LLM Models

ElevenLabs' most expressive text-to-speech model (GA February 2026), producing natural, lifelike speech with high emotional range and contextual understanding across many languages. Eleven v3 adds a Text to Dialogue API for multi-speaker, emotionally rich conversations.

Udio's AI music generation model, producing high-fidelity songs with vocals from text prompts. Udio v1.5 brought 48kHz stereo output, key guidance, and global language support (including Mandarin), with later updates improving stem separation and vocal pitch control.

Suno's AI music generation model (May 2025), producing full songs with vocals and instrumentation from a text prompt. v4.5 extended song length to 8 minutes, added 1,200+ genre tags, and improved vocal expressiveness and prompt adherence for coherent, radio-quality tracks.

Meta's AI music generation model available in 300M, 1.5B, and 3.3B parameter sizes. Single-stage auto-regressive Transformer trained over 32kHz EnCodec tokenizer with 4 codebooks sampled at 50Hz. Trained on 20K hours of licensed music from internal dataset (10K high-quality tracks) plus ShutterStock and Pond5 collections (390K instrument-only tracks). Eliminates cascading model requirement through efficient token interleaving - only 50 auto-regressive steps per second of audio. Supports both text-to-music and melody-guided generation. Evaluated on MusicCaps benchmark showing superiority vs baselines. Released April-May 2023. Part of AudioCraft toolkit alongside AudioGen and EnCodec.

DISCONTINUED. Jina AI's open text embedding model (September 2024), a 570M-parameter model with task-specific LoRA adapters (retrieval, clustering, classification) and long-context support. It offers strong price-performance and multilingual retrieval, self-hostable or via the Jina API. Jina AI's own model page marks it deprecated in favour of newer models.

Nomic's open text embedding model (February 2024), fully open with public weights, training code, and data. It offers an 8,192-token context - among the longest for open models - with Matryoshka dimensions, competitive quality, and reproducible, auditable training.

BAAI's open-source multilingual embedding model, notable for Multi-Functionality (dense, sparse, and multi-vector retrieval), Multi-Linguality (100+ languages), and Multi-Granularity (inputs up to 8,192 tokens). It is the strongest open option for multilingual search, matching commercial quality when self-hosted.

Google's embedding model that maps text (and, in multimodal variants, images, video, audio, and PDFs) into a shared vector space, leading retrieval benchmarks (~67.7 MTEB retrieval). It supports Matryoshka dimensions up to 3,072 and integrates with the Gemini API and Vertex AI.

Voyage AI's retrieval-optimized commercial embedding model, among the best for MTEB retrieval quality at production scale. It supports long context and Matryoshka (shortenable) dimensions, and is a common choice for high-accuracy RAG pipelines.

Cohere's multimodal embedding model, a top MTEB performer that embeds text and images into a shared space with support for 100+ languages and long documents. Built for enterprise search and RAG, it pairs with Cohere Rerank for high-precision retrieval pipelines.

OpenAI's largest text embedding model (January 2024), producing up to 3,072-dimensional vectors for retrieval, RAG, clustering, and classification. It supports shortening dimensions via the API for storage/latency trade-offs and remains a strong general-purpose retrieval baseline.

OpenAI's state-of-the-art automatic speech recognition (ASR) model with 1.55B parameters supporting 99+ languages. Trained on 1M hours of weakly labeled audio plus 4M hours of pseudo-labeled audio from Whisper large-v2. Uses 128 Mel frequency bins (vs 80 in previous versions) and trained for 2.0 epochs. Achieves 7.4% WER average and 97.9% word accuracy on LibriSpeech. Shows 10-20% error reduction vs large-v2 and 72% WER reduction vs prior MLPerf ASR model (RNN-T). Performs automatic language identification, generates phrase-level timestamps, and handles punctuation/capitalization. Strongest on high-resource languages (English, Spanish, French, German). Open source enables self-hosting and commercial use.