# Technical Documentation: datalab-to/chandra

> ℹ️ **Provenance:** Hybrid Fusion: `datalab-to/chandra` (README + 1 In-Tree Chapters) · [CodeWiki Reference](https://codewiki.google/github.com/datalab-to/chandra) · Recency: Active (< 180 days)

## 1. Project Overview & Quickstart (datalab-to/chandra)

<h1 align="center">Datalab</h1>




<hr/>

# Chandra OCR 2

Chandra OCR 2 is a state of the art OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.

## Try Chandra on Datalab

Our managed platform runs an improved Chandra with higher accuracy than the open weights, zero data retention by default, SOC 2 Type 2, and custom BAAs.

If you have high volume workloads, we offer a batch processing service that has processed 200M+ pages per week — we manage the infrastructure so your workloads finish on time.

Get started with **$5 in free credits** — [sign up](https://www.datalab.to/?utm_source=gh-chandra) — takes under 30 seconds — or try Chandra in our [public playground](https://www.datalab.to/playground?utm_source=gh-chandra).

Commercial self-hosting requires a license — see [Commercial usage](#commercial-usage). For on-prem licensing, [contact us](https://www.datalab.to/contact?utm_source=gh-chandra-onprem).

## News

- 3/2026 - Chandra 2 is here with significant improvements to math, tables, layout, and multilingual OCR
- 10/2025 - Chandra 1 launched

## Features

- Tops external olmocr benchmark and significant improvement in internal multilingual benchmarks
- Convert documents to markdown, html, or json with detailed layout information
- Support for 90+ languages ([benchmark below](#multilingual-benchmark-table))
- Excellent handwriting support
- Reconstructs forms accurately, including checkboxes
- Strong performance with tables, math, and complex layouts
- Extracts images and diagrams, and adds captions and structured data
- Two inference modes: local (HuggingFace) and remote (vLLM server)



## Quickstart

The easiest way to start is with the CLI tools:

```shell
pip install chandra-ocr

# With vLLM (recommended, lightweight install)
chandra_vllm
chandra input.pdf ./output

# With HuggingFace (requires torch)
pip install chandra-ocr[hf]
chandra input.pdf ./output --method hf

# Interactive streamlit app
pip install chandra-ocr[app]
chandra_app
```

## Benchmarks

Multilingual performance was a focus for us with Chandra 2.  There isn't a good public multilingual OCR benchmark, so we made our own.  This tests tables, math, ordering, layout, and text accuracy.



See full scores [below](#multilingual-benchmark-table). We also have a [full 90-language benchmark](FULL_BENCHMARKS.md).

We also benchmarked Chandra 2 with the widely accepted olmocr benchmark:



See full scores [below](#benchmark-table).

## Examples

| Type | Name                     | Link                                                                                                        |
|------|--------------------------|-------------------------------------------------------------------------------------------------------------|
| Math | CS229 Textbook           | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/cs229.png)                    |
| Math | Handwritten Math         | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/handwritten_math.png)         |
| Math | Chinese Math             | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/chinese_math.png)             |
| Tables | Statistical Distribution | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/complex_tables.png)         |
| Tables | Financial Table          | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/financial_table.png)        |
| Forms | Registration Form        | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/handwritten_form.png)        |
| Forms | Lease Form               | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/lease_filled.png)            |
| Handwriting | Cursive Writing          | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/cursive_writing.png)   |
| Handwriting | Handwritten Notes        | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/handwritten_notes.png) |
| Languages | Arabic                   | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/arabic.png)              |
| Languages | Japanese                 | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/japanese.png)            |
| Languages | Hindi                    | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/hindi.png)               |
| Languages | Russian                  | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/russian.png)             |
| Other | Charts                   | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/charts.png)                  |
| Other | Chemistry                | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/chemistry.png)               |

## Installation

### Package

```bash
# Base install (for vLLM backend)
pip install chandra-ocr

# With HuggingFace backend (includes torch, transformers)
pip install chandra-ocr[hf]

# With all extras
pip install chandra-ocr[all]
```

If you're using the HuggingFace method, we also recommend installing [flash attention](https://github.com/Dao-AILab/flash-attention) for better performance.

