Context Window

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Gpt 5 6 Luna 2026 07 09 vs Moonshotai Kimi K3

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 6 Luna 2026 07 09

Image inputTool calling

Context window

922K

922,000 tokens · ~692K words

Model page
Moonshot

Model

Moonshotai Kimi K3

Image inputTool 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.

Gpt 5 6 Luna 2026 07 09922K
Moonshotai Kimi K31M

Moonshotai Kimi K3 has about 1.1× the context window of the other in this pair.

Token calculator →Memory savings →

Moonshotai Kimi K3 has 8% more context capacity (1000K vs 922K tokens). Gpt 5 6 Luna 2026 07 09 is 93% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Moonshotai Kimi K3. Its 1000K context fits entire documents without chunking (vs 922K).

  • RAG / high-volume retrieval

    Use Gpt 5 6 Luna 2026 07 09. Input tokens are 93% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecGpt 5 6 Luna 2026 07 09Moonshotai Kimi K3
Context window922,000 tokens (922K)1,000,000 tokens (1000K)
Max output tokens128,000 tokens (128K)N/A
Speed tierBalancedBalanced
VisionYesYes
Function callingYesYes
Extended thinkingYesYes
Prompt cachingYesYes
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.

ProviderGpt 5 6 Luna 2026 07 09 inGpt 5 6 Luna 2026 07 09 outMoonshotai Kimi K3 inMoonshotai Kimi K3 out
Aws Bedrock——$3.00/M$15.00/M
Azure$0.200/M$1.20/M——

Frequently asked questions

Moonshotai Kimi K3 has a larger context window: 1000K tokens vs 922K. 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