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
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gertlabs
On our multi-agent coding and reasoning evaluations, GLM 5.2 is the first model we've tested that crossed the threshold of being on par with or better than Opus 4.6 (although as usual, we have GLM 5.2 and most other Chinese models a bit below most other benchmarks with test methodologies that are more vulnerable to benchmaxxing).
tangent question: Claude code seems to be very much loved and suggested by most major Chinese LLM using the env vars to change the server. that however means you lose a lot of anthropic tools like auto mode, running shells, monitors/crons. is there a way to get those with non anthropic plans?
lanycrost
It's always nice to see how open source models growing, hope we will have good performance with lower tier hardware some day.
theturtletalks
I want to trust their benchmarks but when they have Muse Spark over GPT-5.5, it gives me pause.
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XCSme
I also tested it[0]: quite similar to GLM 5, a few percent better, 30% faster and 50% more expensive.
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
On our multi-agent coding and reasoning evaluations, GLM 5.2 is the first model we've tested that crossed the threshold of being on par with or better than Opus 4.6 (although as usual, we have GLM 5.2 and most other Chinese models a bit below most other benchmarks with test methodologies that are more vulnerable to benchmaxxing).
Data at https://gertlabs.com/rankings
tangent question: Claude code seems to be very much loved and suggested by most major Chinese LLM using the env vars to change the server. that however means you lose a lot of anthropic tools like auto mode, running shells, monitors/crons. is there a way to get those with non anthropic plans?
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.
I also tested it[0]: quite similar to GLM 5, a few percent better, 30% faster and 50% more expensive.
[0]: https://aibenchy.com/?q=glm
Local models are already useful today. The next milestone is getting this level of performance onto truly affordable hardware.
One or two more releases and they will reach Fable level.
still quite verbose at 140m output tokens, but this is on max thinking. high should do better.
Some more discussion: https://news.ycombinator.com/item?id=48567759