### From Source

```bash
git clone https://github.com/datalab-to/chandra.git
cd chandra
uv sync
source .venv/bin/activate
```

## Usage

### CLI

Process single files or entire directories:

```bash
# Single file, with vllm server (see below for how to launch vllm)
chandra input.pdf ./output --method vllm

# Process all files in a directory with local model
chandra ./documents ./output --method hf
```

**CLI Options:**
- `--method [hf|vllm]`: Inference method (default: vllm)
- `--page-range TEXT`: Page range for PDFs (e.g., "1-5,7,9-12")
- `--max-output-tokens INTEGER`: Max tokens per page
- `--max-workers INTEGER`: Parallel workers for vLLM
- `--include-images/--no-images`: Extract and save images (default: include)
- `--include-headers-footers/--no-headers-footers`: Include page headers/footers (default: exclude)
- `--batch-size INTEGER`: Pages per batch (default: 28 for vllm, 1 for hf)

**Output Structure:**

Each processed file creates a subdirectory with:
- `<filename>.md` - Markdown output
- `<filename>.html` - HTML output
- `<filename>_metadata.json` - Metadata (page info, token count, etc.)
- Extracted images are saved directly in the output directory

### Streamlit Web App

Launch the interactive demo for single-page processing:

```bash
chandra_app
```

### vLLM Server (Optional)

For production deployments or batch processing, use the vLLM server:

```bash
chandra_vllm
```

This launches a Docker container with optimized inference settings. Configure via environment variables:

- `VLLM_API_BASE`: Server URL (default: `http://localhost:8000/v1`)
- `VLLM_MODEL_NAME`: Model name for the server (default: `chandra`)
- `VLLM_GPUS`: GPU device IDs (default: `0`)

You can also start your own vllm server with the `datalab-to/chandra-ocr-2` model.

### Configuration

Settings can be configured via environment variables or a `local.env` file:

```bash
# Model settings
MODEL_CHECKPOINT=datalab-to/chandra-ocr-2
MAX_OUTPUT_TOKENS=12384

# vLLM settings
VLLM_API_BASE=http://localhost:8000/v1
VLLM_MODEL_NAME=chandra
VLLM_GPUS=0
```

# Commercial usage

This code is Apache 2.0, and our model weights use a modified OpenRAIL-M license (free for research, personal use, and startups under $2M funding/revenue, cannot be used competitively with our API). To remove the OpenRAIL license requirements, or for broader commercial licensing, visit our pricing page [here](https://www.datalab.to/pricing?utm_source=gh-chandra).

