z-ai
Z.ai: GLM 5.3 Prime
z-ai/glm-5.3-prime
Our take
AI-assisted analysis based on the sources below. Suitability is an editorial assessment; test it on your own workload.
OpenRouter’s catalog lists z-ai/glm-5.3-prime as Z.ai’s text-to-text GLM 5.3 Prime variant, canonicalized as z-ai/glm-5.3-prime-20260921. The catalog describes it as a higher-throughput version of GLM-5.3 that preserves the related model’s capabilities, while exposing a 1M-token context and always-on reasoning with adjustable effort. It also lists tool, response-format, sampling, and reasoning parameters. These are source-reported capabilities rather than measured results; the supplied benchmark fields are empty.[1]
Strengths
Limitations
- Only OpenRouter catalog evidence was supplied; no independent model card was available, and benchmark fields were unpopulated.[1]
- Only text inputs and outputs are listed; other modalities are not supported in this evidence.[1]
- Reasoning is mandatory and defaults to maximum effort, so workflows needing disabled reasoning may not fit.[1]
Where to use it
- Potentially useful for long-document summarization or analysis requiring a very large context.[1]
- Potentially suitable for throughput-oriented text assistants if the acceleration claim is workload-validated.[1]
- Potentially appropriate for tool-using text workflows needing reasoning-effort and response-format controls.[1]
Sources
- [1] OpenRouter catalog · Z.ai: GLM 5.3 PrimeSources checked: Nov 13, 2026
About this model
Description below is the original text supplied by OpenRouter, shown in the source language.
GLM-5.3-Prime is the high-speed variant of Z.ai's GLM-5.3, inheriting its full capabilities while delivering 1.5–2× the output throughput through inference acceleration. It supports text input and output with a 1M-token...
Specifications
- Model ID
- z-ai/glm-5.3-prime
- Canonical ID
- z-ai/glm-5.3-prime-20260921
- Creator
- z-ai
- Added to OpenRouter
- 23.09.2026
- Context length
- 1,000,000
- Modality
- text->text
- Input modalities
- text
- Output modalities
- text
- Token pricing
- USD per 1M tokens: in $2.8 / out $8.8
- Knowledge cutoff
- —
- Expiration date
- —
Tasks and capabilities
A grade appears only where the source supplies a matching measurement for that task, the model is active and the cohort holds at least 10 comparable models. The capability column lists facts the source declares — supported formats, reasoning and tool parameters — which are not measured quality.
| Task | Grade | Source-declared capability |
|---|---|---|
| General quality | No measurement | reasoning: mandatory · default max · efforts: max, high, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Coding | No measurement | — |
| Agents | No measurement | parameters: tool_choice, tools |
| Writing | No measurement | output: text |
| Reasoning | No measurement | reasoning: mandatory · default max · efforts: max, high, low · parameters: include_reasoning, reasoning, reasoning_effort |
| Document analysis | No measurement | — |
| Vision | No measurement | — |
| Image generation | No measurement | — |
| Audio | No measurement | — |
| Video | No measurement | — |
| Embeddings | No measurement | — |
Evaluations
No evaluations are supplied for this model.
How grades work
Each letter grade compares a model only against other models scored by the same source and metric in the current snapshot. S means the top 10%, A the next 20%, B the next 30%, C the rest. Models without a published measurement are shown as not rated — a supported format alone never implies measured quality.
- S — S — top 10% of the cohort
- A — A — next 20% of the cohort
- B — B — next 30% of the cohort
- C — C — below the B band
Providers
Optional live provider statistics from OpenRouter. Missing values are unknown, not zero.
| Provider | Context length | Price in /1M | Price out /1M | Latency p50 (30m, s) | Throughput p50 (30m, tok/s) | Uptime (30m) |
|---|---|---|---|---|---|---|
| Alibaba | 1,000,000 | $2.8 | $8.8 | — | — | 100% |
Provider data retrieved: 13.11.2026
Related articles
No published articles identify this model yet.