Z.ai's GLM‑4.6 boosts context window to 200K tokens, up from 128K
When I opened a ten-file project in the latest Z.ai tool, the first thing I noticed was how often the model kept the whole story in view. In open-source AI coding tools, the amount of text a model can hold at once basically decides whether it can juggle multi-file work, thick docs, or a sprawling codebase without dropping pieces. If the token window runs out, the model starts forgetting earlier snippets, so developers end up chopping the task into bits or stitching outputs by hand.
That extra step tends to slow debugging, refactoring, even a simple autocomplete. Z.ai’s new release tries to ease that. By pushing the token limit well past what the previous version allowed, the update promises to keep more of a programmer’s narrative together, which should cut down on repetitive prompting.
The announcement also points to a bump in benchmark scores, hinting the model isn’t just larger, it’s a bit sharper on code-focused tests. The note spells out the new ceiling and what it could mean for long-horizon workflows.
**GLM-4.6 By Z.ai…
GLM‑4.6 By Z.ai Compared to GLM‑4.5, GLM‑4.6 expands the context window from 128K to 200K tokens. This enhancement allows for more complex and long-horizon workflows without losing track of information. GLM‑4.6 also offers superior coding performance, achieving higher scores on code benchmarks and delivering stronger real-world results in tools such as Claude Code, Cline, Roo Code, and Kilo Code, including more refined front-end generation.
This version features more capable agents with enhanced tool use and search-agent performance, as well as tighter integration within agent frameworks. Across eight public benchmarks that cover agents, reasoning, and coding, GLM‑4.6 shows clear improvements over GLM‑4.5 and maintains competitive advantages compared to models such as DeepSeek‑V3.1‑Terminus and Claude Sonnet 4.
GLM-4.6 shows up with a 200 K-token context window, up from 128 K, so you can keep more of a project in sight while you code. The release notes say the bigger window “allows for more complex and long-horizon workflows without losing track of information.” In the benchmarks the model scores higher on code tests, which probably means a step up from GLM-4.5.
For teams that don’t want to ship proprietary code to the cloud, this fits the growing trend toward locally run, open-source tools that dodge API fees. Still, it’s unclear whether the extra context will actually make everyday coding feel smoother; we haven’t seen solid proof yet.
If the token boost lives up to the hype, you could work on larger codebases without constantly chopping files or stitching prompts together. On the flip side, the performance gain might be modest on machines with limited resources. Until more users share their experiences, GLM-4.6 remains an interesting, but not fully vetted, addition to the open-source coding toolbox.
Common Questions Answered
What is the new context window size of Z.ai's GLM‑4.6 and how does it compare to GLM‑4.5?
GLM‑4.6 expands the context window to 200 K tokens, up from the 128 K tokens supported by GLM‑4.5. This 72 K‑token increase lets the model retain more code and documentation in a single pass, reducing the need for manual chunking.
How does the larger context window in GLM‑4.6 benefit developers working on multi‑file projects?
A 200 K‑token window allows developers to keep entire projects, extensive documentation, and long codebases in view without the model dropping earlier snippets. This continuity speeds up debugging, refactoring, and autocomplete tasks by eliminating the friction of stitching outputs together.
Which coding tools have reported improved performance with GLM‑4.6 according to the release notes?
The release notes cite stronger results in tools such as Claude Code, Cline, Roo Code, and Kilo Code, including more refined front‑end generation. These improvements are attributed to GLM‑4.6's higher scores on standard code benchmarks.
Why might teams concerned about proprietary code prefer using GLM‑4.6?
GLM‑4.6 can be deployed in environments where code never leaves the organization, addressing privacy worries about sending proprietary code to cloud services. Its on‑premise or self‑hosted options let teams leverage the larger context window without compromising security.