Context Window

Compare

In Moonshotai Kimi K3 1 vs Mistral 7b

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

In Moonshotai Kimi K3 1

Image inputTool calling

Context window

869K

868,928 tokens · ~652K words

Model page
Mistral

Model

Mistral 7b

Context window

4K

4,096 tokens · ~3K 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.

In Moonshotai Kimi K3 1869K
Mistral 7b4K

In Moonshotai Kimi K3 1 has about 212.1× the context window of the other in this pair.

Token calculator →Memory savings →

In Moonshotai Kimi K3 1 has 21114% more context capacity (868K vs 4K tokens). Mistral 7b is 97% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use In Moonshotai Kimi K3 1. Its 868K context fits entire documents without chunking (vs 4K).

  • RAG / high-volume retrieval

    Use Mistral 7b. Input tokens are 97% cheaper — critical when sending large retrieved contexts.

Full specs

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

SpecIn Moonshotai Kimi K3 1Mistral 7b
Context window868,928 tokens (868K)4,096 tokens (4K)
Max output tokens131,072 tokens (131K)N/A
Speed tierBalancedFast
VisionYesNo
Function callingYesNo
Extended thinkingYesNo
Prompt cachingYesNo
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.

ProviderIn Moonshotai Kimi K3 1 inIn Moonshotai Kimi K3 1 outMistral 7b inMistral 7b out
Aws Bedrock$4.40/M$22.00/M——
Fireworks——$0.200/M$0.200/M
Openrouter——$0.130/M$0.130/M
Perplexity——$0.070/M$0.280/M
Snowflake————

Frequently asked questions

In Moonshotai Kimi K3 1 has a larger context window: 868K 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.

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