Community compares local LLMs for agentic workflows using tool-eval-bench
A GitHub user published an interactive comparison report evaluating local LLMs for agentic workflows, using the tool-eval-bench benchmark (84 scenarios, 16 categories, 8 trials). The report targets single DGX Spark or other 96-128GB rigs and covers multi-turn tool orchestration, function calling, and autonomous planning as exercised by Hermes Agent.
Entities
Related
LLM comparison dashboard for quality, latency, and cost
A dashboard that lets users test LLMs on their own data, comparing quality, latency, and cost side by side. It runs on Nebius Serverless and helps developers choose the best model for their specific use case rather than relying on leaderboards.
Muse Spark 1.1 benchmarked against top models on Artificial Analysis
A user shared an Artificial Analysis comparison of Muse Spark 1.1 (xhigh) against models like Gemini 3.5 Flash, Claude Fable 5, and GPT-5.6 Sol, evaluating intelligence, performance, and cost per task. The benchmark provides practitioners with a data-driven view of where Muse Spark 1.1 stands relative to leading models.
DataGovBench: New benchmark evaluates LLMs on real-world data analysis with large multi-tabular datasets
Researchers introduced DataGovBench, a benchmark derived from governmental open data to evaluate LLMs on practical data analysis tasks. It includes Table QA and Table Insight tasks, addressing limitations of existing benchmarks that focus on small tables and fact retrieval.
STS2-Bench tests LLM long-horizon decision-making in Slay the Spire 2
A developer created STS2-Bench, a benchmark using Slay the Spire 2 to evaluate LLMs on sequential decision-making under uncertainty. The benchmark tests models on reading changing game states, weighing short-term vs long-term goals, and adapting plans. Early results show 5.6Sol performing surprisingly well.
UniClawBench benchmark proposed for evaluating proactive AI agents in real-world tasks
Researchers introduced UniClawBench, a universal benchmark for evaluating proactive agents that operate everyday tools in real-world environments. Unlike existing benchmarks that rely on sandboxed settings and single-turn evaluations, UniClawBench aims to isolate specific model capabilities to identify root causes of agent failures.