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Published directory entries matching your search.
UNIGINE Benchmarks are performance benchmarks by UNIGINE that put a PC under maximum load to measure real performance. The site states they can be used to determine stability of CPU, GPU, power supply and cooling under stressful conditions and for overclocking. Benchmarks support Windows, Linux and macOS with DirectX and OpenGL. A free Basic edition and a Professional edition are offered.
Kaggle Benchmarks is a Kaggle feature that allows the AI research community to build, run, and share benchmarks for evaluating artificial intelligence models and agents. All benchmarks are crowdsourced by the global AI research community.
Benchmarks Game is a developer resource for coding, documentation, communities, security research, or software tools.
Cybenetics PSU Benchmarks is a curated directory or link collection for discovering useful websites, tools, and resources.
go-ml-benchmarks is a repository of machine learning inference benchmarks for Go, focused on tabular models such as XGBoost. It compares typical implementations using different transports, encodings, preprocessing libraries and models, using the Titanic dataset. The repository was archived by the owner and is now read-only.
Human Benchmark is a miscellaneous web resource for discovery, directories, archives, web culture, or useful tools.
Open Benchmarking is a miscellaneous web resource for discovery, directories, archives, web culture, or useful tools.
The Packrift Packaging Optimization Benchmark Corpus is a publicly available dataset generated from 1,000 exact-spec Packrift feed records, containing 24,000 SKU-specific benchmark pages across 24 operational page types for packaging optimization workflows, with public source ledgers, manifests, metadata, and solver fixtures to support auditability.
The Benchmark Energy & Geometry Database collects results of highly accurate quantum mechanical calculations of molecular structures, energies and properties. These data serve as benchmarks for testing and parameterizing other computational methods. Featured datasets include interaction energies and dissociation curves for noncovalent complexes.
Chunk Tuner is a tool that benchmarks chunking strategies for a RAG corpus and recommends a configuration. It benchmarks strategies such as fixed-token windows, recursive character splitting, semantic splitting and PDF structural chunking, scoring each against retrieval metrics. It is available as a Python library, a CLI and an MCP server.
CodSpeed provides continuous performance testing for CI. It runs benchmarks in the CI pipeline, reports regressions in pull requests, and offers autonomous performance optimization. A documentation quickstart describes connecting a repository and setting up benchmarks, either through an automated wizard or manual configuration, including plugins for languages such as Python and C++.
LLMs Bullshit Benchmark provides machine-learning models, research, training resources, or evaluation tools.
MacGamingDB is a compatibility database for games on macOS and Apple Silicon. It reports Apple Silicon benchmarks for M1–M4 and lists FPS results through Rosetta, CrossOver, Parallels and GPTK. The site presents real-world benchmarks and FPS reports for games on modern Macs.
RankedAGI is a website that compares benchmarks across different AI models. It features benchmarks such as SWE Bench Verified, Codeforces, LMSYS and LiveBench, and presents a ranked table of models with size, version, release date, context length, cutoff knowledge, license and organization.
The SEAL LLM Leaderboard is a resource from Scale Labs that provides expert-driven benchmarks and updated rankings for large language models. It evaluates capabilities including agentic coding, frontier reasoning, and safety alignment, with over 20 benchmarks and assessments of more than 100 models from leading AI labs and open-source contributors.
A site offering side-by-side comparisons of programming languages and their compilers or runtimes. Benchmark tests are executed in a single CI job, and the static website is regenerated once a change is merged into the main branch. It aims to compare performance differences between languages and between compilers or runtimes of the same language.
MathNet is a free dataset and benchmark of 30,676 Olympiad-level math problems with solutions, spanning 47 countries, 17 languages and two decades of competitions. It supports problem solving, math-aware retrieval and retrieval-augmented problem solving.
AgentBench is a multi-dimensional benchmark that consists of 8 distinct environments for evaluating LLMs as autonomous agents in interactive tasks. It assesses reasoning and decision-making abilities in multi-turn open-ended generation, and tests over 25 LLMs including APIs and open-source models.
Aider LLM Leaderboards presents quantitative benchmarks of LLM code editing skill. The polyglot benchmark tests LLMs on 225 Exercism coding exercises in C++, Go, Java, JavaScript, Python and Rust, and benchmarks evaluate an LLM's ability to follow instructions and edit code without human intervention.
Arena is a platform that provides leaderboards to compare and benchmark leading frontier AI models across text, image, vision, and other modalities. Its rankings are powered by real user sessions from a global user base, it tracks new model releases, and offers features including live agent sessions, side-by-side model comparison, and user voting for model performance.
Artificial Analysis provides comparison and analysis of AI models and API hosting providers. It offers independent benchmarks across performance metrics including quality, price, output speed and latency, along with an intelligence index and cost-per-task measures.
AudioArena is a site for benchmarking and comparing voice models through real conversations. It offers AudioAgentBench, a set of six static multi-turn voice agent benchmarks, scored across five dimensions, and lets users select a benchmark, model, and run to view turn-by-turn transcripts.
Awesome AutoDL is a curated list of automated deep learning resources and an in-depth analysis, subtitled that neural architecture search is not the end. It collects papers, libraries and benchmarks covering neural architecture search and hyper-parameter optimization.
A GitHub collection of graph classification papers with reference implementations, covering embedding, deep learning, graph kernel and factorization methods. It also links to graph classification benchmark datasets and similar curated collections.
