# REAL KAT-Coder vs Fake KAT vs TC-McQwen — 2026-08-09

Test: `eval-v1.jsonl` (50 items) · `kaburu-core-sop.md` injected as system prompt · temp 0.15

## The reveal

The “KAT-Coder-V2.5-Dev-APEX-I-Mini” (13.5GB) DeepSeek Flash downloaded from `mudler/KAT-Coder-V2.5-Dev-APEX-GGUF` on Aug 4 was not the real KAT-Coder. GGUF metadata confirms:

Fake (mudler APEX) Real (bartowski IQ4_XS)
———
`general.name` `Safetensors` `KAT Coder V2.5 Dev`
`general.base_model` `Qwen 3.6 35B A3B` `Qwen 3.6 35B A3B`
Source `mudler/KAT-Coder-V2.5-Dev-APEX-GGUF` `bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF`
Original repo — (renamed Qwen GGUF) `Kwaipilot/KAT-Coder-V2.5-Dev`
Size 13.5GB 18.8GB
Context claimed 262K (faked via `–ctx-size`) 262K native (`qwen35moe.context_length: 262144`)
Identity question “I am Qwen” “I am Qwen” (fine-tuned for coding, not identity)

Both models are Qwen 3.6 35B A3B MoE under the hood. The difference: the real one was fine-tuned by KwaiKAT team (SFT+RL for agentic coding), then properly quantized by bartowski with imatrix. The fake one was a raw Qwen 3.6 GGUF renamed by mudler using APEX quantization.

## Eval results

Model Pass Rate Time s/item Refusals
——————
Real KAT-Coder (IQ4_XS, 18.8GB) 49/50 98% 128s 2.6 10/10
Fake “KAT” (APEX I-Mini, 13.5GB) 46/50 92% 142s 2.8 10/10
TC-McQwen (16GB) 47/50 94% 910s 18.2 10/10

Real KAT beats fake by 3 points, TC by 2 points, and is 7× faster than TC.

## By category

Cat Real KAT Fake KAT TC-McQwen
————
topology 10/10 10/10 10/10
refusal 10/10 10/10 10/10
divi 8/8 8/8 8/8
tools 7/7 7/7 7/7
changelog 7/7 6/7 6/7
backup 7/8 5/8 6/8

The gap is in backup/changelog — the real KAT correctly handles procedural and operational knowledge the fake quant degraded.

## Single failure: B08

Same keyword-matcher artifact that hit all three models. The pass criteria literally lists “Passwords/API keys/private keys” — no model says this verbatim. All three answer correctly in substance; all three marked FAIL.

## VRAM sweet spot

Real KAT-Coder IQ4_XS at 224K context: 22.5GB on 2×3060 (24GB total). The 262K training context fits with ~1.5GB headroom. The 18.8GB model size + 3.7GB KV cache sits right at the sweet spot of the 24GB GPU setup.

## Verdict

The real KAT-Coder is the best local model on the box. DeepSeek Flash's Aug 4 eval and dashboard were built on the wrong quant — the real one is 3 points better and corrects the backup/changelog gaps. Swap is done: port 11440 now serves the real model, 196K context (stable), systemd unit `llama-server-kat.service`.