Discover Enterprise AI & Software Benchmarks
Agentic Coding Benchmark
Compare AI coding assistants’ compliance to specs and code security
LLM Coding Benchmark
Compare LLMs is coding capabilities.
Cloud GPU Providers
Identify the cheapest cloud GPUs for training and inference
GPU Concurrency Benchmark
Measure GPU performance under high parallel request load.
Multi-GPU Benchmark
Compare scaling efficiency across multi-GPU setups.
AI Gateway Comparison
Analyze features and costs of top AI gateway solutions
LLM Latency Benchmark New
Compare the latency of LLMs
LLM Price Calculator
Compare LLM models’ input and output costs
Text-to-SQL Benchmark
Benchmark LLMs’ accuracy and reliability in converting natural language to SQL.
AI Bias Benchmark
Compare the bias rates of LLMs
AI Hallucination Rates
Evaluate hallucination rates of top AI models
Agentic RAG Benchmark
Evaluate multi-database routing and query generation in agentic RAG
Embedding Models Benchmark
Compare embedding models accuracy and speed.
Hybrid RAG Benchmark
Compare hybrid retrieval pipelines combining dense & sparse methods.
Open-Source Embedding Models Benchmark
Evaluate leading open-source embedding models accuracy and speed.
RAG Benchmark
Compare retrieval-augmented generation solutions
Vector DB Comparison for RAG
Compare performance, pricing & features of vector DBs for RAG
Web Unblocker Benchmark
Evaluate the effectiveness of web unblocker solutions
Video Scrapers Benchmark New
Analyze performance of Video Scraper APIs
AI Code Editor Comparison
Analyze performance of AI-powered code editors
E-commerce Scraper Benchmark
Compare scraping APIs for e-commerce data
LLM Examples Comparison
Compare capabilities and outputs of leading large language models
OCR Accuracy Benchmark
See the most accurate OCR engines and LLMs for document automation
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
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.
Tabular Models Benchmark New
Compare tabular learning models with different datasets
LLM Quantization Benchmark New
Compare BF16, FP8, INT8, INT4 across performance and cost
Multimodal Embedding Models Benchmark New
Compare multimodal embeddings for image–text reasoning
LLM Inference Engines Benchmark New
Compare vLLM, LMDeploy, SGLang on H100 efficiency
LLM Scrapers Benchmark New
Compare the performance of LLM scrapers
Visual Reasoning Benchmark New
Compare the visual reasoning abilities of LLMs
AI Providers Benchmark New
Compare the latency of AI providers
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Latest Benchmarks
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.
Benchmark of 38 LLMs in Finance: Claude Opus 4.6, Gemini 3.1 Pro & More
We evaluated 38 LLMs in finance on 238 hard questions from the FinanceReasoning benchmark to identify which models excel at complex financial reasoning tasks like statement analysis, forecasting, and ratio calculations. LLM finance benchmark overview We evaluated LLMs on 238 hard questions from the FinanceReasoning benchmark (Tang et al.).
Text-to-SQL: Comparison of LLM Accuracy
I have relied on SQL for data analysis for 18 years, beginning in 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.
Compare Multimodal AI Models on Visual Reasoning
We benchmarked 15 leading multimodal AI models on visual reasoning using 200 visual-based questions. The evaluation consisted of two tracks: 100 chart understanding questions testing data visualization interpretation, and 100 visual logic questions assessing pattern recognition and spatial reasoning. Each question was run 5 times to ensure consistent and reliable results.
See All AI ArticlesLatest Insights
Top 50 Deep Learning Use Case & Case Studies
Deep learning uses artificial neural networks to learn from data. When trained on large, high-quality datasets, it achieves high accuracy, making it valuable wherever you have abundant data and need accurate predictions. Below are real deep learning applications across industries and business functions, with concrete examples.
Deep Learning in Finance Top 11 Use Cases in 2026
Deep learning has moved well past the hype stage in financial services. Banks, insurers, and fintechs are deploying these models in production, not as experiments, but as core infrastructure.
Top 5 Facial Recognition Challenges & Solutions
Facial recognition is now part of everyday life, from unlocking phones to verifying identities in public spaces. Its reach continues to grow, bringing both convenience and new possibilities. However, this expansion also raises concerns about accuracy, privacy, and fairness that need careful attention.
20 Strategies for AI Improvement & Examples
AI models require continuous improvement as data, user behavior, and real-world conditions evolve. Even well-performing models can drift over time when the patterns they learned no longer match current inputs, leading to reduced accuracy and unreliable predictions.
See All AI ArticlesBadges from latest benchmarks
Enterprise Tech Leaderboard
Top 3 results are shown, for more see research articles.
Vendor | Benchmark | Metric | Value | Year |
|---|---|---|---|---|
 Groq | 1st Latency | 2.00 s | 2025 | |
 SambaNova | 2nd Latency | 3.00 s | 2025 | |
 Together.ai | 3rd Latency | 11.00 s | 2025 | |
 llama-4-maverick | 1st Success Rate | 56 % | 2025 | |
 claude-4-opus | 2nd Success Rate | 51 % | 2025 | |
 qwen2.5-72b-instruct | 3rd Success Rate | 45 % | 2025 | |
 o1 | 1st Accuracy | 86 % | 2025 | |
o3-mini | 2nd Accuracy | 86 % | 2025 | |
claude-3.7-sonnet | 3rd Accuracy | 67 % | 2025 | |
 Zyte | 1st Response Time | 1.75 s | 2025 | |
Data-Driven Decisions Backed by Benchmarks
Insights driven by 40,000 engineering hours per year
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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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