Note: This article is adapted from the official Phala blog post. Original article published June 16, 2026 at https://phala.com/posts/glm-5-2-open-source-sota-confidential-ai-phala
Phala Network just became a launch partner for GLM-5.2, the latest open-source model from Z.ai. If you build agents, run long-context workflows, or work with sensitive data in production, this one is worth a closer look. The partnership brings together a model that sits at the top of open-source coding benchmarks with infrastructure built specifically for private, verifiable AI inference.
GLM-5.2 comes with a 1 million token context window and strong performance across long-horizon coding tasks. On FrontierSWE it trails Claude Opus 4.8 by just 1% and edges out GPT-5.5 by the same margin. It also scored first on Design Arena’s code category with 1360 Elo, and shows a sharp improvement over GLM-5.1 on Terminal-Bench 2.1 and SWE-bench Pro. For an open-source model with open weights, those numbers put it in serious company.
Why Running It on Phala Changes the Equation
Model capability gets a lot of attention, but where and how a model runs matters just as much when the workloads are sensitive. Agents handling source code, customer records, legal documents, or internal business logic carry real privacy risk inside every prompt and tool trace. Phala addresses this by running inference inside hardware-isolated environments called TEEs, where execution is protected and the runtime properties can be independently verified. Redpill provides an OpenAI-compatible API layer on top, so developers can route into this stack without changing their existing integrations.
How It Performs in a Real Environment
Phala ran their own benchmark of GLM-5.2-FP8 on an 8xH200 setup using SGLang. At standard context lengths, it holds above 25 tokens per second per user through 64 concurrent users, with aggregate throughput continuing to scale. At longer input shapes it maintains that same threshold through 32 concurrent users before latency pressure increases at higher concurrency. These are practical serving numbers that reflect how the model actually behaves under load, not just isolated lab conditions.
Where to Access It
GLM-5.2 is live on both Phala and Redpill at $1.40 per million input tokens and $4.60 per million output tokens. Most infrastructure conversations treat privacy as something added after deployment. Phala’s approach builds it into the deployment layer from the start, and this launch is a clear signal of where that infrastructure is heading.
GLM-5.2 on Phala: https://phala.com/models/z-ai/glm-5.2 on Redpill: https://redpill.ai/models/z-ai/glm-5.2
Reach out to the Phala team directly at @PhalaNetwork on X or visit https://phala.com/ to explore enterprise access and deployment options.
For individual developers and teams, getting started is straightforward through either platform. For institutions, this is a more significant conversation. If your organization is evaluating AI infrastructure for workloads that involve regulated data, client information, or anything where data exposure is a compliance or legal risk, Phala confidential inference stack is one of the few production ready options that addresses that problem at the infrastructure level rather than asking you to manage it yourself. The combination of open source model strength, verifiable execution, and a familiar API surface makes this a practical starting point, not just a proof of concept.
