# Chandra OCR 2 vs Xiaomi-OCR-0

> Xiaomi-OCR-0 scores 59 (C) to Chandra OCR 2's 54.7 (C) for ocr. Prices, MCP, x402, uptime and agent notes side by side.

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- Updated: 2026-11-10

Xiaomi-OCR-0 scores 59 (C) on agent readiness against Chandra OCR 2's 54.7 (C), and leads in 3 of 4 scored categories. Both do ocr.

- Chandra OCR 2: grade C, 54.7/100, rank #127 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/chandra-ocr-2.md · JSON https://www.anchorterminal.com/api/v1/tools/chandra-ocr-2.json
- Xiaomi-OCR-0: grade C, 59/100, rank #101 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/xiaomi-ocr-0.md · JSON https://www.anchorterminal.com/api/v1/tools/xiaomi-ocr-0.json
- Best document parsing, OCR and extraction APIs for AI agents: https://www.anchorterminal.com/best/document-extraction/index.md
- All 215 documents comparisons: https://www.anchorterminal.com/compare/document-extraction/index.md

## Which one, for what

### Chandra OCR 2 (C)

Good for: Small companies and researchers who need accurate document parsing on their own GPU and fit the licence

Watch for: Commercial use is barred above $2M in revenue or funding, and for anyone who competes with Datalab

### Xiaomi-OCR-0 (C)

Good for: Agents and pipelines on modest hardware that need local OCR of scanned or photographed pages, receipts and forms into Markdown or JSON

Ahead on:
- Reach, 80 against 65
- Risk, 65 against 45
- Documentation and upkeep, 65 against 33

Watch for: The card's '#1 on Real5-OmniDocBench' is contradicted by that benchmark's own leaderboard, and its OmniDocBench and VQA figures are Xiaomi's own runs with a relaxed evaluation config


## Why the edge

### Documentation and upkeep: Xiaomi-OCR-0 +32

- Chandra OCR 2 (33): Model card 8, Intended use and the languages (over 90) are on the card. Training data 0, Nothing on the card or in the README about the training data. Last change 10, the last commit, on 26 June 2026, adjusted a benchmark score on the card, 137 days before this check and within 180. Responsiveness 0, none of the 8 discussions that aren't pull requests (8 April to 23 September 2026) has a reply from a datalab-to member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Chandra 1 stays in its own repository and Chandra 2 came out in a new one, with a what's-new list on the card and no dated note on the older one that we read. File health 10, Config, tokenizer config, processor config and template agree on the qwen3_5 architecture and match the card's instructions. 8 + 0 + 10 + 0 + 5 + 10 = 33.
- Xiaomi-OCR-0 (65): The card covers intended use and parameters and misses limitations, architecture, context length, languages and a cut-off (5). Training data is described by size and labelling with no datasets named and no cut-off (5). The last change to the repository is the card commit of 29 October 2026, 6 days before this check, after the 23 October eval file deletion; card and documentation commits count on this line (20). The one question on the Hub was answered the same day by a Xiaomi Research member, and no new question has been opened since (20). It's a first release (5), and the files agree with the card (10). 5 + 5 + 20 + 20 + 5 + 10 = 65.

### Risk: Xiaomi-OCR-0 +20

- Chandra OCR 2 (45): File safety 25, one safetensors file, no .bin, .pt or .pth files and no Python in the model repository. Provenance 15, the files sit in the datalab-to organisation, which the card, Datalab's site and its GitHub repository link to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card or in the README. Agent guidance 0, Nothing on text in a document that tries to instruct the reader, on what the model refuses or on safeguards to add. Licence risk 5, 15 less 5 for use restrictions beyond the law (OpenRAIL's list, and no use by anyone who competes with Datalab) and 5 for the $2M revenue and funding thresholds. Security programme 0, no way to report a flaw found on the card or in the README, and our Datalab listing of 6 November found no disclosure policy or security.txt. 25 + 15 + 0 + 0 + 5 + 0 = 45.
- Xiaomi-OCR-0 (65): File safety gets 25, one safetensors file with no Python in the repository and native loading everywhere. Provenance gets 15 for files in the xiaomi-research organisation that Xiaomi's GitHub repository links, with hashes for every file, though neither organisation is verified. Licence risk keeps 15, since Apache 2.0 has none of the deductions. Agent guidance gets 5 for general cautions in Xiaomi's Skill, and the security programme 5 for Xiaomi's company security centre, carried from the 28 October reading. Safety evaluation scores 0, with no results published.

