GLM-4.7-Flash: Features, Deployment, and Coding Use
GLM-4.7-Flash is an open-weight text AI model from Z.AI. Its model card identifies it as a 30B-A3B mixture-of-experts model, meaning roughly 30 billion total parameters and about 3 billion activated for each token. Z.AI documents a 200K-token context window, up to 128K output tokens, and an MIT License.
What Is GLM-4.7-Flash
The model is the lightweight member of the GLM-4.7 series. It supports thinking modes, streaming, function calling, context caching, and structured output through the Z.AI platform. The published weights also support self-hosted inference, giving teams a choice between managed API access and infrastructure they operate themselves.
The word “Flash” describes its positioning in the series. It does not mean that every deployment has the same latency or cost; hardware, quantization, serving framework, prompt length, and concurrency all matter.
30B-A3B MoE Architecture
“30B MoE” is incomplete. The more precise description is 30B-A3B MoE. Only a subset of experts is activated for each token, which can reduce inference work relative to activating every parameter. The downloaded model still needs storage, memory, a supported runtime, and operational headroom.
The model card reports about 31B parameters in its files and recommends current development versions of supported inference software. Plan around the official configuration rather than assuming that active parameters equal total memory use.
GLM-4.7-Flash vs Gemma and Phi Models
Model-family names alone do not define a fair comparison. Gemma and Phi each include several sizes and releases, so specify the exact checkpoint.
| Evaluation dimension | What to record |
|---|---|
| Deployment | Weight format, quantization, GPUs, memory, and runtime |
| Coding | Repository snapshot, tool harness, tests, and retries |
| Long context | Prompt length, retrieval accuracy, and latency |
| Cost | Hardware time, hosting, operations, or API usage |
Do not publish a broad winner unless all models run under matched conditions. Vendor tables can inform a shortlist, but your workload should decide the deployment.
Local Deployment Guide
Z.AI documents local serving through vLLM, SGLang, and Transformers. Its examples use tensor parallelism across four devices for vLLM and SGLang, but they do not establish a universal minimum hardware requirement. Do not assume that an unspecified consumer device can run the model well.
Before deployment:
- Confirm the unquantized or quantized weight size and available accelerator memory.
- Validate that your selected runtime supports the GLM-4.7 tool and reasoning parsers.
- Start with a short context, then measure memory and latency as context grows.
- Test concurrency separately from single-request success.
Agentic Coding Performance
The model card publishes results for LiveCodeBench, SWE-bench Verified, tool use, and other tasks, with evaluation settings and some harness modifications. Treat these as Z.AI-reported results. They support testing the model for code editing and tool-driven work, but they do not prove production reliability in your repository.
Use three practical tests: fix a reproducible issue, implement a small feature across code and tests, and complete a tool-using task with an explicit stop condition. Review every diff and record test evidence.
Using It in Verdent
Verdent's current built-in catalog lists GLM-5.2, not GLM-4.7-Flash. An older Verdent changelog entry announced GLM-4.7, but that is a different model name and is not evidence for the Flash variant. Check the current model catalog rather than assuming native support.
Size the Deployment Before You Pilot
Choose the weight format and serving runtime first, then measure a real coding task at the context and concurrency you expect to use.
Inspect Verdent's active GLM catalog Download Verdent for a deployment pilot
Frequently Asked Questions
How large is GLM-4.7-Flash?
It is a 30B-A3B mixture-of-experts model with about 30 billion total parameters and about 3 billion activated parameters per token.
What license does GLM-4.7-Flash use?
The official model card lists the MIT License. Review the repository license and any dependency terms before redistribution or production deployment.
Can GLM-4.7-Flash run on consumer hardware?
The official sources do not define a universal consumer-hardware minimum. Feasibility depends on quantization, memory, context length, concurrency, and the serving runtime.
Is GLM-4.7-Flash free?
Z.AI's current API pricing table lists the model's token usage as free. Rate limits, account availability, hosting cost, and future pricing remain separate considerations.