Context Window

Compare

Deep Research Max Preview 04 2026 vs Fw Kimi K2 7 Code

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.

Google

Model

Deep Research Max Preview 04 2026

Image input

Context window

131K

131,072 tokens · ~98K words

Model page
Moonshot

Model

Fw Kimi K2 7 Code

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.

Deep Research Max Preview 04 2026131K
Fw Kimi K2 7 Code262K

Fw Kimi K2 7 Code has about 2× the context window of the other in this pair.

Token calculator →Memory savings →

Fw Kimi K2 7 Code has 100% more context capacity (262K vs 131K tokens). Fw Kimi K2 7 Code is 47% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use Fw Kimi K2 7 Code. Its 262K context fits entire documents without chunking (vs 131K).

  • RAG / high-volume retrieval

    Use Fw Kimi K2 7 Code. Input tokens are 47% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use Fw Kimi K2 7 Code. Its 262K 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.

SpecDeep Research Max Preview 04 2026Fw Kimi K2 7 Code
Context window131,072 tokens (131K)262,144 tokens (262K)
Max output tokens65,536 tokens (65K)262,144 tokens (262K)
Speed tierBalancedBalanced
VisionYesYes
Function callingNoYes
Extended thinkingNoYes
Prompt cachingNoYes
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.

ProviderDeep Research Max Preview 04 2026 inDeep Research Max Preview 04 2026 outFw Kimi K2 7 Code inFw Kimi K2 7 Code out
Azure——$1.05/M$4.40/M
Google$2.00/M$12.00/M——

Frequently asked questions

Fw Kimi K2 7 Code has a larger context window: 262K tokens vs 131K. 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