Context Window

Compare

Gpt 5 1 Chat Latest vs Kimi K2.6

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.

Openai

Model

Gpt 5 1 Chat Latest

Image input

Context window

128K

128,000 tokens · ~96K words

Model page
Moonshot

Model

Kimi K2.6

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.

Gpt 5 1 Chat Latest128K
Kimi K2.6262K

Kimi K2.6 has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Kimi K2.6 has 104% more context capacity (262K vs 128K tokens). Kimi K2.6 is 24% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Kimi K2.6. Its 262K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use Kimi K2.6. Input tokens are 24% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Kimi K2.6. Its 65K 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.

SpecGpt 5 1 Chat LatestKimi K2.6
Context window128,000 tokens (128K)262,144 tokens (262K)
Max output tokens16,384 tokens (16K)65,536 tokens (65K)
Speed tierBalancedBalanced
VisionYesYes
Function callingNoYes
Extended thinkingYesYes
Prompt cachingYesYes
Batch APINoNo
Release dateN/AApr 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.

ProviderGpt 5 1 Chat Latest inGpt 5 1 Chat Latest outKimi K2.6 inKimi K2.6 out
Moonshot——$0.950/M$4.00/M
Openai$1.25/M$10.00/M——

Frequently asked questions

Kimi K2.6 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.

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