google/embeddinggemma-2
Feature Extraction • 0.7B • Updated • 7.56k • 815
🫠 whatever
opsi ryougi
opsirygi
P(doom) Rejects the premise
AI & ML interests
None yet
Recent Activity
liked a model about 11 hours ago
google/embeddinggemma-2 liked a model 3 days ago
indobenchmark/indobert-base-p1 liked a model 6 days ago
Cloudflare/clefOrganizations
None yet
liked a
model about 11 hours ago
liked a
model 3 days ago
liked a
model 6 days ago
liked a
model 7 days ago
liked a
dataset 10 days ago
published a
model 12 days ago
liked a
dataset 13 days ago
published a
model 13 days ago
liked a
model 15 days ago
liked a
model 17 days ago
updated a
model 18 days ago
liked a
model 18 days ago
upvoted an article 18 days ago
Article
Anatomy of a Frontier Lab Agent Intrusion: A Technical Timeline of the July 2026 Incident
- +2
hlarcher, XciD, raphael-gl, chris-rannou
• • 507 liked a
model 19 days ago
liked a
dataset 20 days ago
reacted to ginigen-ai's post with 🤗 20 days ago
Post
2967
OpenRouter Leaderboard — every model, every provider, one comparable table. Price, precision, uptime, measured latency and language quality on the same axes.
Building it turned up three things.
We graded 330 models on Korean and two axes collapsed.
Honorifics — only 8.5% earn an A
Knowledge of Korean institutions — 9.4%
Every other axis sits above 31%
Fluency hides it. A model can write clean, natural Korean and still attach an honorific to a coffee cup. Fluent and wrong at the same time is worse than obviously broken, because nobody catches it in review.
A 2023 model beats the 2026 flagships. gpt-3.5-turbo-16k scores a perfect 3.00. Korean cannot be inferred from release date, parameter count or English benchmarks — it has to be measured, per model.
Quality, value and speed are three different models. Across five axes, the same model almost never takes two columns.
425 models, latency measured on 329 on a paid API, Korean graded on 330. Three languages, three currencies, daily refresh, open API, no key.
📝 https://huggingface.co/blog/ginigen-ai/openrouter-leaderboard 🔎 ginigen-ai/open-router-leaderboard
Building it turned up three things.
We graded 330 models on Korean and two axes collapsed.
Honorifics — only 8.5% earn an A
Knowledge of Korean institutions — 9.4%
Every other axis sits above 31%
Fluency hides it. A model can write clean, natural Korean and still attach an honorific to a coffee cup. Fluent and wrong at the same time is worse than obviously broken, because nobody catches it in review.
A 2023 model beats the 2026 flagships. gpt-3.5-turbo-16k scores a perfect 3.00. Korean cannot be inferred from release date, parameter count or English benchmarks — it has to be measured, per model.
Quality, value and speed are three different models. Across five axes, the same model almost never takes two columns.
425 models, latency measured on 329 on a paid API, Korean graded on 330. Three languages, three currencies, daily refresh, open API, no key.
📝 https://huggingface.co/blog/ginigen-ai/openrouter-leaderboard 🔎 ginigen-ai/open-router-leaderboard
liked a
model 20 days ago
liked a
dataset 21 days ago
liked 2
models 23 days ago
