llm-benchmark

[ICML 2024] TrustLLM: LLM trustworthiness evaluation across truthfulness, safety, fairness, robustness, privacy and ethics. Python/CLI toolkit for local models, compatible APIs and AI agent orchestration.

A curated list on AI token economics: what tokens cost, where they get wasted, and how to cut the bill. Tools, benchmarks, papers, and copy-paste configs for the token economy of LLMs and coding agents.

A reproducible local LLM benchmarking platform with versioned datasets, deterministic scoring, and auditable reports.

Bug Hunt Bench: 105 real bugs in two production repos, frontier coding models (GPT-6, Claude, Grok, Gemini, DeepSeek...) find and fix them in their own CLI, graded blind. Live leaderboard + every receipt.

An Open Harness and Benchmark for AI in Cybersecurity Operations.

How many bugs can LMs find & fix in large codebases?

The AI-native wire format for structured data. 100% comprehension on every frontier model. 50-92% fewer tokens than JSON. 43B+ lossless round-trips across 17 formats. Spec v3.5.1 Stable.

Открытый бенчмарк LLM: какая нейросеть лучше пишет код 1С:Предприятие (BSL). Объективная оценка LLM по методике SMOP с реальным исполнением в 1С — Claude, GPT, Gemini, DeepSeek, YandexGPT, GigaChat.

Community benchmark database for running LLMs on Apple Silicon Macs

[ICCV 2025] AdsQA: Towards Advertisement Video Understanding Arxiv: https://arxiv.org/abs/2509.08621

Benchmark coding agents and agent harnesses on 226 deterministic file-editing tasks with byte-exact verification and a public leaderboard.

ICRTL Benchmark: Industrial-level RTL design challenges for evaluating PPA optimization, code generation, and LLM applications in EDA.

Open-source benchmark for evaluating LLMs on 220 real professional tasks across 9 sectors and 44 occupations. Reproducible experiments, artifact validation, grading, and a live evidence dashboard.

An LLM Benchmark evaluation platform for automated model testing and analysis. 面向大语言模型的 Benchmark 自动化评测平台，支持模型测试与结果分析

Core engine behind Calibrate, a framework for evaluating AI agents: speech-to-text, text-to-speech, LLM evaluation, end-to-end simulations

Daily LLM value rankings - compare 300+ models by intelligence, speed and price. OpenRouter + Artificial Analysis. 大模型性价比排行榜

A Minesweeper benchmark for LLM agents. One identical board, up to nine models in parallel, one clock, one tool layer.

Catch local LLMs spilling into the CPU. One run shows GPU placement, prefill, decode, memory, power and a verdict.

Measured llama.cpp benchmarks on AMD Radeon RDNA4 with ROCm: RX 9070 XT + Radeon AI PRO R9700 (48 GB). Qwen3.8, Gemma 4, GLM, gpt-oss; GGUF quant quality (KLD), MTP speculative decoding, 128K context, Claude Code with local models.

tool-eval-bench results for Prism ML Ternary-Bonsai-27B (Q2_0) — 8 trials, score 85, deployability 80