Jina AI

@JinaAI_ · San Francisco, CA · joined 31 Mar 2020

Your Search Foundation, Supercharged! (acquired by @elastic Oct. 2025)

17 343Followers

1Following

2 269Posts total

207.7KViews on collected posts

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Jina AI@JinaAI_13 May

@JinaAI_ You guys haven’t made small (<100M) models in quite some time. I’d love to see a small variant of your v5 text models. 331 views · 1 likes · 0 reposts · 0 replies Open on X →

Jina AI@JinaAI_12 May

Per-task performance across 13 task types. Gold stars mark tasks where jina-embeddings-v5-omni-small beats the best open-weight baseline (3-9x larger). Wins: image classification (68.55 vs 64.30), image clustering (84.57 vs 83.24), audio classification (55.89 vs 53.39). Main http 2.4K views · 8 likes · 0 reposts · 1 replies Open on X →

Jina AI@JinaAI_12 May

Today v5-omni is available on Elastic Inference Service, HuggingFace and Jina API. Learn more about v5-omni from links below. 🤗: https://t.co/HohSXQe29i arXiv: https://t.co/gzWYoFIpm7 blog: https://t.co/2V6DaotHLW 3.1K views · 26 likes · 7 reposts · 0 replies Open on X →

Jina AI@JinaAI_12 May

v5-omni keeps the v5-text backbone completely frozen and adds pretrained vision and audio encoders connected through small trainable projectors: - Vision: Qwen3.5 vision encoders with 2x2 spatial merge. We freeze everything except the final projection layer (fc_vision_2), which 3.8K views · 13 likes · 1 reposts · 2 replies Open on X →

Jina AI@JinaAI_12 May

jina-embeddings-v5-omni is here! Our first universal embedding model for text, images, audio, and video. Available in two sizes: small (1.57B, 1024-dim, 32K context) and nano (0.95B, 768-dim, 8K context). Both support Matryoshka truncation down to 32 dimensions. v5-omni is http 135.6K views · 585 likes · 91 reposts · 12 replies Open on X →

Jina AI@JinaAI_12 May

Pareto frontier of all open-weight omni embedding models (supporting text, image, audio, and video). jina-embeddings-v5-omni-small (1.57B) matches the average score of LCO-7B (8.93B) while using 5.7x fewer parameters. jina-embeddings-v5-omni-nano (0.95B) outperforms LanguageBind 3.3K views · 13 likes · 1 reposts · 1 replies Open on X →

Jina AI@JinaAI_5 Apr

@JinaAI_ @JinaAI_ Exciting times for AI frameworks! The CLI brings a new level of interaction for developers. Integrating feedback loops can really enhance user experience. Keep innovating! 🚀 19 views · 0 likes · 0 reposts · 0 replies Open on X →

Jina AI@JinaAI_14 Mar

@JinaAI_ @grok what is this 108 views · 0 likes · 0 reposts · 1 replies Open on X →

Jina AI@JinaAI_13 Mar

@JinaAI_ Love the CLI support being brought forward! Agents love CLI 423 views · 1 likes · 0 reposts · 0 replies Open on X →

Jina AI@JinaAI_13 Mar

Our official CLI for agents https://t.co/XLhRvLRuDc https://t.co/wFtN8i9YcA 22.2K views · 224 likes · 34 reposts · 7 replies Open on X →

Jina AI@JinaAI_19 Feb

jina-embeddings-v5-text is here! Our fifth generation of jina embeddings, pushing the quality-efficiency frontier for sub-1B multilingual embeddings. Two versions: small & nano, available today on Elastic Inference Service, vLLM, GGUF and MLX. https://t.co/68GGuBRdy4 15.9K views · 116 likes · 16 reposts · 5 replies Open on X →

Jina AI@JinaAI_11 Feb

Most don't know (1) how easy it is to invert embedding vectors back into sentences, (2) this is a perfect task text diffusion models. Here's a 78M parameter model and live demo that recovers 80% of tokens from Qwen3-Embedding and EmbeddingGemma vectors. Works even on multilingual 13.2K views · 173 likes · 21 reposts · 7 replies Open on X →

Jina AI@JinaAI_27 Jan

jina-reranker-v3 was the first listwise reranker to throw all documents into one context window (where traditional rerankers loop over ⟨q,d⟩ pairs) and let them fight it out via self-attention—what we call "last but not late" interaction. Bold or stupid? But not mediocre. Today h 7.4K views · 91 likes · 14 reposts · 1 replies Open on X →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

7.4K27 Jan

13.2K11 Feb

15.9K19 Feb

22.2K13 Mar

423

10814 Mar

195 Apr

3.3K12 May

135.6K

3.8K

3.1K

2.4K

33113 May

Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

1.46%27 Jan

1.54%11 Feb

0.92%19 Feb

1.22%13 Mar

0.24%

0.93%14 Mar

0.00%5 Apr

0.45%12 May

0.52%

0.42%

1.07%

0.38%

0.30%13 May

Reactions — likes, reposts, replies and quotes — divided by views.

What the audience does

Likes50.5%1 251 in total

Reposts7.5%185 in total

Replies1.5%37 in total

Quotes1.7%41 in total

Bookmarks38.9%963 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

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Growth & engagement

Views per post

Engagement rate per post

What the audience does

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