It does really well on "AA-Omniscience Non-Hallucination Rate", far higher than DeepSeek, GPT 5.5 or Fable. I really like that benchmark because it's one of the few benchmarks that allows LLMs to elect not to answer if they are unsure and punishes them for trying to bullshit their way through the benchmark
Local models are already useful today. The next milestone is getting this level of performance onto truly affordable hardware.
I also tested it[0]: quite similar to GLM 5, a few percent better, 30% faster and 50% more expensive.<p>[0]: <a href="https://aibenchy.com/?q=glm" rel="nofollow">https://aibenchy.com/?q=glm</a>
PS: Just added a cool feature, so you can filter the leaderboard for multiple models at once, by using a comma, like: <a href="https://aibenchy.com/?q=glm,claude" rel="nofollow">https://aibenchy.com/?q=glm,claude</a>
still 1/4 of the price of anthropic and openai models though
It's always nice to see how open source models growing, hope we will have good performance with lower tier hardware some day.
I want to trust their benchmarks but when they have Muse Spark over GPT-5.5, it gives me pause.
still quite verbose at 140m output tokens, but this is on max thinking. high should do better.
Some more discussion: <a href="https://news.ycombinator.com/item?id=48567759">https://news.ycombinator.com/item?id=48567759</a>
One or two more releases and they will reach Fable level.