Compare
GLM-OCR vs GLM-5.3
Put two to four models side by side on the fields that decide a deployment: context, price, region, license, languages, hosting, and what is verified.
| GLM-OCR | GLM-5.3 | |
|---|---|---|
| Provider | Z.ai (Zhipu AI) | Z.ai (Zhipu AI) |
| Parameters | 1.33B | 753.33B |
| Context window | 131k | 1.0M |
| License | mit | other |
| Modalities | vision | text |
| Release date | 30 Jan 2026 | 25 Aug 2026 |
| Languages | Strong: zh, en Adequate: de, fr, es, ru, ja, ko | Strong: en, zh Adequate: de, fr, es, it, pt, ru, ja, ko |
| Open weights | yes | yes |
| Tasks | Vision, Retrieval (RAG), Legal drafting, Low cost, Summarisation | Coding, Reasoning, Summarisation, Retrieval (RAG) |
Where two models differ, this table shows the difference and stops. LLM EU publishes no winner badge without a documented score for that task, and a missing figure stays missing instead of becoming a zero.
When to use it
GLM-OCR
Strengths
- purpose-built document OCR rather than general vision
- runs on modest hardware and through common inference servers
- MIT licence
Limits
- text extraction only; it is not a general chat or reasoning model
- vendor parameter count (0.9B) differs from the published safetensors total (1.33B)
- no OCR benchmark has been run by LLM EU
Tasks
Vision, Retrieval (RAG), Legal drafting, Low cost, Summarisation
GLM-5.3
Strengths
- very large context window of 1M tokens
- top published agentic and terminal-benchmark scores
- open weights, unlike most frontier-scale models
Limits
- bespoke Z.ai licence, which must be read before commercial use
- 753B parameters require a multi-node deployment
- all benchmark figures are self-reported by the vendor
Tasks
Tasks: Retrieval (RAG), Summarisation