Context Window

Compare

Deepseek R1 0528 Tput vs Kimi K2.5

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.

Deepseek

Model

Deepseek R1 0528 Tput

Tool calling

Context window

128K

128,000 tokens · ~96K words

Model page
Moonshot

Model

Kimi K2.5

Image inputTool 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.

Deepseek R1 0528 Tput128K
Kimi K2.5262K

Kimi K2.5 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Kimi K2.5 has 104% more context capacity (262K vs 128K tokens). Deepseek R1 0528 Tput is 8% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Kimi K2.5. Its 262K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Deepseek R1 0528 Tput. Input tokens are 8% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecDeepseek R1 0528 TputKimi K2.5
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokensN/A262,144 tokens (262K)
Speed tierDeepBalanced
VisionNoYes
Function callingYesYes
Extended thinkingNoYes
Prompt cachingNoYes
Batch APINoNo
Release dateN/AJan 2026

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.

ProviderDeepseek R1 0528 Tput inDeepseek R1 0528 Tput outKimi K2.5 inKimi K2.5 out
Azure——$0.600/M$3.00/M
Baseten——$0.600/M$3.00/M
Moonshot——$0.600/M$3.00/M
Openrouter——$0.600/M$3.00/M
Together Ai$0.550/M$2.19/M$0.500/M$2.80/M

Frequently asked questions

Kimi K2.5 has a larger context window: 262K tokens vs 128K. 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.

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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