Context Window

Compare

Kimi K2.6 (free) vs Qwen Turbo 2025 04 28

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 K2.6 (free)

Image inputTool calling

Context window

262K

262,144 tokens · ~197K words

Model page
Alibaba

Model

Qwen Turbo 2025 04 28

Tool calling

Context window

1M

1,000,000 tokens · ~750K 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 K2.6 (free)262K
Qwen Turbo 2025 04 281M

Qwen Turbo 2025 04 28 has about 3.8× the context window of the other in this pair.

Token calculator →Memory savings →

Qwen Turbo 2025 04 28 has 281% more context capacity (1000K vs 262K tokens).

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Qwen Turbo 2025 04 28. Its 1000K context fits entire documents without chunking (vs 262K).

Full specs

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

SpecKimi K2.6 (free)Qwen Turbo 2025 04 28
Context window262,144 tokens (262K)1,000,000 tokens (1000K)
Max output tokensN/A16,384 tokens (16K)
Speed tierBalancedBalanced
VisionYesNo
Function callingYesYes
Extended thinkingYesYes
Prompt cachingNoNo
Batch APINoNo
Release dateApr 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.

ProviderKimi K2.6 (free) inKimi K2.6 (free) outQwen Turbo 2025 04 28 inQwen Turbo 2025 04 28 out
Alibaba Cloud——$0.050/M$0.200/M

Frequently asked questions

Qwen Turbo 2025 04 28 has a larger context window: 1000K tokens vs 262K. 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.

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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