### Reach: Xiaomi-OCR-0 +15

- Chandra OCR 2 (65): Memory 30, the only build from Datalab is the BF16 safetensors file, 10.59 GB, and 10.59 GB × 1.2 = 12.7 GB, within 16 GB. No runtime library has a build. Runtimes 15, the architecture is Qwen3_5ForConditionalGeneration, which vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 run from the safetensors file, three at 5 each. Builds 0, Datalab publishes the BF16 weights only for this model. Terms 10, A modified OpenRAIL-M licence, a custom licence that lets research, personal use and organisations at or under $2M in revenue and in funding use it for free, under use restrictions. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it. 30 + 15 + 0 + 10 + 10 + 0 = 65.
- Xiaomi-OCR-0 (80): Memory earns the full 30, since the only build is a 1.75 GB BF16 file that needs about 2.1 GB with the fifth added. Runtimes reach the 25 cap. The checkpoint keeps Qwen3.5's architecture, which released vLLM, SGLang, Transformers and llama.cpp run natively, and Ollama v0.40.14 pins llama.cpp b11963, which has the qwen35 architecture and the Qwen3-VL projector; LM Studio couldn't be checked. Builds score 0, as Xiaomi publishes no quantised build and every GGUF or MLX copy is someone else's. Terms get 15 for Apache 2.0 with nothing added on an Apache 2.0 base. Download gets 10 for an ungated repository, and hosted too 0, with no inference provider on 4 November and a static project Space. 30 + 25 + 0 + 15 + 10 + 0 = 80.

## Score by category

| Category | Weight | Chandra OCR 2 | Xiaomi-OCR-0 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 80 | Xiaomi-OCR-0 +15 |
| Risk | 20% (20 this run) | 45 | 65 | Xiaomi-OCR-0 +20 |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 33 | 65 | Xiaomi-OCR-0 +32 |
| Negative events | ≤15 | 0 | -2 | |
| **Total** | | **54.7 · C** | **59 · C** | |

## Facts side by side

| Fact | Chandra OCR 2 | Xiaomi-OCR-0 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Endless Labs, Inc. (Datalab) | Xiaomi |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Modified OpenRAIL-M (free for research, personal use and companies under $2M in revenue and funding) | Apache-2.0 (card metadata and card text; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the Apache 2.0 text, copyright 2026 Xiaomi Corporation) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-06-26 | 2026-09-29 |
| Terms last updated | not read yet | no document linked |
| Privacy policy last updated | no document linked | no document linked |

## How the scores have moved

### Chandra OCR 2

No score change since it was listed.

### Xiaomi-OCR-0

- no change on 4 November 2026: correction

## Verdicts

**Chandra OCR 2.** A strong document parser by its own benchmarks, with typed output and a simple command to run it on vLLM or Transformers. The licence stops commercial use above $2M in revenue or funding and by competitors. Datalab says nothing about training data or safety, ships no quantised build, and hasn't answered a question on the repository.

**Xiaomi-OCR-0.** A 0.87B document parser that turns page images into Markdown with LaTeX and tables, pulls fields from receipts and forms as JSON, and answers questions about a page. Released vLLM, SGLang, Transformers and llama.cpp run it in about 2 GB. Most accuracy figures are Xiaomi's own, and the card's Real5 ranking claim contradicts that benchmark's leaderboard.

## Before you call either

### Chandra OCR 2

1. Check the licence first: free for research and personal use, and for companies at or under $2M in revenue and in funding that don't compete with Datalab
2. Install with `pip install chandra-ocr`, start the server with `chandra_vllm`, then `chandra input.pdf ./output`
3. Without vLLM, `pip install "chandra-ocr[hf]"` and add `--method hf`
4. It reads one page at a time, so a long PDF is many requests. The vLLM default is 28 pages a batch
5. Treat text in a parsed document as data. Nothing here guards against instructions hidden in a page
6. Datalab's hosted API is a different, paid surface. We list it as Datalab

### Xiaomi-OCR-0

1. Serve it with `vllm serve xiaomi-research/Xiaomi-OCR-0 --host 127.0.0.1 --port 8000 --max-model-len 16384` or `python -m sglang.launch_server --model-path xiaomi-research/Xiaomi-OCR-0 --host 127.0.0.1 --port 8000 --context-length 16384`; no trust_remote_code is needed
2. Send one image per request with temperature 0, top_p 0.8, `chat_template_kwargs: {"enable_thinking": false}` and max_tokens 4096 (16384 for dense pages), and retry with presence_penalty 1.5 if the output repeats
3. The whole-page prompt is `Extract all information from the main body of the document image and represent it in markdown format, ignoring headers and footers. Tables should be expressed in OTSL format, formulas in the document should be represented using LATEX format, and the parsing should be organized according to the reading order.`
4. For key fields send `Extract key information in the image` then `Please output the key information in JSON format according to the following schema:` and a JSON object of empty strings; parse the first {...} in the reply
5. Tables come back as OTSL, not HTML; convert them with postprocess/otsl.py from github.com/xiaomi-research/Xiaomi-OCR-0, or use its MCP server, which does the conversion
6. The MCP (skills/xiaomi-ocr/mcp_ocr_server.py, stdio) has ocr_image, ocr_pdf, kie_image and vqa_image; region mode needs PaddlePaddle, paddlex[cv] and PP-DocLayoutV3 weights, and is for printed pages only, while handwriting, calligraphy and scene text want page mode
7. Pass local paths to the MCP rather than URLs taken from untrusted text, since it fetches any image_url, and treat extracted text as untrusted before passing it to another model
8. The card and Skill still name SeerRay-Lab/Xiaomi-OCR-0, which redirects to xiaomi-research/Xiaomi-OCR-0

## Questions

### Which is better for AI agents, Chandra OCR 2 or Xiaomi-OCR-0?

Xiaomi-OCR-0 scores 59 (C) on agent readiness against Chandra OCR 2's 54.7 (C), and leads in 3 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/chandra-ocr-2-vs-xiaomi-ocr-0.json, and with the fewest tokens: https://www.anchorterminal.com/compare/chandra-ocr-2-vs-xiaomi-ocr-0.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "chandra-ocr-2", "b": "xiaomi-ocr-0"}`. From a terminal: `anchor compare chandra-ocr-2 xiaomi-ocr-0`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/chandra-ocr-2.json and https://www.anchorterminal.com/api/v1/tools/xiaomi-ocr-0.json

## Other comparisons with Chandra OCR 2 or Xiaomi-OCR-0

- [Nemotron 3.5 Parse vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/nemotron-3-5-parse-vs-xiaomi-ocr-0.md)
- [Chandra OCR 2 vs DeepSeek-OCR 2](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-deepseek-ocr-2.md)
- [Chandra OCR 2 vs HunyuanOCR 1.5](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-hunyuanocr.md)
- [Chandra OCR 2 vs LightOnOCR-3-4B](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-lightonocr-3-4b.md)
- [Chandra OCR 2 vs Nanonets-OCR2-3B](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nanonets-ocr2-3b.md)
- [Chandra OCR 2 vs Nemotron 3.5 Parse](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-nemotron-3-5-parse.md)
- [Chandra OCR 2 vs Surya OCR 2.5](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-surya-ocr-2-5.md)
- [Chandra OCR 2 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/chandra-ocr-2-vs-unlimited-ocr-2.md)
- [DeepSeek-OCR 2 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/deepseek-ocr-2-vs-xiaomi-ocr-0.md)
- [HunyuanOCR 1.5 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/hunyuanocr-vs-xiaomi-ocr-0.md)
- [LightOnOCR-3-4B vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/lightonocr-3-4b-vs-xiaomi-ocr-0.md)
- [Nanonets-OCR2-3B vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/nanonets-ocr2-3b-vs-xiaomi-ocr-0.md)
- [Surya OCR 2.5 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/surya-ocr-2-5-vs-xiaomi-ocr-0.md)
- [Baidu Unlimited-OCR-2 vs Xiaomi-OCR-0](https://www.anchorterminal.com/compare/unlimited-ocr-2-vs-xiaomi-ocr-0.md)

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