Discover Top Enterprise Software & AI Insights
Enterprise AI Benchmarks
LLM Price Calculator
Compare LLM models’ input and output costs
AI Coding Benchmark
Compare AI coding assistants’ compliance to specs and code security
AI Hallucination Rates
Evaluate hallucination rates of top AI models
AI Code Editor Comparison
Analyze performance of AI-powered code editors
Cloud GPU Providers
Identify the cheapest cloud GPUs for training and inference
RAG Benchmark
Compare retrieval-augmented generation solutions
OCR Accuracy Benchmark
See the most accurate OCR engines and LLMs for document automation
Web Unblocker Benchmark
Evaluate the effectiveness of web unblocker solutions
E-commerce Scraper Benchmark
Compare scraping APIs for e-commerce data
SERP Scraper API Benchmark
Benchmark search engine scraping API success rates and prices
Proxy Pricing Calculator
Calculate and compare proxy provider costs
AI Gateway Comparison
Analyze features and costs of top AI gateway solutions
Agentic RAG Benchmark
Evaluate multi-database routing and query generation in agentic RAG
Vector DB Comparison for RAG
Compare performance, pricing & features of vector DBs for RAG
LLM Model Examples Comparison
Compare capabilities and outputs of leading large language models
Screenshot to Code Benchmark
Evaluate tools that convert screenshots to front-end code
Agentic Frameworks Benchmark
Compare latency and completion token usage for agentic frameworks
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Latest Insights
Top 20+ Agentic RAG Frameworks
Agentic RAG enhances traditional RAG by boosting LLM performance and enabling greater specialization. We conducted a benchmark to assess its performance on routing between multiple databases and generating queries. Explore agentic RAG frameworks and libraries, key differences from standard RAG, benefits, and challenges to unlock their full potential.
Text-to-SQL: Comparison of LLM Accuracy
I have been relying on SQL for data analysis for 18 years, beginning with my days as a consultant. Translating natural-language questions into SQL makes data more accessible, allowing anyone, even those without technical skills, to work directly with databases.
LLM Latency Benchmark by Use Cases
The effectiveness of large language models (LLMs) is determined not only by their accuracy and capabilities but also by the speed at which they engage with users. We benchmarked the performance of leading language models across various use cases, measuring their responsiveness to user input.
GPU Concurrency Benchmark
We benchmarked the latest NVIDIA GPUs, including the NVIDIA (H100, H200, and B200) and AMD (MI300X), for concurrency scaling analysis. Using the vLLM framework with the gpt-oss-20b model, we tested how these GPUs handle concurrent requests, from 1 to 1024.
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See how Enterprise AI Performs in Real-Life
AI benchmarking based on public datasets is prone to data poisoning and leads to inflated expectations. AIMultiple’s holdout datasets ensure realistic benchmark results. See how we test different tech solutions.
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