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title: KAT-Coder-V2.5-Dev vs Qwen3.6-35B: Is 8x Fewer Tokens...
description: A hands-on comparison of KAT-Coder-V2.5-Dev (a fine-tuned version of Qwen3.6-35B by Quai Pilot) against the base Qwen3.6-35B model for agentic coding tasks....
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og:description: A hands-on comparison of KAT-Coder-V2.5-Dev (a fine-tuned version of Qwen3.6-35B by Quai Pilot) against the base Qwen3.6-35B model for agentic coding tasks....
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# KAT-Coder-V2.5-Dev vs Qwen3.6-35B: Is 8x Fewer Tokens Actually Better for Agentic Coding?

**[Execute Automation](https://daily.dev/sources/executeautomation)** · 15 min read · 0 upvotes · 0 comments

## Summary

A hands-on comparison of KAT-Coder-V2.5-Dev (a fine-tuned version of Qwen3.6-35B by Quai Pilot) against the base Qwen3.6-35B model for agentic coding tasks. The fine-tuned model uses SFT on 127,000 examples plus reinforcement learning to dramatically reduce token usage and tool calls. In a side-by-side test using a Python coding agent on local hardware (via OMLX with 4-bit quantization), KAT-Coder generated only 183K tokens vs 1.4M for Qwen — 7x fewer — with 9.7x less cache reads and 3.4x fewer assistant turns. The key insight is that the base Qwen model re-reads its own plan before almost every file, causing context bloat and RL training instability, while the fine-tuned model reads once and moves on. Benchmark results on SWE-bench verified also show KAT-Coder outperforming the base model on agentic coding tasks.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.youtube.com/watch?v=ox6upguli7U>

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Tags: [#llm](https://daily.dev/tags/llm), [#deep-learning](https://daily.dev/tags/deep-learning), [#ai-coding](https://daily.dev/tags/ai-coding), [#reinforcement-learning](https://daily.dev/tags/reinforcement-learning), [#local-ai](https://daily.dev/tags/local-ai)

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