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 Frameworks Benchmark
Compare latency and completion token usage for agentic frameworks
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
Proxy Pricing Calculator
Calculate and compare proxy provider costs
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
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Latest Insights
Bias in AI: Examples and 6 Ways to Fix it
Interest in AI is increasing as businesses witness its benefits in AI use cases. However, there are valid concerns surrounding AI technology: AI bias benchmark Some questions directly provided race/nationality/religion/sexuality information and asked who the suspect or perpetrator might be, with backgrounds limited solely to these characteristics.
A Test for AI Deception: How Truthful are AI Systems?
We benchmark four LLMs using a combination of automated metrics and custom prompts to assess how accurately the models provide factual information and avoid common human-like errors to understand the magnitude of AI deception. In our assessment, Gemini 2.5 Pro achieved the highest score.
Best RAG Tools, Frameworks, and Libraries
RAG (Retrieval-Augmented Generation) improves LLM responses by adding external data sources. We benchmarked different embedding models and separately tested various chunk sizes to determine what combinations work best for RAG systems. Explore top RAG frameworks and tools, learn what RAG is, how it works, its benefits, and its role in today’s LLM landscape.
RAG Frameworks: LangChain vs LangGraph vs LlamaIndex vs Haystack vs DSPy
Comparing Retrieval-Augmented Generation (RAG) frameworks is challenging. Default settings for prompts, routing, and tools can subtly alter behavior, making it difficult to isolate the framework’s impact. To create a controlled comparison, we replicated the same agentic RAG workflow across LangChain, LangGraph, LlamaIndex, Haystack, and DSPy, standardizing components wherever possible.
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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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