# Benchmark table

| **Model**                 |  ArXiv   | Old Scans Math |  Tables  | Old Scans | Headers and Footers | Multi column | Long tiny text |   Base   |    Overall     |     Source     |
|:--------------------------|:--------:|:--------------:|:--------:|:---------:|:-------------------:|:------------:|:--------------:|:--------:|:--------------:|:--------------:|
| Datalab API               | **90.4** |    **90.2**    |   90.7   | **54.6**  |        91.6         |     83.7     |      92.3      | **99.9** | **86.7 ± 0.8** | Own benchmarks |
| Chandra 2                 |   86.9   |      89.1      | **92.1** |   51.1    |        91.4         |     82.1     |    **93.7**    | **99.9** |   85.8 ± 0.8   | Own benchmarks |
| dots.ocr 1.5              |   85.9   |      85.5      |   90.7   |   48.2    |        94.0         |   **85.3**   |      81.6      |   99.7   |      83.9      | dots.ocr repo  |
| Chandra 1                 |   82.2   |      80.3      |   88.0   |   50.4    |        90.8         |     81.2     |      92.3      | **99.9** |   83.1 ± 0.9   | Own benchmarks |
| olmOCR 2                  |   83.0   |      82.3      |   84.9   |   47.7    |      **96.1**       |     83.7     |      81.9      |   99.6   |      82.4      |  olmocr repo   |
| dots.ocr                  |   82.1   |      64.2      |   88.3   |   40.9    |        94.1         |     82.4     |      81.2      |   99.5   |   79.1 ± 1.0   | dots.ocr repo  |
| olmOCR v0.3.0             |   78.6   |      79.9      |   72.9   |   43.9    |        95.1         |     77.3     |      81.2      |   98.9   |   78.5 ± 1.1   |  olmocr repo   |
| Datalab Marker v1.10.0    |   83.8   |      69.7      |   74.8   |   32.3    |        86.6         |     79.4     |      85.7      |   99.6   |   76.5 ± 1.0   | Own benchmarks |
| Deepseek OCR              |   75.2   |      72.3      |   79.7   |   33.3    |      **96.1**       |     66.7     |      80.1      |   99.7   |   75.4 ± 1.0   | Own benchmarks |
| Mistral OCR API           |   77.2   |      67.5      |   60.6   |   29.3    |        93.6         |     71.3     |      77.1      |   99.4   |   72.0 ± 1.1   |  olmocr repo   |
| GPT-4o (Anchored)         |   53.5   |      74.5      |   70.0   |   40.7    |        93.8         |     69.3     |      60.6      |   96.8   |   69.9 ± 1.1   |  olmocr repo   |
| Qwen 3 VL 8B              |   70.2   |      75.1      |   45.6   |   37.5    |        89.1         |     62.1     |      43.0      |   94.3   |   64.6 ± 1.1   | Own benchmarks |
| Gemini Flash 2 (Anchored) |   54.5   |      56.1      |   72.1   |   34.2    |        64.7         |     61.5     |      71.5      |   95.6   |   63.8 ± 1.2   |  olmocr repo   |


# Multilingual benchmark table

The table below covers the 43 most common languages, benchmarked across multiple models. For a comprehensive evaluation across 90 languages (Chandra 2 vs Gemini 2.5 Flash only), see the [full 90-language benchmark](#full-90-language-benchmark-table).

| Language | Datalab API | Chandra 2 | Chandra 1 | Gemini 2.5 Flash | GPT-5 Mini |
|---|:---:|:---:|:---:|:---:|:---:|
| ar | 67.6% | 68.4% | 34.0% | 84.4% | 55.6% |
| bn | 85.1% | 72.8% | 45.6% | 55.3% | 23.3% |
| ca | 88.7% | 85.1% | 84.2% | 88.0% | 78.5% |
| cs | 88.2% | 85.3% | 84.7% | 79.1% | 78.8% |
| da | 90.1% | 91.1% | 88.4% | 86.0% | 87.7% |
| de | 93.8% | 94.8% | 83.0% | 88.3% | 93.8% |
| el | 89.9% | 85.6% | 85.5% | 83.5% | 82.4% |
| es | 91.8% | 89.3% | 88.7% | 86.8% | 97.1% |
| fa | 82.2% | 75.1% | 69.6% | 61.8% | 56.4% |
| fi | 85.7% | 83.4% | 78.4% | 86.0% | 84.7% |
| fr | 93.3% | 93.7% | 89.6% | 86.1% | 91.1% |
| gu | 73.8% | 70.8% | 44.6% | 47.6% | 11.5% |
| he | 76.4% | 70.4% | 38.9% | 50.9% | 22.3% |
| hi | 80.5% | 78.4% | 70.2% | 82.7% | 41.0% |
| hr | 93.4% | 90.1% | 85.9% | 88.2% | 81.3% |
| hu | 88.1% | 82.1% | 82.5% | 84.5% | 84.8% |
| id | 91.3% | 91.6% | 86.7% | 88.3% | 89.7% |
| it | 94.4% | 94.1% | 89.1% | 85.7% | 91.6% |
| ja | 87.3% | 86.9% | 85.4% | 80.0% | 76.1% |
| jv | 87.5% | 73.2% | 85.1% | 80.4% | 69.6% |
| kn | 70.0% | 63.2% | 20.6% | 24.5% | 10.1% |
| ko | 89.1% | 81.5% | 82.3% | 84.8% | 78.4% |
| la | 78.0% | 73.8% | 55.9% | 70.5% | 54.6% |
| ml | 72.4% | 64.3% | 18.1% | 23.8% | 11.9% |
| mr | 80.8% | 75.0% | 57.0% | 69.7% | 20.9% |
| nl | 90.0% | 88.6% | 85.3% | 87.5% | 83.8% |
| no | 89.2% | 90.3% | 85.5% | 87.8% | 87.4% |
| pl | 93.8% | 91.5% | 83.9% | 89.7% | 90.4% |
| pt | 97.0% | 95.2% | 84.3% | 89.4% | 90.8% |
| ro | 86.2% | 84.5% | 82.1% | 76.1% | 77.3% |
| ru | 88.8% | 85.5% | 88.7% | 82.8% | 72.2% |
| sa | 57.5% | 51.1% | 33.6% | 44.6% | 12.5% |
| sr | 95.3% | 90.3% | 82.3% | 89.7% | 83.0% |
| sv | 91.9% | 92.8% | 82.1% | 91.1% | 92.1% |
| ta | 82.9% | 77.7% | 50.8% | 53.9% | 8.1% |
| te | 69.4% | 58.6% | 19.5% | 33.3% | 9.9% |
| th | 71.6% | 62.6% | 47.0% | 66.7% | 53.8% |
| tr | 88.9% | 84.1% | 68.1% | 84.1% | 78.2% |
| uk | 93.1% | 91.0% | 88.5% | 87.9% | 81.9% |
| ur | 54.1% | 43.2% | 28.1% | 57.6% | 16.9% |
| vi | 85.0% | 80.4% | 81.6% | 89.5% | 83.6% |
| zh | 87.8% | 88.7% | 88.3% | 70.0% | 70.4% |
| **Average** | **80.4%** | **77.8%** | **69.4%** | **67.6%** | **60.5%** |

