Discover Enterprise AI & Software Benchmarks
AI Code Editor Comparison
Analyze performance of AI-powered code editors
AI Coding Benchmark
Compare AI coding assistants’ compliance to specs and code security
AI Gateway Comparison
Analyze features and costs of top AI gateway solutions
AI Hallucination Rates
Evaluate hallucination rates of top AI models
Agentic RAG Benchmark
Evaluate multi-database routing and query generation in agentic RAG
Cloud GPU Providers
Identify the cheapest cloud GPUs for training and inference
E-commerce Scraper Benchmark
Compare scraping APIs for e-commerce data
LLM Examples Comparison
Compare capabilities and outputs of leading large language models
LLM Price Calculator
Compare LLM models’ input and output costs
OCR Accuracy Benchmark
See the most accurate OCR engines and LLMs for document automation
RAG Benchmark
Compare retrieval-augmented generation solutions
Screenshot to Code Benchmark
Evaluate tools that convert screenshots to front-end code
SERP Scraper API Benchmark
Benchmark search engine scraping API success rates and prices
Vector DB Comparison for RAG
Compare performance, pricing & features of vector DBs for RAG
Web Unblocker Benchmark
Evaluate the effectiveness of web unblocker solutions
LLM Coding Benchmark
Compare LLMs is coding capabilities.
Handwriting OCR Benchmark
Compare the OCRs in handwriting recognition.
Invoice OCR Benchmark
Compare LLMs and OCRs in invoice.
AI Reasoning Benchmark
See the reasoning abilities of the LLMs.
Speech-to-Text Benchmark
Compare the STT models' WER and CER in healthcare.
Text-to-Speech Benchmark
Compare the text-to-speech models.
AI Video Generator Benchmark
Compare the AI video generators in e-commerce.
AI Bias Benchmark
Compare the bias rates of LLMs
Multi-GPU Benchmark
Compare scaling efficiency across multi-GPU setups.
GPU Concurrency Benchmark
Measure GPU performance under high parallel request load.
Embedding Models Benchmark
Compare embedding models accuracy and speed.
Open-Source Embedding Models Benchmark
Evaluate leading open-source embedding models accuracy and speed.
Text-to-SQL Benchmark
Benchmark LLMs’ accuracy and reliability in converting natural language to SQL.
Hybrid RAG Benchmark
Compare hybrid retrieval pipelines combining dense & sparse methods.
Latest Benchmarks
15 AI Agent Observability Tools: AgentOps & Langfuse [2026]
AI agent observability tools, such as Langfuse and Arize, help gather detailed traces (a record of a program or transaction’s execution) and provide dashboards to track metrics in real time. Many agent frameworks, like LangChain, use the OpenTelemetry standard to share metadata with agentic monitoring. On top of that, many observability tools provide custom instrumentation for greater flexibility.
Computer Use Agents: Benchmark & Architecture in 2026
Computer-use agents promise to operate real desktops and web apps, but their designs, limits, and trade-offs are often unclear. We examine leading systems by breaking down how they work, how they learn, and how their architectures differ.
AI Memory: Most Popular AI Models with the Best Memory
Smarter models often have worse memory. We tested 26 large language models in a 32-message business conversation to determine which actually retain information. AI memory benchmark results We tested 26 popular large language models through a simulated 32-message business conversation with 43 questions.
Benchmarking Agentic AI Frameworks in Analytics Workflows
Frameworks for building agentic workflows differ substantially in how they handle decisions and errors, yet their performance on imperfect real-world data remains largely untested.
See All Agentic AI ArticlesLatest Insights
Figma MCP Server Tested: Figma to Code in 2026
Figma MCP server, which connects design files directly to AI coding tools like Cursor, Windsurf, and Claude Code. The server uses Model Context Protocol (MCP) to provide design context to AI coding assistants, enabling code generation that reflects both design specifications and existing codebase patterns.
Agentic Payments & Commerce: Tools, Use Cases & Benefits
Agentic AI is moving from a concept to a critical piece of modern infrastructure. This transformation is massive: the Agentic AI industry is estimated to reach $155B by 2030.
Code Execution with MCP: A New Approach to AI Agent Efficiency
Anthropic introduced a method in which AI agents interact with Model Context Protocol (MCP) servers by writing executable code rather than making direct calls to tools. The agent treats tools as files on a computer, finds what it needs, and uses them directly with code, so intermediate data doesn’t have to pass through the model’s memory.
Agentic AI for Cybersecurity: Use Cases & Examples [2026]
Agentic AI refers to AI systems that combine models like large language models (LLMs) with automated workflows, tool integration, and decision support. These systems assist security teams in SecOps and AppSec by analyzing alerts, automating routine tasks, and supporting investigative work. Agentic AI tools generally operate under human oversight.
See All Agentic AI ArticlesBadges from latest benchmarks
Enterprise Tech Leaderboard
Top 3 results are shown, for more see research articles.
Vendor | Benchmark | Metric | Value | Year |
|---|---|---|---|---|
 X | Latency | 2.00 s | 2025 | |
 SambaNova | Latency | 3.00 s | 2025 | |
 Together.ai | Latency | 11.00 s | 2025 | |
 llama-4-maverick | 1st LMMs | Success Rate | 56 % | 2025 |
 claude-4-opus | 2nd LMMs | Success Rate | 51 % | 2025 |
 qwen-2.5-72b-instruct | 3rd LMMs | Success Rate | 45 % | 2025 |
 o1 | Accuracy | 86 % | 2025 | |
o3-mini | Accuracy | 86 % | 2025 | |
claude-3.7-sonnet | Accuracy | 67 % | 2025 | |
 Bright Data | Cost | 2025 | ||
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