An Open-Source IDE Built for GLM
GLM 5.2 is compelling for a simple reason: it gets unusually close to Claude Opus 4.8 in quality while sitting at a much lower cost point. stagewise gives it a local IDE built for long-running agent work.
Why GLM 5.2 stands out right now
The main case for GLM 5.2 is not that it is merely good for an open-weight model. It is that it gets surprisingly close to Claude Opus 4.8 on capability while landing in a very different cost range.
Artificial Analysis currently describes GLM-5.2 as the new leading open-weights model on its Intelligence Index. In the benchmark snapshot below, GLM-5.2 scores 51 on the index, while Claude Opus 4.8 sits at 56. The cost-per-intelligence view places GLM-5.2 much lower on cost at the same time.
Benchmark charts from Artificial Analysis. GLM-5.2 is shown close to Claude Opus 4.8 on intelligence, while sitting in a cheaper part of the cost curve.
stagewise is a local IDE for software engineers who work with coding agents. It runs on your machine, connects to your development environment, and can orchestrate multiple agents in parallel. The runtime is model-agnostic, so you can use frontier models, open-weight models, or local inference. It also manages context aggressively, which matters once a task starts stretching across many turns. Learn more about stagewise.
Keeping long-running GLM tasks practical
GLM 5.2 is strong enough that the bottleneck quickly becomes runtime efficiency rather than raw model quality. Once a task runs for dozens of turns, this is where the agent matters.
Our agent keeps the early part of the conversation stable across multiple turns, so the prefix stays the same from one request to the next. That improves cache hit rates and often lowers both latency and cost.
Also, when the environment changes (files were changed, skills were enabled/disabled, etc.), the system does not resend the full context. If you rename a file, open a tab, or move a selection, it appends a compact state delta to the model context instead of rebuilding everything. The model still gets an up-to-date view of the workspace, but with way fewer tokens.
The runtime also automatically compresses context as tasks grow. Older turns are summarized and pruned so the model keeps a focused working set. That makes longer jobs practical: multi-file features, refactors that unfold over hours, or debugging sessions for tricky issues.
Use GLM through our hosted setup, your own endpoint, or local inference
You can use GLM 5.2 through a stagewise Account and start without managing a separate API key.
If you already have GLM access elsewhere, you can point the IDE at that setup instead. Existing API subscriptions, third-party endpoints, and local inference all work with the same runtime. See the custom providers docs for the setup details.
We think agent tools should not dictate where you buy inference. You choose the billing and hosting model that fits your setup.
Working with image files despite GLM's lack of vision
One major drawback of the current GLM models is that they do not have native vision capabilities. They cannot inspect image contents directly. In a real codebase, that is a meaningful limitation because screenshots, exported assets, and design files are still part of the workspace.
The stagewise agent compensates for that limitation. Our file transformation pipeline turns each image into structured file context the model can reason about in text: file type, dimensions, format, and a compact representation of the image. That keeps image files inside the same workflow as source files and config.
The file transformation pipeline turns files into structured content parts.
Image files are transformed into structured metadata and a compact representation. GLM only operates on that metadata layer.
This does not give GLM vision. It does give the model enough context to work sensibly with image files in a repository — for example when updating references, resizing assets, or understanding the layout of a design directory.