I increased test time 4 times (same 44 tasks, 4 agents in parallel, auto-graded):
🥇 DeepSeek-V4.1-Flash max + DeepSeek Harness: 41/44 in 87 min
🥈 DeepSeek-V4.1-Flash max + Terminus-2: 41/44 in 204 min (was 36)
🥉 DeepSeek-V4.1-Flash max + DeepSeek Harness + "work efficiently" prompt: 39/44 in 54 min
What changed:
• Max effort was mostly too slow, not too dumb. With more time it catches up.
• Low effort gained nothing. Its misses are real errors, not timeouts.
• The efficiency prompt increased speed but decreased quality: about 40% less time in both harnesses, but 2–3 fewer tasks solved.
• The harness matters as much as the model: same DeepSeek, same 41/44, 204 min in Terminus-2 vs 87 min in DeepSeek Harness.
Time-limited agentic benchmark on DGX Spark (44 tasks, 4 agents in parallel, auto-graded):
🥇 DeepSeek-V4.1-Flash (low effort, 4 Sparks): 40/44
🥈 GLM-5.3 (low effort, 4 Sparks): 37/44
GLM-5.3-Flash (2 Sparks): 33/44
MiniMax-M3.1-Flash (4 Sparks): 26/44 (but best vision)
Low effort edged out max for both DS and GLM. Agent tasks are time-bound, so thinking less finishes more - though max wasn't far behind.
Caveat: these are practical, moderately hard tasks (TB 2.1 subset: 14 medium, 6 hard). On harder problems without a clock, higher effort may still win.