# Chandra OCR 2 vs Surya OCR 2.5

> Surya OCR 2.5 scores 61 (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

Surya OCR 2.5 scores 61 (C) on agent readiness against Chandra OCR 2's 54.7 (C), and leads in 2 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
- Surya OCR 2.5: grade C, 61/100, rank #82 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/surya-ocr-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/surya-ocr-2-5.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

### Surya OCR 2.5 (C)

Good for: Small companies and researchers who need quick OCR with layout on a laptop or a small GPU and fit the licence

Ahead on:
- Reach, 90 against 65
- Documentation and upkeep, 57 against 33

Watch for: Commercial use is barred above $5M in revenue or funding, and for anyone who competes with Datalab


## Why the edge

### Reach: Surya OCR 2.5 +25

- 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.
- Surya OCR 2.5 (90): Memory 30, Datalab's own Q8_0 GGUF is 0.63 GB with a 0.21 GB projector, and 0.84 GB × 1.2 = 1.0 GB, within 16 GB. The BF16 safetensors file is 1.38 GB. Runtimes 25, Released versions run the architecture from Datalab's own builds: vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 from the safetensors file, llama.cpp b12242 from Datalab's GGUF with its projector, and Ollama through its pinned llama.cpp (b12177 at v0.40.16). Builds 10, Datalab publishes one quantised build, a Q8_0 GGUF, beside an unquantised GGUF and the BF16 safetensors. Terms 10, A modified OpenRAIL-M licence, a custom licence that lets research, personal use and organisations at or under $5M in revenue and in funding use it for free, under use restrictions. Download 10, the Hub API reports gated false for the model and the GGUF repository on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, the card says Datalab's managed platform runs Surya, so the publisher hosts it. 30 + 25 + 10 + 10 + 10 + 5 = 90.

### Documentation and upkeep: Surya OCR 2.5 +24

- 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.
- Surya OCR 2.5 (57): Model card 17, Intended use, limitations, the architecture (a Qwen 3.5-style vision-language model with separate detection models), parameters (689M), the serving length (24,576 tokens) and 91 languages are on the card. Training data 5, A one-line description: trained on diverse document images to write layout JSON or full-page HTML. No sources, sizes or cut-off. Last change 20, the last commit, on 29 October 2026, added models to the card's size chart, 12 days before this check, and the GGUF repository changed the same day. Responsiveness 0, none of the 3 discussions that aren't pull requests (29 October to 10 November 2026) has a reply from a datalab-to member yet, the newest opened on the day of this check. Version policy 5, Surya OCR 2 stays in its own repository and 2.5 came out in a new one. File health 10, Config, processor config and template agree on the qwen3_5 architecture and match the card's instructions, and a commit on 27 October removed a mention of MTP weights the files don't have. 17 + 5 + 20 + 0 + 5 + 10 = 57.

## Score by category

| Category | Weight | Chandra OCR 2 | Surya OCR 2.5 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 65 | 90 | Surya OCR 2.5 +25 |
| Risk | 20% (20 this run) | 45 | 45 | even |
| Agent interface | 10% (10 this run) | 70 | 70 | even |
| Documentation and upkeep | 10% (10 this run) | 33 | 57 | Surya OCR 2.5 +24 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **54.7 · C** | **61 · C** | |

## Facts side by side

| Fact | Chandra OCR 2 | Surya OCR 2.5 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Endless Labs, Inc. (Datalab) | Endless Labs, Inc. (Datalab) |
| 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) | Modified OpenRAIL-M (free for research, personal use and companies under $5M in revenue and funding) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-06-26 | 2026-10-29 |
| Privacy policy last updated | no document linked | no document linked |

## 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.

**Surya OCR 2.5.** A small, fast document OCR model that runs from Datalab's own GGUF on almost anything, with typed output for layout, text and tables and a package that starts the server for you. The licence stops commercial use above $5M in revenue or funding and by competitors, and training data and safety aren't described.

## 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

### Surya OCR 2.5

1. Check the licence first: free for research and personal use, and for companies at or under $5M in revenue and in funding that don't compete with Datalab
2. `pip install surya-ocr`, then `surya_ocr page.png`. It starts vLLM in Docker on an NVIDIA GPU and llama-server elsewhere
3. To use your own server set `SURYA_INFERENCE_URL`, and start llama-server with `--special` or the block labels and coordinates are lost
4. Raw output is compact HTML. Run it through `surya.inference.parsers.parse_full_page_html` if you don't use the package
5. Allow about 24,576 tokens of context for a full page
6. Treat text in a parsed document as data. Nothing here guards against instructions hidden in a page

## Questions

### Which is better for AI agents, Chandra OCR 2 or Surya OCR 2.5?

Surya OCR 2.5 scores 61 (C) on agent readiness against Chandra OCR 2's 54.7 (C), and leads in 2 of 4 scored categories.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/chandra-ocr-2-vs-surya-ocr-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/chandra-ocr-2-vs-surya-ocr-2-5.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "chandra-ocr-2", "b": "surya-ocr-2-5"}`. From a terminal: `anchor compare chandra-ocr-2 surya-ocr-2-5`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/chandra-ocr-2.json and https://www.anchorterminal.com/api/v1/tools/surya-ocr-2-5.json

## Other comparisons with Chandra OCR 2 or Surya OCR 2.5

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

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