reports/qwopus-coder-27b.md

Qwopus3.6-27B-Coder, four legs measured: a real #2 quality model, a true 100-tps claim — and the cleanest synthetic-benchmark mirage the reality anchor has caught

Rig: one RTX 5090 32GB · llama.cpp b9562 (--jinja, temp 0, think-off) · Q5_K_M (the release's claimed quant) Model: Jackrong/Qwopus3.6-27B-Coder-MTP-GGUF — community coder SFT of Qwopus3.6-27B-v2 (itself a Qwen3.6-27B lineage merge), trained on Claude-Opus trace-inversion data plus Hermes agent reasoning traces, with a natively fine-tuned MTP head. Benched the day of release (we were download #1). Claims tested: 67% SWE-bench Verified (thinking off), 100 tok/s on a single 5090.

Leg 1 — quality: the real deal

Standard 5-task treatment (think-off, 50% MMLU/HellaSwag sampling, temp 0):

MMLU	ARC-C	HellaSwag	GSM8K	HumanEval	q_avg
87.5	96.8	95.2	97.5	93.3	94.1

#2 on the Thinking-OFF leaderboard — above its own base family (Qwen3.6-27B Q6_K, 94.0) at a smaller quant, a hair under Gemma 4 31B (94.2). The coder SFT gained code (+0.6 HumanEval) and math (+0.2 GSM8K) while giving back only noise-level MMLU (−0.4). No general-quality tax. Base decode: 70.5 tok/s tg128, 18.2 GiB, 20 GB VRAM peak.

Leg 2 — MTP: the 100-tps claim holds; the head is weaker than the original

Live-server A/B, draft-depth 2, same protocol as our published Qwen3.6-27B-MTP numbers:

workload	base	+MTP	speedup	Qwen3.6 original head
prose	70.3	98.0	1.4x	1.8x
Q&A	70.2	95.7	1.4x	1.9x
code	70.2	97.9	1.4x	2.0x
JSON	70.2	112.4	1.6x	2.2x
repetitive	70.2	114.5	1.6x	2.2x

"100 tok/s on a single 5090" — true (96–114 typical). But the "natively finetuned" head accepts worse than the original: 1.4–1.6x flat, where the Qwen3.6 head climbs 1.8→2.2x with output predictability. Net effect: the base Qwen3.6-27B-MTP at Q6_K is still absolutely faster (113–137 tok/s) than the coder at Q5_K_M. The SFT presumably shifted the output distribution away from what the head predicts best — fine-tuning a model means re-earning its drafter's acceptance. (Caveat: tested at draft-depth 2 for protocol comparability; a depth sweep might flatter the new head.)

Leg 3 — Agentic Score: a perfect 100, and why we didn't celebrate

Gate PASS, then 40/40 tasks, tool-efficiency 1.00, stability 100%, 195 tokens/task — Agentic Score 100.0, new #1 on the agentic-score-leaderboard, displacing Qwen3.6-27B (98.6). It is also the leanest agent we've measured (195 tok/task vs Qwen's 285).

But this model is trained on Hermes agent traces, and our harness speaks that dialect — partially in-distribution. A perfect score from a model trained on your bench's flavor is a hypothesis, not a result. Which is what the anchor is for.

Leg 4 — the reality anchor: the mirage, measured

Same 30 SWE-bench Verified bugs, real repo tools in Docker, official harness grading:

	synthetic	real resolve	empty patches
Qwopus3.6-27B-Coder	100.0	17/30 (57%)	8
Qwen3.6-27B (its base family)	98.6	19/30 (63%)	8
Qwopus-GLM-18B (sibling)	97.1	12/30 (40%)	14

The perfect synthetic score loses to its own base on real bugs. Not the Nemotron failure mode (it commits patches at the same rate as Qwen — the give-up tell is absent); this is the second, subtler failure mode: training-data overlap inflating the synthetic number without moving real capability. The 8-model anchor correlation actually strengthened with this point (Pearson r=0.59, Spearman ρ=0.76 — up from 0.50/0.68), and the board README now documents both catch modes.

The 67% claim doesn't reproduce: our 30-bug subset is easier than full Verified (smallest-patch selection — our baselines run high on it), and the coder scores 57% here. Wide CI at n=30, but a claim made on the harder full set should not lose ten points on the easier subset.

Verdict

A genuinely strong release wrapped in prose that outruns it: real top-2 general quality at 19 GB, a true 100-tps MTP figure, the leanest tool-calling we've measured — and a coding headline that the official harness doesn't support. Donald keeps Qwen3.6-27B (better real-bug resolve, calibrated board score, no in-distribution asterisk).

Worth it if you want a fast, lean, snappy local agent for interactive coding — the thinking-off experience their post describes is real. Not if you're choosing models by their SWE-bench claims — measure, or read someone who did.

Reproduce

Standard treatment via run_treatment.sh · scripts/bench_mtp_workload.sh · gate_and_run.sh (agentic) · scripts/swebench_batch_{gen,grade}.sh (anchor) · chart: scripts/chart_qwopus_coder.py. Board + anchor raw: agentic-score-leaderboard.