PulseAugur
EN
LIVE 21:29:14

LLM benchmarking issues fixed by adjusting 'thinking mode' parameters

By PulseAugur Editorial · [1 sources] ·

A developer encountered issues benchmarking three large language models, Kimi K2.5, MiniMax M2.5, and Gemma 4, initially deeming them broken due to low scores or errors. The root cause was identified as a default "thinking mode" that consumed the token budget before generating output. Adjusting specific parameters like "reasoning: {"effort": "none"}" or "include_reasoning: false" resolved these issues, significantly improving the models' benchmark performance. AI

IMPACT Highlights the importance of understanding model-specific configurations for accurate benchmarking and efficient agent development.

RANK_REASON Blog post detailing a specific technical issue and solution encountered during LLM benchmarking. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM benchmarking issues fixed by adjusting 'thinking mode' parameters

COVERAGE [1]

  1. dev.to — LLM tag TIER_1 English(EN) · Vilius ·

    How we almost wrote off 3 models as broken — the thinking-mode tax

    <h1> How we almost wrote off 3 models as broken — the thinking-mode tax </h1> <p><em>By Vilius Vystartas | May 2026</em></p> <p>Three models scored under 15% in my first benchmark run. Kimi K2.5: 10%. MiniMax M2.5: 15%. Gemma 4: HTTP 400 on every call. I almost excluded them as b…

RELATED ENTITIES

RELATED TOPICS