Context Window

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Gpt 35 Turbo Instruct 0914 vs Moonshotai 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.

Openai

Model

Gpt 35 Turbo Instruct 0914

Context window

4K

4,097 tokens · ~3K words

Model page
Moonshot

Model

Moonshotai 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.

Gpt 35 Turbo Instruct 09144K
Moonshotai Kimi K2 5262K

Moonshotai Kimi K2 5 has about 64× the context window of the other in this pair.

Token calculator →Memory savings →

Moonshotai Kimi K2 5 has 6298% more context capacity (262K vs 4K tokens). Moonshotai Kimi K2 5 is 52% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Moonshotai Kimi K2 5. Its 262K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Moonshotai Kimi K2 5. Input tokens are 52% 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 35 Turbo Instruct 0914Moonshotai Kimi K2 5
Context window4,097 tokens (4K)262,144 tokens (262K)
Max output tokensN/A262,144 tokens (262K)
Speed tierBalancedBalanced
VisionNoYes
Function callingNoYes
Extended thinkingNoNo
Prompt cachingNoNo
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 35 Turbo Instruct 0914 inGpt 35 Turbo Instruct 0914 outMoonshotai Kimi K2 5 inMoonshotai Kimi K2 5 out
Aws Bedrock——$0.720/M$3.60/M
Azure$1.50/M$2.00/M——

Frequently asked questions

Moonshotai Kimi K2 5 has a larger context window: 262K tokens vs 4K. 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