Extremely unscientific benchmark on local models 2026-10-24

2026-10-24 21:10:23 - No Comments

This is the "unscientific" edition: nine local models through the same 99 agentic coding tasks, 3 repeats each, on the opencode 1.18.16 harness — fixed 300s timeout, 3-repair budget. No cross-prompt averaging, no statistical treatment, and the boxes were under real load. The goal was never a league table; it was to find what this rig can actually run at the top end.

That ceiling just moved. Two quants of Qwen3.8-Flash-Next — a 125B Mixture-of-Experts with only a few billion params active per token — took the top two seats:

The catch is speed. Those two quants sit at 10.5 / 12.9 tok/s — bottom half of the group — because a 125B model doesn't decode fast on prosumer hardware. The older, smaller ones (nemotron 46.7, laguna 34.1, ornith 34.6 tok/s) run faster but land lower on pass-rate (73–92%). So the honest read is a trade, not a victory lap: when you need the model to actually get the job done right, Flash-Next wins — and you pay for it in wait time. For high-throughput single-shot, the smaller models still have the edge.

Treat the tok/s column with the same skepticism as everything else below.

Local model benchmark — harness: opencode 1.18.16

Model Pass rate First-try validity Avg repairs Avg generation Avg execution Tokens/sec
laguna-xs.2:latest 91/99 (91.9%) 100.0% 0.00 93.31 s 224 ms 34.1
muse-glimmer:30b 87/99 (87.9%) 100.0% 0.00 2m 11s 172 ms 11.6
nemotron-3.5-lightning:30b 78/99 (78.8%) 98.9% 0.01 2m 45s 124 ms 46.7
ornith:35b 72/99 (72.7%) 100.0% 0.00 49.96 s 3.40 s 34.6
qwen3.6:27b 85/99 (85.9%) 100.0% 0.00 2m 18s 129 ms 3.9
qwen3.6:35b 78/99 (78.8%) 100.0% 0.00 53.89 s 3.43 s 23.9
qwen3.8-flash-next-coder-iq1_m 97/99 (98.0%) 99.0% 0.01 81.92 s 158 ms 10.5
qwen3.8-flash-next-iq3_s 99/99 (100.0%) 100.0% 0.00 88.81 s 173 ms 12.9
qwen3.8:27b 91/99 (91.9%) 100.0% 0.00 72.79 s 129 ms 15.4

Run dates 2026-08-13 to 2026-10-24 · repeats 3 · exec timeout 300 s · repair budget 3 · prompt 1.0.0

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