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Gpt 5 1 Chat Latest vs KAT-Coder-Pro V2

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
Kwaipilot

Model

KAT-Coder-Pro V2

Tool calling

Context window

256K

256,000 tokens · ~192K 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
KAT-Coder-Pro V2256K

KAT-Coder-Pro V2 has about 2× the context window of the other in this pair.

KAT-Coder-Pro V2 has 100% more context capacity (256K vs 128K tokens). KAT-Coder-Pro V2 is 76% cheaper on input.

Quick verdicts

Short takeaways — validate with your own workloads.

  • Long document processing

    Use KAT-Coder-Pro V2. Its 256K context fits entire documents without chunking (vs 128K).

  • RAG / high-volume retrieval

    Use KAT-Coder-Pro V2. Input tokens are 76% cheaper — critical when sending large retrieved contexts.

  • Long output (reports, code files)

    Use KAT-Coder-Pro V2. Its 80K 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 LatestKAT-Coder-Pro V2
Context window128,000 tokens (128K)256,000 tokens (256K)
Max output tokens16,384 tokens (16K)80,000 tokens (80K)
Speed tierBalancedBalanced
VisionYesNo
Function callingNoYes
Extended thinkingYesNo
Prompt cachingYesYes
Batch APINoNo
Release dateN/AMar 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 outKAT-Coder-Pro V2 inKAT-Coder-Pro V2 out
Novita$0.300/M$1.20/M
Openai$1.25/M$10.00/M

Frequently asked questions

KAT-Coder-Pro V2 has a larger context window: 256K 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.

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