Story · r/LocalLLaMA
This finance-model benchmark card is more useful for what it discloses than for who "wins" (r/LocalLLaMA)
Reddit thread · Story page
The Ling-3.0-flash-Fin card discloses what got scored: a common ReAct scaffold with web search and Python, Claude Code 2.1.173 driving LibreOffice 25.8.7, turn limits, timeouts, and a GPT-5 judge. The unit under test is the agent system, not the checkpoint, and the post reads as a checklist for any agent benchmark.
In plain words
- A finance comparison revealed that scores reflected complete software setups, not only the artificial intelligence systems being compared.
- Each setup used different search access, spreadsheet software, interaction limits, time limits, and settings.
- Some scores came from public sources, while others came from private tests and privately checked answers.
- Several tests or specialized finance settings were not yet public, which limits independent checking.
- People choosing finance software should compare the full testing setup before treating its score as a fair ranking.
Appeared in
- Maersk's 100,000 corrections, preference-trap evals, and Tencent's Hy4 Preview
Aug 31, 2026 · in the sections
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