# Full 90-language benchmark table

We also have a more comprehensive evaluation covering 90 languages, comparing Chandra 2 against Gemini 2.5 Flash. The average scores are lower than the 43-language table above because this includes many lower-resource languages. Chandra 2 averages **72.7%** vs Gemini 2.5 Flash at **60.8%**.

See the [full 90-language results](FULL_BENCHMARKS.md).

## Throughput

Benchmarked with vLLM on a single NVIDIA H100 80GB GPU using a diverse mix of documents (math, tables, scans, multi-column layouts) from the olmOCR benchmark set.  This set is significantly slower than real-world usage - we estimate 2 pages/s in real-world usage.

| Configuration | Pages/sec | Avg Latency | P95 Latency | Failure Rate |
|---|:---:|:---:|:---:|:---:|
| vLLM, 96 concurrent sequences | 1.44 | 60s | 156s | 0% |

# Credits

Thank you to the following open source projects:

- [Huggingface Transformers](https://github.com/huggingface/transformers)
- [VLLM](https://github.com/vllm-project/vllm)
- [olmocr](https://github.com/allenai/olmocr)
- [Qwen 3.5](https://github.com/QwenLM/Qwen3)

## 2. In-Tree Documentation Chapters (datalab-to/chandra)

<h1 align="center">Datalab</h1>




<hr/>

# Chandra OCR 2

Chandra OCR 2 is a state of the art OCR model that converts images and PDFs into structured HTML/Markdown/JSON while preserving layout information.

## Try Chandra on Datalab

Our managed platform runs an improved Chandra with higher accuracy than the open weights, zero data retention by default, SOC 2 Type 2, and custom BAAs.

If you have high volume workloads, we offer a batch processing service that has processed 200M+ pages per week — we manage the infrastructure so your workloads finish on time.

Get started with **$5 in free credits** — [sign up](https://www.datalab.to/?utm_source=gh-chandra) — takes under 30 seconds — or try Chandra in our [public playground](https://www.datalab.to/playground?utm_source=gh-chandra).

Commercial self-hosting requires a license — see [Commercial usage](#commercial-usage). For on-prem licensing, [contact us](https://www.datalab.to/contact?utm_source=gh-chandra-onprem).

