GLM 5.2: A powerful coding model with a million-token context

GLM 5.2 is built for long-context work

GLM 5.2’s main feature is a context window of 1M. This is useful when a single prompt needs to include a large project, a lengthy contract, several technical documents, or a month of correspondence. The model was developed by Chinese company Z.ai and is designed for tasks where the answer requires more than an immediately plausible paragraph: it must preserve connections between different parts of the input.

On QueryWise, GLM 5.2 answers alongside other leading models, so it is easy to evaluate in context rather than in isolation. The current top 4 includes Claude Fable 5, GPT-5.6 Sol, Kimi K3, Grok 4.5, while Claude Fable 5 holds first place with 9.8/10. Against that backdrop, GLM 5.2 looks less like an all-purpose champion and more like a dependable workhorse with a strong technical focus.

What drew me to the model was the combination of context length and price. A million tokens sounds impressive, but window size alone guarantees nothing: a model can miss details, mix up requirements, or produce a confident answer containing errors. That is why we focused on practical performance on QueryWise rather than an attractive specification.

For large files, it is a strong candidate. For a short question such as “How do I cook beshbarmak?”, the advantage is barely noticeable.

On QueryWise, it reasons well without challenging the top spot

According to our current measurements, GLM 5.2 scored 8.6/10 overall and ranks 11th. Two figures stand out in particular: programming at 8.9/10 and reasoning at 9.0/10. This is the profile of a model that is useful when a task requires breaking down conditions, finding contradictions, and proposing a step-by-step solution.

The gap to the leader is 1.2 points: Gemini 3.5 Flash is currently above it with 8.7/10, while Gemini 3.1 Pro is below it with 8.2/10. The current top 4 compares as follows: Claude Fable 5, GPT-5.6 Sol, Kimi K3, Grok 4.5. Leaders may have an edge in response style, speed, or accuracy on particular prompt types, so one overall score should not be treated as absolute truth.

GLM 5.2’s median response time in our telemetry is 38 seconds. That is not lightning-fast. On the other hand, reliability was 100% across the prompts we collected: the model did not drop responses or produce technical failures. We added it to our working test set on release day and separately checked long inputs, code, and questions with multiple constraints.

My conclusion for now is simple: do not expect instant replies, but the model has earned a place in comparative testing through its stability and reasoning.

Tasks GLM 5.2 actually handles well

The first strong use case is coding. You can give it a prompt such as: “Write a Python script that reads a sales CSV from Almaty, groups the data by month, and saves a report to Excel.” A good answer should account for the file format, handle empty values, and explain how to run the script. Its programming score of 8.9/10 confirms that technical instructions are one of the model’s strongest areas.

The second example is planning a complex everyday task. For instance: “Create a five-day itinerary from Almaty to Bishkek, taking the budget, border crossing, and a backup option in case of delays into account.” GLM 5.2 can break the task into stages, state its assumptions, and keep transport separate from expenses. Still, schedules and route information should be checked against current sources because the model is not a substitute for carriers’ up-to-date websites.

The third use case is document work. You can upload tender requirements and ask: “Create a table of mandatory documents, deadlines, and risks for a participant from Kazakhstan.” A 1M context window helps it keep a long source in view, although quality also depends on how the original file is structured.

For study, it is useful as an explainer: ask it to explain derivatives in simple terms and then provide three exercises with answer checks. But submitting an unverified take-home assignment is a bad idea. The model can make persuasive mistakes, especially with dates, formulas, and local rules.

A large context window does not remove the usual weaknesses

GLM 5.2 has noticeable limitations. A median speed of 38 seconds is suitable for thoughtful tasks, but frustrating in a short chat when you need an answer within a few seconds. The delay becomes more noticeable after several consecutive follow-up questions. For quick everyday prompts, another model in the ranking may be more convenient.

Long context should not be treated as a guarantee of perfect memory either. If you upload hundreds of pages containing repetition, tables, and a poorly recognized scan, the model may miss an important line. It can produce a polished conclusion based on a wrong premise. This is especially risky in medical, legal, and financial matters: GLM 5.2 is an analysis assistant, not a doctor, lawyer, or financial adviser.

There is also a language nuance. The model understands Russian, but its wording can sometimes sound less natural than that of the best competitors. Its Kazakh output should be checked separately: translating a simple message and producing a complete formal document are different tasks. A prompt such as “Translate this customer notice into Kazakh while preserving a polite business tone” is worth giving to another model as well so you can compare the versions.

We also do not recommend trusting code without running it. A high programming score of 8.9/10 is a good signal, but it is not a license to publish a script without testing, checking dependencies, and protecting data.

The price and access from Kazakhstan look reasonable

On QueryWise, the average cost of a question to GLM 5.2 is ≈10 ₸. Price matters more than attractive rankings when you use a model every day: overpaying for the most expensive option is rarely justified for a quick text check. Here, you can ask technical questions, compare answers from several systems, and pay in tenge without dealing with a separate foreign subscription.

The service is available from Kazakhstan, with an interface in Russian and Kazakh. New users receive three free questions to get started — enough to check speed, style, and performance on a task of your own. I would begin with a short prompt, then upload a small document, and only after that send a large project.

Against the current top 4 — Claude Fable 5, GPT-5.6 Sol, Kimi K3, Grok 4.5 — GLM 5.2 is not the strongest choice for every scenario. Its value lies elsewhere: it gives you a model rated 8.6/10, with reasoning at 9.0/10, programming at 8.9/10, and a 1M context window, without complicated access from Kazakhstan. At the same time, reliability in our logs was 100% — not just a promise from a marketing brochure.

My verdict: GLM 5.2 is a good fit for developers, analysts, and anyone working with long materials. It is expensive only for simple conversations, but at ≈10 ₸ per question it is quite reasonable as a second pair of eyes. If you need the fastest possible conversational model, look higher in the table at Gemini 3.5 Flash; if code and large documents matter, GLM 5.2 is worth trying.