Can you run GLM-5.2 locally?
See actual BF16, FP8, and quantized artifact sizes, realistic RAM and VRAM tiers, and when an API is the more sensible choice. Check the hardware guide →
GLM-5.2 Guide: API, Tools & Benchmarks
Research-backed answers on GLM-5.2 API pricing, the 1M-token context window, local hardware, coding tools, and model comparisons.
Independent Publisher claims are labeledEvidence first Specifications link to sourcesDecision focused Costs and constraints included
What is GLM-5.2?
Section titled “What is GLM-5.2?”GLM-5.2 is Z.ai’s text-in, text-out flagship model for long-horizon coding and agentic engineering. The official API documents a 1,048,576-token context window, up to 131,072 output tokens, thinking modes, function calling, context caching, structured output, and MCP integration. The weights are also published under the MIT license.
Those headline facts do not answer every practical question. A one-million-token ceiling does not guarantee perfect recall across a million tokens, an MIT-licensed checkpoint does not make a 753-billion-parameter model easy to self-host, and a low token price does not automatically make a subscription plan or third-party provider cheaper for every workload. GLM52.ai focuses on those decision-changing details.
Our dedicated What is GLM-5.2? technical explainer unpacks the 744B-versus-753B parameter labels, mixture-of-experts routing, IndexShare, open-weight licensing, and what the 1M context does—and does not—guarantee.
The GLM model release history traces the family from the 2021 research paper through GLM-5.2, then uses the recent flagship cadence to forecast the next release window and feature direction.
Specifications and prices on this page were checked against the official GLM-5.2 model guide, Z.ai pricing, and official model repository on July 15, 2026.
GLM-5.2 specifications that matter
Section titled “GLM-5.2 specifications that matter”Context window1,048,576 tokens
The model limit; an app or inference provider may expose less.
Maximum output131,072 tokens
A ceiling, not a recommendation to request very long answers by default.
Model size753B total parameters
Roughly 40B are active per token, but the full checkpoint still needs storage.
Release modelMIT-licensed weights
Downloadable and commercially permissive; practical hosting remains demanding.
The most valuable long-context test is not “does the request fit?” It is whether the model can retrieve the right evidence, preserve constraints, and finish a multi-step task without drifting. Likewise, model size should be evaluated using total checkpoint size plus runtime and KV-cache overhead, not only the smaller number of parameters activated for each token.
Choose your GLM-5.2 starting point
Section titled “Choose your GLM-5.2 starting point”How can you try GLM-5.2 for free?
Compare no-cost entry points without confusing a chat trial, limited credits, and repeatable production API access. Compare free options →
GLM-5.2 or Kimi K3?
Compare launch-week coding evidence, API costs, 1M context, native vision, tool controls, and the real status of both open-weight releases. Read the Kimi K3 comparison →
GLM-5.2 or MiniMax M3?
Compare coding evidence, direct API cost, the 512K price boundary, native vision, reasoning controls, and open-weight license terms. Read the MiniMax M3 comparison →
GLM-5.2 or Claude Opus 4.8?
Compare coding evidence, 1M context, vision, API costs, open weights, and the workload each model fits best. Read the evidence-based comparison →
GLM-5.2 or Claude Fable 5?
Compare current coding evidence, direct API costs, 1M context, native vision, safety fallback, retention, and deployment control. Read the Fable 5 comparison →
GLM-5.2 or GPT-5.6 Sol?
Compare coding evidence, direct API costs, the 272K price boundary, image input, managed tools, and deployment control. Read the GPT-5.6 Sol comparison →
GLM-5.2 or Grok 4.5?
Compare independent intelligence and speed data, coding evidence, direct API costs, the 200K price boundary, image input, tools, and weights. Read the Grok 4.5 comparison →
GLM-5.2 or GPT-4o?
Compare documented context, modalities, API features, pricing, and deployment control—without invented quality scores. Read the model comparison →
Can you fine-tune GLM-5.2?
