GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models [pdf]

Really appreciate the depth of this paper; it's a welcome change from the usual model announcement blog posts. The Zhipu/Tsinghua team laid out not just the 'what' but the 'how,' which is where the most interesting details are for anyone trying to build with or on top of these models.

The post-training methodology (Sec 3) is what really stands out to me. The idea of creating specialized 'expert models' for reasoning, agents, and chat, and then distilling their capabilities into a final unified model is a fascinating approach. It feels like a more structured way to solve the "jack of all trades, master of none" problem that can plague generalist models. Instead of just mixing all the data, they're essentially having a generalist learn from a committee of specialists.

A couple of the findings from their RL experiments are pure gold for anyone working in this space. The counter-intuitive result that a single-stage RL process at the full 64K context length outperforms a progressive, multi-stage approach (Fig 6) is a fantastic lesson. I've seen teams assume the opposite would be true. Also, the pragmatic choice to use an XML-like template for function calls to avoid JSON escaping hell (Fig 4) may be a small but brilliant engineering decision that makes a huge difference in practice. Wrangling escaped code inside JSON turns out to be a mess.

The performance on SWE-bench is impressive, putting it in the same league as much larger or proprietary models. What I’d love to see, and maybe others here have thoughts, is whether this hybrid training recipe holds up outside ARC-style evals. For example, do the agentic improvements transfer to messier, real-world workflows where APIs are undocumented, partial failures are common, and user input is full of ambiguity?

I've been playing around with GLM-4.5 as a coding model for a while now and it's really, really good. In the coding agent I've been working on, Octofriend [1], I've sometimes had it on and confused it for Claude 4. Subjectively, my experience has been:

1. Claude is somewhat better at whole-codebase tasks, where you need to reason over a bunch of context and consider system interactions.

2. GLM-4.5 is somewhat better at being "honest" — i.e. I rarely see it doing the things Claude does like making broken tests pass by changing the test instead of fixing the bug.

Both are quite good though, and GLM-4.5 has found bugs that both Claude 4 Sonnet and 4.1 Opus have failed to catch. In general I think Claude wins a little more frequently on debugging tasks than GLM-4.5, but it's close.

Compared to GPT-5, both Claude and GLM feel like they're more consistent, although GPT-5 sometimes has long brilliant runs where it nails everything with subjectively higher code quality than either of the latter. However, once GPT-5 goes off the rails, it's hard to get it back on track, so it can be a bit frustrating to work with in comparison.

1: https://github.com/synthetic-lab/octofriend

So GLM-4.5 series omits the embedding layer and the output layer when counting both the total parameters and the active parameters:

> When counting parameters, for GLM-4.5 and GLM-4.5-Air, we include the parameters of MTP layers but not word embeddings and the output layer.

This matches with the calculation I did for GLM-4.5 (355B A32B):

    In [14]: 356732107008 - (775946240 * 2) # token_embd / output are 775946240 each. assume omitted
    Out[14]: 355180214528

    In [15]: 356732107008 - 339738624000 - (775946240 * 2) # parameters that are always active
    Out[15]: 15441590528

    In [16]: 339738624000 * 8 / 160 # parameters from activated experts
    Out[16]: 16986931200.0

> We refer to the models as “120b” and “20b” for simplicity, though they technically have 116.8B and 20.9B parameters, respectively. Unembedding parameters are counted towards active, but not embeddings.

And Qwen3 series includes both the embedding layer and the output layer when counting both the total parameters and the active parameters.

Why there is no standard in counting? Which approach is more accurate?

Seems like we may get local, open, workstation-grade models that are useful for coding in a few years. By workstation-grade I mean a computer around 2000 USD, and by useful for coding I mean around Sonnet 4 level. Current cloud based models are fun and useful, but a tool that is / will be so core to the developer experience, I want to be able to run locally.

This feels like the first open model that doesn’t require significant caveats when comparing to frontier proprietary models. The parameter efficiency alone suggests some genuine innovations in training methodology. I am keen to see some independent verification of the results and to see how if does on Aider’s LLM Leaderboard.

The sheer number of things "they observed" in this paper that could be whole papers in themselves is astounding! Lots of great stuff in here around training processes and data collection+synthesis.

Does anyone have any background information on the authors? Have they published similarly impressive works in the past?

Fantastic release, and it's under the Apache license too. I'm so happy that we've got open source models pushing the envelope.

For people who, like me, prefer to read the abstract first: https://www.arxiv.org/abs/2508.06471

This is a great model for software development - probably the best of the freely available ones.

I’ve been using it and I think it’s on par with sonnet.

Why was qwen3 omitted from the coding benchmark but not other benchmarks?

What’s up with the charts? In their first figure they show sonnet 4 as having a swebench score of 53; in the next figure it’s close to 70. In reality it’s closer to the latter. [0]

[0] https://www.swebench.com/