Compare
GLM 4 32B vs KAT-Coder-Pro V2.5
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.
Model
KAT-Coder-Pro V2.5
Context window
256K
256,000 tokens · ~192K words
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.
KAT-Coder-Pro V2.5 has about 2× the context window of the other in this pair.
KAT-Coder-Pro V2.5 has 100% more context capacity (256K vs 128K tokens).
Quick verdicts
Short takeaways — validate with your own workloads.
Long document processing
Use KAT-Coder-Pro V2.5. Its 256K context fits entire documents without chunking (vs 128K).
Full specs
Context, output, capabilities, and dates. Green highlights the favorable value where we compute a winner.
| Spec | GLM 4 32B | KAT-Coder-Pro V2.5 |
|---|---|---|
| Context window | 128,000 tokens (128K) | 256,000 tokens (256K) |
| Max output tokens | N/A | 80,000 tokens (80K) |
| Speed tier | Balanced | Balanced |
| Vision | No | No |
| Function calling | Yes | Yes |
| Extended thinking | No | No |
| Prompt caching | No | Yes |
| Batch API | No | No |
| Release date | Jul 2025 | Jul 2026 |
Frequently asked questions
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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.
Example: a multi-turn chat session
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