Flash, uncensored and abliterated are separate choices
The GLM entries in this catalog include z-ai/glm-5.3-flash-uncensored, z-ai/glm-5.3-uncensored and z-ai/glm-5.3-flash-apex-abliterated. Copy the full ID: a Flash variant and an Apex abliterated variant are separate models, even when both belong to the GLM family.
Use the comparison below to shortlist by input price, output price and published context. A lower token rate is useful for cost control; it does not establish a quality or latency advantage. Kearnix Cloud marks the uncensored service offering, while Z.ai is the listed model developer.
Compare writing and instruction following
For a writing application, try the same short brief, a revision request and a conversation with several turns on each candidate. Score whether the answer follows your format, retains the requested voice and stays consistent with supplied facts. Keep the output budget and prompt identical across variants.
For a document workflow, start with a short representative excerpt before trying a large context. The published context window is a catalog limit, not a guarantee that every token position is used equally well. Check answer accuracy against the source, not just whether the request succeeds.
Start with chat completions
These GLM variants currently list /v1/chat/completions in the Kearnix catalog. Use the Python or Node.js guide below with your chosen GLM ID. The catalog does not currently publish tool-calling or structured-output capabilities for these entries, so validate those separately before choosing a coding agent or a schema-dependent workflow.
Uncensored and abliterated labels describe variants to evaluate. They do not promise that every request is answered or that every fine-tune behaves identically. Keep your application's own response checks and review the applicable terms before expanding traffic.