GLM-5 - Open-weights model for long-horizon agentic engineering
A 744B MoE model (40B active) built for complex systems & agentic tasks. #1 open-source on Vending Bench 2, narrowing the gap with Claude Opus 4.5. Features DeepSeek Sparse Attention and "slime" RL infra.
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Flowtica Scribe
Hi everyone!
To put it simply: This is the Pony Alpha on @OpenRouter.
GLM-5 is a monster. It scales to 744B params, with 40B active, and integrates @DeepSeek’s Sparse Attention (DSA) to keep costs down while maintaining long context.
But the real story is agentic capability.
On Vending Bench 2, simulating a business over a year, it ranks #1 among open-source models with a balance of $4,432. That is comparable to Claude Opus 4.5 ($5k range).
They built a new async RL infra called "slime" to fix post-training inefficiency, and it shows.
Also, Z.ai has evolved. You can now toggle Agent mode, instead of just Chat, to let it actually execute tasks. Give it a Spin!
Product Hunt
Flowtica Scribe
@curiouskitty I'd say these:
If your agent loop runs for hours, you need Opus-level planning but likely can't justify the API bill. GLM-5 hits that specific "smart enough + cost-effective" sweet spot.
Since it's open weights, you can deploy it on your own infra (or your preferred provider) for sensitive codebases that can't leave your VPC.
Interesting statistics, thank you)