Review training stacks through architecture support, distributed execution, adapter workflows, and the limits of generic compatibility claims. Review the toolchain →
GLM-5.2 API pricing and coding plans
Section titled “GLM-5.2 API pricing and coding plans”Z.ai currently lists the direct GLM-5.2 API at $1.40 per million input tokens, $0.26 per million cached input tokens, and $4.40 per million output tokens. A request with 100,000 uncached input tokens and 10,000 output tokens therefore costs about $0.184 at those published rates: (0.1 × $1.40) + (0.01 × $4.40).
If a workload repeats a long policy, tool catalog, or reference document, use the GLM-5.2 prompt-caching guide to preserve the reusable prefix, verify cached_tokens, and convert the observed hit rate into cost.
The GLM Coding Plan is a separate subscription product, not a bucket of ordinary pay-as-you-go API credit. It starts at $18 per month and is designed for coding tools such as Claude Code, Cline, and OpenCode, with rolling and weekly usage limits. Compare the subscription with direct API billing using the number and size of agent calls you actually make; do not treat “$18/month” as unlimited general API access.
| Access route | Best fit | Important constraint |
|---|---|---|
| Direct Z.ai API | Products, automations, measured workloads | Pay per token; monitor prompt growth and retries |
| GLM Coding Plan | Interactive coding-agent use | Subscription quotas and model multipliers can apply |
| Third-party provider | Existing multi-model stack or regional infrastructure | Context, features, and prices may differ from Z.ai’s direct API |
| Self-hosted weights | Teams needing deployment control | Very high memory, storage, and operations requirements |
Prices and plan rules can change. Check the live provider documentation before purchasing or budgeting.
Can you run GLM-5.2 locally?
Section titled “Can you run GLM-5.2 locally?”Yes in the literal sense—the weights are downloadable—but not like a typical desktop model. The official repository reports 753B parameters. The published BF16 files total roughly 1.5 TB, while the official FP8 files are about 756 GB, before runtime buffers, KV cache, the operating system, and concurrency headroom.
Community quantization and experimental weight-streaming methods can reduce resident memory, but “it starts” and “it is useful at interactive speed” are different success criteria. Our GLM-5.2 local hardware guide separates file size, working memory, context cost, and realistic serving paths.
How we review GLM-5.2
Section titled “How we review GLM-5.2”Editorial method
Document facts first; test claims second
We separate official specifications, publisher benchmark claims, third-party limits, transparent arithmetic, and our own observations. A result is not presented as an independent benchmark unless the prompt set, settings, environment, and limitations are available.
This approach matters because model names can refer to different products: an official API, a coding subscription, an open-weight checkpoint, or a third-party hosted endpoint. They can share the name “GLM-5.2” while exposing different context limits, tools, rate limits, and costs.
Frequently asked GLM-5.2 questions
Section titled “Frequently asked GLM-5.2 questions”Is GLM-5.2 open source?
Section titled “Is GLM-5.2 open source?”The official weights are released under the MIT license. We usually say open-weight because access to weights does not by itself mean that every training dataset, preprocessing pipeline, and training artifact has been published. The MIT release still gives developers unusually broad permission to download, modify, fine-tune, and deploy the checkpoint subject to the license.
Does GLM-5.2 really support a one-million-token context window?
Section titled “Does GLM-5.2 really support a one-million-token context window?”Z.ai’s direct model specification lists 1,048,576 tokens. A coding tool or third-party provider may impose a lower limit, and maximum capacity is not the same as reliable retrieval at every distance. Verify the endpoint limit and test long-context accuracy on your own repository or documents.
Is GLM-5.2 multimodal?
Section titled “Is GLM-5.2 multimodal?”The base GLM-5.2 API is documented as text input and text output. Some Z.ai plans add vision through a separate MCP capability; that does not turn the base model endpoint into a native image-input model. Choose a vision-language model when direct image understanding is a hard requirement.
What should a developer read first?
Section titled “What should a developer read first?”Start with the local hardware guide if deployment control matters or the free-access comparison if you are evaluating the model. For an API decision, compare GLM-5.2 with Claude Opus 4.8 for hard agentic coding and 1M context, or with GPT-4o for an established multimodal OpenAI workflow.