Search Results for "benchmark"
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Showing 884 open source projects for "benchmark"
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Benchmark
A microbenchmark support library
A library to benchmark code snippets, similar to unit tests. -
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hyperfine
A command-line benchmarking tool
A command-line benchmarking tool. Statistical analysis across multiple runs. Support for arbitrary shell commands. Constant feedback about the benchmark progress and current estimates. Warmup runs can be executed before the actual benchmark. Cache-clearing commands can be set up before each timing run. Statistical outlier detection to detect interference from other programs and caching effects. Export results to various formats: CSV, JSON, Markdown, AsciiDoc. Parameterized benchmarks (e.g. vary the number of threads). ... -
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MLPerf
Reference implementations of MLPerf™ training benchmarks
...Together, we create the reference implementations, rules, policies, and procedures to benchmark a wide variety of AI workloads. -
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XMRig
RandomX, KawPow, CryptoNight, AstroBWT and GhostRider unified miner
High performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT CPU/GPU miner, RandomX benchmark, and stratum proxy. XMRig is a high-performance, open-source, cross-platform RandomX, KawPow, CryptoNight, and AstroBWT unified CPU/GPU miner and RandomX benchmark. Official binaries are available for Windows, Linux, macOS, and FreeBSD. The preferred way to configure the miner is the JSON config file as it is more flexible and human-friendly. -
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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Superposition Benchmark (Unigine)
GPU benchmark testing graphics performance with realistic 3D scenes.
Superposition Benchmark by Unigine is a powerful GPU stress-testing and benchmarking tool designed to evaluate graphics performance using the Unigine 2 Engine. It features advanced visuals, real-time lighting, and physics simulations to test DirectX and OpenGL performance. Superposition provides detailed results, including frame rates, GPU temperatures, and stability data. -
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GLM-4.6
Agentic, Reasoning, and Coding (ARC) foundation models
...Its reasoning capabilities have been strengthened, including improved tool usage during inference and more effective integration within agent frameworks. GLM-4.6 also enhances writing quality, producing outputs that better align with human preferences and role-playing scenarios. Benchmark evaluations demonstrate that it not only outperforms GLM-4.5 but also rivals leading global models such as DeepSeek-V3.1-Terminus and Claude Sonnet 4. -
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BenchmarkTools.jl
A benchmarking framework for the Julia language
...Under the hood, BenchmarkTrackers relied on Benchmarks for actual benchmark execution. -
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BEIR
A Heterogeneous Benchmark for Information Retrieval
BEIR is a benchmark framework for evaluating information retrieval models across various datasets and tasks, including document ranking and question answering. -
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AgentBench
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
AgentBench is an open-source benchmark designed to evaluate the capabilities of large language models when used as autonomous agents. Unlike traditional language model benchmarks that focus on static text tasks, AgentBench measures how models perform in interactive environments that require planning, reasoning, and decision-making. The benchmark includes multiple environments that simulate realistic scenarios such as web interaction, database querying, and problem solving tasks. ... -
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CrystalDiskMark
A simple disk benchmark software
A simple disk benchmark software. -
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HumanEval
Code for the paper "Evaluating Large Language Models Trained on Code"
...The benchmark has become a standard for evaluating code generation models, including those in the Codex and GPT families. Researchers can use the dataset to run reproducible comparisons across models and track improvements in functional code synthesis. By focusing on correctness through execution, human-eval provides a rigorous and practical way to evaluate programming capabilities in AI systems. -
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SAM 3
Code for running inference and finetuning with SAM 3 model
...This capability is grounded in a new data engine that automatically annotated over four million unique concepts, producing a massive open-vocabulary segmentation dataset and enabling the model to achieve 75–80% of human performance on the SA-CO benchmark, which itself spans 270K unique concepts. -
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MTEB
MTEB: Massive Text Embedding Benchmark
...This makes progress in the field difficult to track, as various models are constantly being proposed without proper evaluation. To solve this problem, we introduce the Massive Text Embedding Benchmark (MTEB). MTEB spans 8 embedding tasks covering a total of 58 datasets and 112 languages. Through the benchmarking of 33 models on MTEB, we establish the most comprehensive benchmark of text embeddings to date. We find that no particular text embedding method dominates across all tasks. This suggests that the field has yet to converge on a universal text embedding method and scale it up sufficiently to provide state-of-the-art results on all embedding tasks. -
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LongBench
LongBench v2 and LongBench (ACL 25'&24')
...LongBench addresses this gap by providing datasets that require models to process and reason over long sequences of text across multiple tasks. The benchmark includes multiple categories such as single-document question answering, multi-document reasoning, summarization, long dialogue understanding, and code analysis. It supports bilingual evaluation in English and Chinese to assess multilingual capabilities across extended contexts. Newer versions of the benchmark introduce extremely long context windows ranging from thousands to millions of tokens, enabling researchers to test the limits of modern long-context models. -
