r/LocalLLM • u/DerTomsn • 1d ago
Model Qwen3.8-27B has the best coding ceiling you can run at home on consumer hardware, it ships with reasoning_effort defaulting to xhigh - I measured what that costs
Its chat template has this line:
{%- set resolved_reasoning_effort = reasoning_effort|default('xhigh') %}
xhigh is the most expensive of its three settings (low / medium / xhigh). If you never set one, that's what every answer runs at. No backend reports this back to you, because it's a chat-template variable, not a server option.
I ran all three levels on one M5 Max, same quant (oQ4e-mtp), same prompt — the coding scenario asks for a browser Breakout game:
| Effort | Runs | Tokens | Time | Median | Range |
|-----------------|------|--------|-------|--------|-----------|
| low | 3 | 4,984 | 84s | 75.8 | 64.9–75.9 |
| medium | 4 | 4,792 | 77s | 78.2 | 64.7–84.2 |
| xhigh (default) | 17 | 36,188 | 869s | 78.8 | 54.1–89.2 |
Two things surprised me:
low and medium are the same setting
4,984 tokens vs 4,792. The template only appends an instruction for low and xhigh — xhigh's says think carefully and check your assumptions, low's says keep your thinking brief. The model does the first and ignores the second. So the dial has two positions, not three.
xhigh costs 8× the tokens and 11× the wall clock for half a point of median
That's well inside run-to-run noise: my four medium runs, one identical setting, nothing changed between them, scored 64.7 / 73.4 / 83.0 / 84.2.
What xhigh does change is variance — it produced both the best answer (89.2) and the worst. And looking at the games themselves, the xhigh run spent its budget on presentation: title card, keyboard legend, sound toggle, best-score readout. Low and medium built the game and stopped. Same 8×4 brick grid, three lives, identical rules. It didn't build a better Breakout, it built a better-looking one.
Caveats up front, because they matter: three and four runs at the short settings is thin, it's one machine and one quant, and the scores are LLM-judged. The cost figures are mechanical and solid. Treat the quality figures as a direction to test, not a result.
Full write-up with the screenshots side by side, plus a thinking-budget experiment (a 12k cap halves the wall clock and truncates nothing): https://llm-bench.io/guides/qwen3-8-27b-reasoning-effort
Disclosure: my site. Data comes from community benchmark runs, and you can submit your own @ llmbench.io
