Context Window

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Kimi K2p6 Fast vs Sonar Pro

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 K2p6 Fast

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Perplexity

Model

Sonar Pro

Image input

Context window

200K

200,000 tokens · ~150K 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 K2p6 Fast262K
Sonar Pro200K

Kimi K2p6 Fast has about 1.3× the context window of the other in this pair.

Token calculator →Memory savings →

Kimi K2p6 Fast has 31% more context capacity (262K vs 200K tokens). Kimi K2p6 Fast is 33% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Kimi K2p6 Fast. Its 262K context fits entire documents without chunking (vs 200K).

  • RAG / high-volume retrieval

    Use Kimi K2p6 Fast. Input tokens are 33% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Kimi K2p6 Fast. Its 262K max output lets you generate complete artifacts in one request.

Full specs

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

SpecKimi K2p6 FastSonar Pro
Context window262,144 tokens (262K)200,000 tokens (200K)
Max output tokens262,144 tokens (262K)8,000 tokens (8K)
Speed tierBalancedBalanced
VisionYesYes
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
Batch APINoNo
Release dateN/AMar 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.

ProviderKimi K2p6 Fast inKimi K2p6 Fast outSonar Pro inSonar Pro out
Fireworks$2.00/M$8.00/M——
Perplexity——$3.00/M$15.00/M

Frequently asked questions

Kimi K2p6 Fast has a larger context window: 262K tokens vs 200K. 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