DeepSeek V4.1 Flash's reasoning_effort scales output quality and tokens ~linearly in tests
zainhas · x · 2026-09-10
Early user testing of DeepSeek-V4.1 Flash's reasoningeffort parameter shows something rare: quality improves nicely and roughly linearly as the setting goes from 1 to 100, and average output token count scales alongside — unlike many models where raising effort yields little.
More from Models
- Kimi K3.1 Pro tops MathArena open models, closing frontier gap to about 6 months — teortaxesTex · 2026-10-26
- Ruff creator Charlie Marsh: frontier models find infinite bugs in any software — charliermarsh · 2026-10-26
- Dev testing one skill across Opus 5.5 and Sol 6.1 finds instruction following 'all over the map' — lucasmeijer · 2026-10-26
- supermemory unveils learner-1, claiming continual learning for any model — julianweisser · 2026-10-26
- Anthropic says Opus 5.5 cuts cost per task by ~40% vs Opus 5 — taufiqintech · 2026-10-26
- AI search APIs ranked: top 12 within 7 points, yet prices differ nearly 7x — FinanceYF5 · 2026-10-26