kimi K2p7 Code Fast. Input tokens are 45% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecKimi K2p7 Code FastLlama 3 1 405b
Context window262,144 tokens (262K)4,096 tokens (4K)
Max output tokens262,144 tokens (262K)N/A
Speed tierBalancedDeep
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
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.

ProviderKimi K2p7 Code Fast inKimi K2p7 Code Fast outLlama 3 1 405b inLlama 3 1 405b out
Fireworks$1.90/M$8.00/M——
Together Ai——$3.50/M$3.50/M

Frequently asked questions

Kimi K2p7 Code Fast has a larger context window: 262K tokens vs 4K. 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