AI · alternatives · 2026
24 alternatives to Marengo 3.0 by TwelveLabs
The most powerful embedding model for video understanding
Marengo 3.0 is TwelveLabs' multimodal embedding model for video understanding. It processes video, audio, and text together to enable precise video search and retrieval at scale. The model is designed for developers… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier.
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Google's first natively multimodal embedding model
Mar 2026 · blog.google · its alternatives →
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The best audio, image & video models now in one platform
Nov 2025 · elevenlabs.io · its alternatives →
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End-to-end deep dive of the project, spanning a large GPU cluster, distributed RocksDB, and terabytes of sharded HNSW.
2025 · blog.wilsonl.in · its alternatives →
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Google EmbeddingGemma 2free▲318An open, lightweight multimodal embedding model
17d ago · blog.google · its alternatives →
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The most expressive Text to Speech model ever
2025 · elevenlabs.io · its alternatives →
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VideoPoet ▲352A large language model for zero-shot video generation
2023 · sites.research.google · its alternatives →
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Luke from ElevenLabs here. We just released Scribe Realtime v2, our new real-time Speech to Text model. We rebuilt it from the ground up around a new architecture that uses predictive transcription – it anticipates the next likely word before it’s spoken. That makes it both fast (around 150 ms latency) and highly accurate. Scribe v2 Realtime outperforms every low latency transcription model across 30 commonly used EU and Asian languages – 93.5% accuracy, compared to Gemini 2.5 Flash (91.4%), GPT-4o MiniTranscribe (90.7%), and Deepgram Nova 3 (85%). It supports 90+ languages including…
2025 · elevenlabs.io · its alternatives →
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Lunair▲488Generate studio-quality explainer videos. Instantly.
Feb 2026 · lunair.ai · its alternatives →
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Translate your videos into any language with one-click
2023 · its alternatives →
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Multilingual video dubbing app for globalizing your content
2024 · video-dubber-demo.robomotion.app · its alternatives →
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LTX.bio▲164Hey HN, after our 13B release we've been working on a faster version of our open-source video model and we're excited to share it. We started with a 13B base model that already had competitive generation speeds (e.g. 55s for a 5s video on an H100 — faster than any other model out there). But we wanted to push it further to allow everyone to quickly iterate over their video generations. So we built a distilled version focused on speed without sacrificing temporal or spatial coherence. With the Distilled model, you can now generate 5-second 720p videos in about 9.5 seconds on an H100, and…
2021 · lightricks.com · its alternatives →
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The model has 3B active parameters. We put the code, homepage, paper and model links here: - Code: https://github.com/bytedance/Lance - Homepage: https://lance-project.github.io/ - Paper: https://arxiv.org/abs/2605.18678 - Model: https://huggingface.co/bytedance-research/Lance p.s. Lance is a research project, not a polished product. The model was trained using fewer than 128 GPUs.
May 2026 · github.com · its alternatives →
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Blanc▲107Translate videos into 47+ languages using 250+ neural voices
2021 · useblanc.com · its alternatives →
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Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance:…
2025 · github.com · its alternatives →
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The Virtual Feature Store. Turn your existing data infrastructure into a feature store. - featureform/featureform
2021 · github.com · its alternatives →
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We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…
2024 · github.com · its alternatives →
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It's our new text-to-image model: a 9.3B single-stream diffusion transformer trained entirely from scratch. We focused heavily on controllability through structured JSON prompts, with strong text rendering, spatial awareness through bounding box guidance, and color palette control. It has the best text rendering of any open-weight model we've tested so far, and the NF4 quantized checkpoint runs on a single 24GB GPU. For more technical details and examples see our blog post: https://ideogram.ai/blog/ideogram-4.0/ We will be happy to answer any questions :)
Jun 2026 · github.com · its alternatives →
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Feature engineering and machine learning: together at last! - asavinov/lambdo
2018 · github.com · its alternatives →
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I want to share the most recent model release we have prepared. It's a Vision-Language understanding Transformer. It has 40% fewer parameters than vanilla CLIP while performing much better on text-to-image retrieval, where it's also beneficial that our output embeddings have 2x fewer dimensions (256 vs. 512). Moreover, it supports 21 languages, including popular English, Hindi, Chinese, Arabic, and lower-resource languages like Ukrainian, Hebrew, and Armenian. We have packed the library into ONNX and CoreML, providing PyTorch inference code for CPUs and GPUs and PopTorch code for Graphcore…
2023 · github.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →