All tasks / Comparisons
GLM-OCR vs OvisOCR2
Compared only on public benchmark results, across the 1 task where both are listed: they share a tier in 1.
GLM-OCR
Z.ai's open-weights ~1B multimodal OCR model (MIT) for parsing document text, tables and formulas.
- Vendor
- Z.ai
- Weights
- Open: run it yourself
- Released
- 30 Jan 2026
- GPU memory to run locally
- ~5 GB
- License
- MIT
- Last verified
- 18 Oct 2026
OvisOCR2
Open-weights 0.8B end-to-end page parser post-trained from Qwen3.5-0.8B that outputs Markdown in reading order.
- Vendor
- ATH-MaaS
- Weights
- Open: run it yourself
- Released
- 13 Jul 2026
- GPU memory to run locally
- ~4 GB
- License
- Apache-2.0
- Last verified
- 18 Oct 2026
Task by task
| Task | GLM-OCR | OvisOCR2 | Higher tier |
|---|---|---|---|
| Documents & OCR Models | S95 on OmniDocBench v1.6 (end-to-end, v1.6_full) · Local | S96 on OmniDocBench v1.6 (end-to-end, v1.6_full) · Local | Same tier |
A tier spans a range of scores, and small gaps on a leaderboard are often within its margin of error, so only a different tier counts as ahead. How we rank.
Same benchmark, both results
| Benchmark | GLM-OCR | OvisOCR2 | Snapshot |
|---|---|---|---|
| OmniDocBench v1.6 (end-to-end, v1.6_full) Overall page-parsing score: ((1 − text edit distance) × 100 + table TEDS + formula CDM) / 3, higher is better | 95 | 96 | 11 Sep 2026 |
| ParseBench (models) Overall: mean of five rule-based dimension scores (tables, charts, content faithfulness, semantic formatting, visual grounding) on ~2,000 human-verified enterprise pages, higher is better | 29.6% | 40.8% | 9 Oct 2026 |