Landed on the cost/accuracy frontier in our data analytics benchmark: https://plotly.com/blog/tencent-hy4-plotly-data-analytics-be..., right in between GPT-6.1 Sol and GPT-6.1 Terra.

Verrrry thorough in its work and therefore verrrrry slow. It spent a long time with each question, running about 26 queries for each question, and in some questions it ran 64 queries. One of the queries that it wrote also took about an hour to run, which affected its overall exam time.

Last year the open weight models were the cheap & good alternatives. Then in September/October that cost story got rewritten by the GPT-6 series and Claude's 5 series.

Hy4 brings open models back on the cost/accuracy frontier, but its behavior is too thorough + slow.

Another thing we're seeing is that post training open models is starting to see results. Particle-1 from last week is also on our frontier, which is post-trained on DeepSeek V4.1 and Fireworks Ember-1 from a few weeks ago was post-trained on Kimi K3.

Take these numbers with a grain of salt: this exam is just for short horizon, low-context data analysis tasks, not full-fledged software projects.