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Comparison of Perplexity: Embed V1 4B, Intfloat: Multilingual-E5-Large, embeddinggemma-2
3 modelsperplexityPerplexity: Embed V1 4BRemove ×intfloatIntfloat: Multilingual-E5-LargeRemove ×googleembeddinggemma-2Remove ×
What’s differentpplx-embed-v1 -4B is one of Perplexity's state-of-the-art text embedding models built for real-world, web-scale retrieval. pplx-embed-v1 is optimized for standard dense text retrieval with the 4B parameter…The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized…EmbeddingGemma 2 is a 740M parameter multimodal embedding model from Google DeepMind that maps text (incl. code), images, video and audio, alone or interleaved, into a single 768-dimensional vector space,…
DateMar 16, 2026ListedJun 4, 2024ListedOct 8, 2026Listed
Listed rate$0.03 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗See provider quotes
Provider endpoints1 endpoint →1 endpoint →0 endpoints →
Context window32K5128.2K
Max output28.8K460—
Parameters4B——
Open weightsNot reportedSupportedNot reported

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