The GLM 5.2 page on OpenRouter currently lists twenty-seven endpoints from twenty-five providers. Wafer and Fireworks each appear twice, which is the whole gap between the two counts. Most input prices cluster around $1.20 to $1.40 per million tokens. The range opens up only at the ends: DeepInfra sells input at $0.93, and a Wafer endpoint reaches $3.00.
That the providers can sell access to the same base model at all comes down to how Z.ai released it. GLM 5.2 went out under an MIT license: the Hugging Face model card carries the license tag, and CNBC described the release as free to download, fine-tune, and run on a company’s own servers. The license generally permits third parties to host and commercialize inference, subject to applicable law and other rights. OpenRouter gathers those offers on one page, with Z.ai appearing as one seller among the others.
When the lab that trains a model is also the only place that serves it, choosing the model and choosing who runs it are one choice. The lab is the host, its price and terms bundled with the model. An open release pulls those apart. GLM 5.2 still has to be right for the job. After that comes a separate question: which provider should run it, and on what terms? OpenRouter’s page is what that second question looks like once the answer is no longer bundled with the lab.
The timing is still striking. The Hugging Face repository was created in mid-June, and within weeks the provider page was already crowded. CNBC’s report on the release offers some context: the model lands within a percentage point of Anthropic’s Opus 4.8 on a key agentic benchmark, at roughly a fifth of the cost. That comparison may help explain the interest around GLM 5.2. It does not tell us why any particular provider listed it, though, or why one endpoint costs more than another.
That benchmark is also where the account of the price spread runs out. It describes the base model rather than the differently quantized serving artifacts, so it does not explain the gap between the cheapest and most expensive listings. OpenRouter does not show how much of that gap comes from quantization, hardware, utilization, margin, or something else in the serving stack. It shows only a few pieces: DeepInfra’s cheapest endpoint runs at fp4, Z.ai’s own at fp8 and $1.40, and Wafer lists a low fp4 endpoint alongside a far costlier one marked “fast.” The labels describe part of each offer without turning into a price breakdown.
Underneath the price, the other columns vary as well. Some endpoints declare their quantization and some leave it blank. The context window a provider will accept varies widely. Cache-read pricing, which sets what a repeated prompt prefix costs, differs from one endpoint to the next, and Morph and AkashML omit it entirely.
DeepInfra has the lowest listed input price. What the table does not show is how the endpoints behave on an actual workload. The fp4 and fp8 labels, context limits, and cache fields are reasons to compare them, not results from that comparison. The cheapest price is exact, and what it buys is only partly on the page.