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AICGSecEval
A.S.E (AICGSecEval) is a repository-level AI-generated code security
AICGSecEval is an open-source benchmark framework designed to evaluate the security of code generated by artificial intelligence systems. The project was developed to address concerns that AI-assisted programming tools may produce insecure code containing vulnerabilities such as injection flaws or unsafe logic. The framework constructs evaluation tasks based on real-world software repositories and known vulnerability cases derived from CVE records. -
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DeepSeek-OCR 2
Visual Causal Flow
...It is designed to handle complex layouts and noisy documents by giving the model causal reasoning capabilities that mimic human visual scanning behavior, enhancing OCR performance on documents with rich spatial structure. The repository provides model code and inference scripts that let researchers and developers run and benchmark the system on both images and PDFs, with support for batch evaluation and optimized pipelines leveraging vLLM and transformers. -
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kube-bench
Checks whether Kubernetes is deployed
kube-bench is a tool that checks whether Kubernetes is deployed securely by running the checks documented in the CIS Kubernetes Benchmark. Trivy, the all-in-one cloud-native security scanner, can be deployed as a Kubernetes Operator inside a cluster. Both, the Trivy CLI, and the Trivy Operator support CIS Kubernetes Benchmark scanning among several other features. There are multiple ways to run kube-bench. You can run kube-bench inside a pod, but it will need access to the host's PID namespace in order to check the running processes, as well as access to some directories on the host where config files and other files are stored. -
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SDGym
Benchmarking synthetic data generation methods
The Synthetic Data Gym (SDGym) is a benchmarking framework for modeling and generating synthetic data. Measure performance and memory usage across different synthetic data modeling techniques – classical statistics, deep learning and more! The SDGym library integrates with the Synthetic Data Vault ecosystem. You can use any of its synthesizers, datasets or metrics for benchmarking. You also customize the process to include your own work. Select any of the publicly available datasets from the... -
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Crow Framework
A Fast and Easy to use microframework for the web
...Get started by installing Crow and building you first application. Or go through the guides if you're stuck somewhere. Easy Routing (similar to flask). Type-safe Handlers. Blazingly fast (see this benchmark and this benchmark). Built-in JSON support. Mustache-based templating library (crow::mustache). Header-only library (single header file available). Middleware support for extensions. HTTP/1.1 and Websocket support. Multi-part request and response support. Uses modern C++ (11/14). -
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Meta Agents Research Environments (ARE)
Meta Agents Research Environments is a comprehensive platform
...Unlike static benchmarks, ARE supports environments where agents must adapt to changes over time and reason over sequences of actions. It interacts with applications and faces uncertainty. The included Gaia2 benchmark offers 800 scenarios across multiple “universes”. It can test reasoning, memory, tool use, and adaptability. Integration with simulated applications/agent APIs (email, file system, etc.). Support for multiple AI model backends/providers. -
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CrystalMark Retro
CrystalMark Retro is a comprehensive benchmarking software.
...It supports 32-bit (x86), 64-bit (x64/ARM64), many-core, and multilingual (48+ languages) systems, and can measure CPU, Disk, 2D graphics (GDI), and 3D graphics (OpenGL) performance with a single click. Benchmark results can be registered in CrystalMarkDB for centralized management of past results (account required: free of charge) and comparison with data registered by users around the world. -
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JMH Gradle Plugin
Integrates the JMH benchmarking framework with Gradle
The JMH Gradle Plugin provides integration of the Java Microbenchmark Harness (JMH) into Gradle builds, enabling developers to write and run performance benchmarks directly in their projects. JMH is the de facto standard for writing accurate and reliable Java microbenchmarks, and this plugin automates tasks like generating benchmark sources, compiling them with the required JMH support classes, and packaging runnable benchmark jars. It simplifies the workflow by handling classpath setup and wiring Gradle tasks for running benchmarks. Developers can run benchmarks via Gradle commands, produce reports, and compare performance over time. This reduces the manual effort of setting up JMH, making performance testing a natural part of the development cycle. ... -
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CrystalMark 3D25
A 3D benchmarking software.
CrystalMark 3D25 is a 3D benchmarking software that allows you to easily measure 3D performance (GLSL/WebGL) with a single click. It was developed with the goal of showcasing the appeal of the demo scene to PC enthusiasts (benchmarkers) worldwide, featuring works provided by demo scene creators who support Japan’s demo scene. -
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Hallucination Leaderboard
Leaderboard Comparing LLM Performance at Producing Hallucinations
...By focusing on hallucination rates rather than traditional metrics such as accuracy or fluency, the benchmark highlights an important aspect of AI system safety and trustworthiness. The leaderboard is regularly updated as new models are released and evaluation methods evolve. -
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CodeGeeX
CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)
...Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art performance compared to other open models like InCoder and CodeGen. CodeGeeX also powers IDE plugins for VS Code and JetBrains, offering features like code completion, translation, debugging, and annotation. The model supports Ascend 910 and NVIDIA GPUs, with optimizations like quantization and FasterTransformer acceleration for faster inference.
