Artificial Intelligence
Explore practical insights, research, and benchmarks on artificial intelligence, including generative AI, large language models, RAG, governance frameworks, MLOps practices, and AI hardware. Gain an understanding of key tools, implementation strategies, and enterprise use cases shaping the AI landscape.
Explore Artificial Intelligence
800+ Leading AI Benchmarks
We curated a list with over 800 AI benchmarks for LLMs, GPUs, cloud GPUs, AI agents, tabular AI, and cybersecurity that are not yet saturated. Note that most of the May–June peak corresponds to the period during which we carried out our research. Benchmarks that update continuously are dated to the last time we verified…
Intelligence Density of 71 LLMs for Smarter & Denser Models
We tracked 71 LLMs released between February 2023 and May 2026 and collected 10 public benchmarks to measure intelligence density. We divided the capability score by the resource the model consumes (active parameters, training compute, and inference price). To calculate intelligence density, we executed the following steps: See methodology for the scoring approach, and per-resource…
Multimodal Embedding Models: Apple vs Meta vs OpenAI
Multimodal embedding models excel at identifying objects but struggle with relationships. Current models struggle to distinguish “phone on a map” from “map on a phone.” We benchmarked 7 leading models across MS-COCO and Winoground to measure this specific limitation. To ensure a fair comparison, we evaluated every model under identical conditions using NVIDIA A40 hardware…
Benchmark of 40+ LLMs in Finance: Claude Fable 5 & GPT-5.6 Sol
We evaluated LLMs on 238 hard questions from the FinanceReasoning benchmark (Tang et al.).2 This subset targets the most challenging financial-reasoning tasks, assessing complex, multi-step quantitative reasoning involving financial concepts and formulas. Our evaluation employed a custom prompt design and scoring criteria of accuracy and token consumption. For a detailed explanation of how these metrics…
AI Code Review Tools Benchmark
With the increased use of AI coding tools, codebases have become more prone to vulnerabilities, which increased the need for effective code reviews. To address this, we introduce RevEval (AI Code Review Eval), which benchmarks the top four AI code review tools across 309 pull requests from repositories of varying sizes and evaluates their performance…
Top 12 AI Control Plane Tools for Regulated Deployments
An AI control plane provides a shared layer for operating AI agents and agent-based applications. We compared the top 12 AI control plane tools for enterprise architects, security teams, and AI governance owners planning AI adoption at enterprise scale. Read the methodology to see how we scored these products. Vendor selection criteria: We included vendors…
GPU Marketplace: Vast.ai vs Shadeform vs Prime Intellect
Finding available GPU capacity at reasonable prices has become a critical challenge for AI teams. While major cloud providers like AWS and Google Cloud offer GPU instances, they’re often at capacity or expensive. GPU marketplace aggregators have emerged as an alternative, connecting users to dozens of providers through a single interface. See below for the…
Top 10 AI Infrastructure Companies & Applications
Many organizations invest heavily in AI, yet most projects fail to scale. 10-20% of AI proofs of concept progress to full deployment.9 A key reason is that existing systems are not equipped to support the demands of large datasets, real-time processing, or complex machine learning models. As AI becomes more central to business strategy, infrastructure…
LLM Latency Benchmark by Use Cases
We benchmarked 11 top large language models with a total of 1,320 requests, splitting reasoning and non-reasoning models, and measured first-token latency, per-token latency, and overall response time. You can find details on how we measured latency here. We report reasoning and non-reasoning models separately. Reasoning models spend several seconds thinking before the first visible…
Recommendation Systems: Applications and Examples
We examined the main types of recommendation systems, key concepts, and real-world applications, and benchmarked LightFM, Cornac BPR, and TensorFlow Recommenders using AUC, Precision@10, and Recall@10. These libraries implement machine learning algorithms to process training data and generate personalized recommendations using collaborative or content-based filtering techniques. Additionally, these libraries implement machine learning models to analyze…