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
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
2.4K
3.8K
3.1K
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%
0.38%
0.42%
1.07%
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.
Followers by day
4 Sep
Daily snapshots since 04 Sep 2026; the dashed line is the starting count.
Latest posts
@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
13 May 2026
· Open on X →
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
12 May 2026
· Open on X →
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
12 May 2026
· Open on X →
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
12 May 2026
· Open on X →
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
12 May 2026
· Open on X →
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
12 May 2026
· Open on X →
@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
05 Apr 2026
· Open on X →
@JinaAI_ Love the CLI support being brought forward! Agents love CLI
423 views · 1 likes
· 0 reposts · 0 replies
13 Mar 2026
· Open on X →
Our official CLI for agents https://t.co/XLhRvLRuDc https://t.co/wFtN8i9YcA
22.2K views · 224 likes
· 34 reposts · 7 replies
13 Mar 2026
· Open on X →
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
19 Feb 2026
· Open on X →
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
11 Feb 2026
· Open on X →
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
27 Jan 2026
· Open on X →
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