I used Jev to classify 1,018 AI research papers.

Classified 1,018 AI research papers with Jev against 24 candidate topics, spending $0.08 total at 256ms median latency per paper.

The original post

I used Jev to classify 1,018 AI research papers.

The result: $0.08 total cost and 256ms median end-to-end latency per paper.

The pipeline was:

1. Summarize each paper with DeepSeek V4 Flash

2. Send the title + summary + 24 possible topics to Jev

3. Use Jev to classify each paper

4. Visualize everything on

The summaries cost $3.99 on @togethercompute. The classifications cost $0.08 on @typesafeai.

So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year.

I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything.

I’m running evals on the Jev classifications before replacing the current ones, but the site is already live:

What the author linked

• 1kpapers.com
• The Year in AI Papers: Essential Research from 2025–2026 — Read clear summaries of the most important AI papers from 2025–2026, organized by topic, research lab, citations, code, and publication date.

Engagement when collected

Numbers are a snapshot taken from X when the case was added to the library (schema v1, collected 2026-09-19); they will not match today.