Embedding Models Pricing - June 2026
Embedding API cost comparison: voyage-4-lite, OpenAI 3-small, Jina v3, and Amazon Titan V2 tie at $0.02/MTok. Gemini Embedding 2 now GA, Cohere Embed 4 dimensions corrected to 1,536 default.
Articles Tagged "Embeddings"
Best AI Models for RAG - June 2026
Gemini 2.5 Flash still leads LIT-RAGBench English RAG accuracy at 87.0%, but the full benchmark data reveals two overlooked entries: GPT-4.1-mini at 84.1% and o4-mini at 83.9%.
AI Agent Memory in 2026: 5 Frameworks Ranked
We compared Mem0, Zep, Letta, LangMem, and Cognee on architecture, benchmarks, pricing, and use cases to find the right memory layer for your agent stack.
Embedding Model Leaderboard: MTEB Rankings April 2026
April 2026 rankings of the top embedding models by MTEB score - Gemini Embedding 001, NV-Embed-v2, Qwen3-Embedding-8B, and the new Jina v4 multimodal release compared for RAG and search.
RAG Benchmarks Leaderboard: Retrieval Rankings 2026
Rankings of the top embedding and RAG systems across BEIR, MTEB retrieval, MIRACL, MS MARCO, KILT, HotpotQA, and RAGTruth hallucination benchmarks as of April 2026.
Microsoft Open-Sources Harrier, a New Embedding Leader
Microsoft's Harrier-OSS-v1 family delivers three MIT-licensed multilingual embedding models, with the 27B variant claiming top spot on Multilingual MTEB v2 at 74.3.
Google Launches Gemini Embedding 2 for Multimodal AI
Google's first natively multimodal embedding model maps text, images, video, audio, and PDFs into a single vector space - now in public preview via Gemini API and Vertex AI.
What Are AI Embeddings? A Plain-English Guide
A beginner-friendly explanation of AI embeddings - the technique that turns text into numbers so machines can understand meaning, power search, and enable RAG.
Embedding Model Leaderboard: MTEB Rankings March 2026
Rankings of the best embedding models by MTEB scores, comparing retrieval quality, dimensions, speed, and pricing for RAG and search.
What Is RAG? Retrieval-Augmented Generation Explained in Plain English
A beginner-friendly explanation of Retrieval-Augmented Generation (RAG) - the technique that lets AI pull in real facts before answering your questions.
