LLMs
LLMs are AI systems trained on vast text data to understand, generate, and manipulate human language for business tasks. We cover performance benchmarks, use cases, cost analyses, deployment options, and best practices to guide enterprise adoption of LLMs.
Github Stars of Open-Source Multimodal Models
We analyzed the growth and adoption of open-source multimodal models from 2021 to 2025, examining how frameworks like LLaVA, CLIP, and CogVLM have evolved in community support and technical capabilities across the rapidly expanding multimodal AI landscape.
Cost comparison of AI gateways
We compared the costs of AI gateways for the Llama 4 Scout model based on 1M output and input tokens.
First token latency comparison of AI gateways
We benchmarked each AI gateway by running short prompts (~18 tokens) and long prompts (~203 tokens) 50 times each, including only successful runs with measurable first-token latency for statistical reliability.
Text-to-SQL Benchmark
We benchmarked 8 large language models (LLMs) to see how well they convert natural language questions into SQL queries. This benchmark evaluates the accuracy of each model’s SQL generation and highlights common errors like faulty joins, aggregation mistakes, missing filters, and syntax issues.
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Explore LLMs
Large Language Models in Cybersecurity [2025]
Large language models (LLMs) are increasingly applied across cybersecurity domains, including threat intelligence, vulnerability detection, anomaly analysis, and red teaming. These applications are supported by both specialized cybersecurity LLMs and general-purpose models.
LLM Latency Benchmark by Use Cases in 2025
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 how quickly they respond to user input.
Text-to-SQL: Comparison of LLM Accuracy in 2025
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.
Top 5 AI Gateways for OpenAI: OpenRouter Alternatives
The growing number of LLM providers creates significant API management hurdles. AI gateways address this complexity by acting as a central routing point, enabling developers to interact with multiple providers through a single, unified API, thereby simplifying development and maintenance.
LLM VRAM Calculator for Self-Hosting in 2025
The use of LLMs has become inevitable, but relying solely on cloud-based APIs can be limiting due to cost, reliance on third parties, and potential privacy concerns. That’s where self-hosting an LLM for inference (also called on-premises LLM hosting or on-prem LLM hosting) comes in.
LLM Pricing: Top 15+ Providers Compared in 2025
We analyzed 15+ LLMs and their pricing and performance. LLM API pricing can be complex and depends on your preferred usage. If you plan to use: Hover over model names to see their full names and over headers to see explanations about the columns.
Compare Top 11 LLM Orchestration Frameworks in 2025
Leveraging multiple LLMs concurrently demands significant computational resources, driving up costs and introducing latency challenges. In the evolving landscape of AI, efficient LLM orchestration is essential for optimizing performance while minimizing expenses. Explore key strategies and tools for managing multiple LLMs effectively.
Compare Top 20 LLM Security Tools & Free Frameworks
Chevrolet of Watsonville, a car dealership, introduced a ChatGPT-based chatbot on their website. However, the chatbot falsely advertised a car for $1, potentially leading to legal consequences and resulting in a substantial bill for Chevrolet. Incidents like these highlight the importance of implementing security measures to LLM applications.
Cloud LLM vs Local LLMs: 3 Real-Life examples & benefits
In 2025, Cloud LLMs and Local LLMs are transforming business operations with unique advantages. Cloud LLMs, powered by advanced models like Grok 3, o3, and GPT-4.1, offer exceptional scalability and accessibility. Conversely, Local LLMs, driven by open-source models such as Qwen 3, Llama 4, and DeepSeek R1, ensure superior privacy and customization.
Large Multimodal Models (LMMs) vs LLMs in 2025
We evaluated the performance of Large Multimodal Models (LMMs) in financial reasoning tasks using a carefully selected dataset. By analyzing a subset of high-quality financial samples, we assess the models’ capabilities in processing and reasoning with multimodal data in the financial domain. The methodology section provides detailed insights into the dataset and evaluation framework employed.