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GLM-OCR vs GLM-4.7-Flash
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-4.7-Flash | |
|---|---|---|
| Provider | Z.ai (Zhipu AI) | Z.ai (Zhipu AI) |
| Parameters | 1.33B | 31.22B |
| Context window | 131k | 203k |
| License | mit | mit |
| Modalities | vision | text |
| Release date | 30 Jan 2026 | 19 Jan 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 | Reasoning, Coding, Low cost, Summarisation |
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-4.7-Flash
Strengths
- MIT licence with a permissive commercial position
- high published maths score for a 30B-class model
- roughly 200K-token context window
Limits
- benchmark table is self-reported by the vendor
- English and Chinese are the only declared languages
- no evaluation has been run by LLM EU
Tasks
Tasks: Low cost, Summarisation