## News

- 3/2026 - Chandra 2 is here with significant improvements to math, tables, layout, and multilingual OCR
- 10/2025 - Chandra 1 launched

## Features

- Tops external olmocr benchmark and significant improvement in internal multilingual benchmarks
- Convert documents to markdown, html, or json with detailed layout information
- Support for 90+ languages ([benchmark below](#multilingual-benchmark-table))
- Excellent handwriting support
- Reconstructs forms accurately, including checkboxes
- Strong performance with tables, math, and complex layouts
- Extracts images and diagrams, and adds captions and structured data
- Two inference modes: local (HuggingFace) and remote (vLLM server)



## Quickstart

The easiest way to start is with the CLI tools:

```shell
pip install chandra-ocr

# With vLLM (recommended, lightweight install)
chandra_vllm
chandra input.pdf ./output

# With HuggingFace (requires torch)
pip install chandra-ocr[hf]
chandra input.pdf ./output --method hf

# Interactive streamlit app
pip install chandra-ocr[app]
chandra_app
```

## Benchmarks

Multilingual performance was a focus for us with Chandra 2.  There isn't a good public multilingual OCR benchmark, so we made our own.  This tests tables, math, ordering, layout, and text accuracy.



See full scores [below](#multilingual-benchmark-table). We also have a [full 90-language benchmark](FULL_BENCHMARKS.md).

We also benchmarked Chandra 2 with the widely accepted olmocr benchmark:



See full scores [below](#benchmark-table).

## Examples

| Type | Name                     | Link                                                                                                        |
|------|--------------------------|-------------------------------------------------------------------------------------------------------------|
| Math | CS229 Textbook           | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/cs229.png)                    |
| Math | Handwritten Math         | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/handwritten_math.png)         |
| Math | Chinese Math             | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/math/chinese_math.png)             |
| Tables | Statistical Distribution | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/complex_tables.png)         |
| Tables | Financial Table          | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/tables/financial_table.png)        |
| Forms | Registration Form        | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/handwritten_form.png)        |
| Forms | Lease Form               | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/forms/lease_filled.png)            |
| Handwriting | Cursive Writing          | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/cursive_writing.png)   |
| Handwriting | Handwritten Notes        | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/handwriting/handwritten_notes.png) |
| Languages | Arabic                   | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/arabic.png)              |
| Languages | Japanese                 | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/japanese.png)            |
| Languages | Hindi                    | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/hindi.png)               |
| Languages | Russian                  | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/languages/russian.png)             |
| Other | Charts                   | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/charts.png)                  |
| Other | Chemistry                | [View](https://github.com/datalab-to/chandra/blob/master/assets/examples/other/chemistry.png)               |

## Installation

### Package

```bash
# Base install (for vLLM backend)
pip install chandra-ocr

# With HuggingFace backend (includes torch, transformers)
pip install chandra-ocr[hf]

# With all extras
pip install chandra-ocr[all]
```

If you're using the HuggingFace method, we also recommend installing [flash attention](https://github.com/Dao-AILab/flash-attention) for better performance.

### From Source

```bash
git clone https://github.com/datalab-to/chandra.git
cd chandra
uv sync
source .venv/bin/activate
```

## Usage

### CLI

Process single files or entire directories:

```bash
# Single file, with vllm server (see below for how to launch vllm)
chandra input.pdf ./output --method vllm

# Process all files in a directory with local model
chandra ./documents ./output --method hf
```

**CLI Options:**
- `--method [hf|vllm]`: Inference method (default: vllm)
- `--page-range TEXT`: Page range for PDFs (e.g., "1-5,7,9-12")
- `--max-output-tokens INTEGER`: Max tokens per page
- `--max-workers INTEGER`: Parallel workers for vLLM
- `--include-images/--no-images`: Extract and save images (default: include)
- `--include-headers-footers/--no-headers-footers`: Include page headers/footers (default: exclude)
- `--batch-size INTEGER`: Pages per batch (default: 28 for vllm, 1 for hf)

**Output Structure:**

Each processed file creates a subdirectory with:
- `<filename>.md` - Markdown output
- `<filename>.html` - HTML output
- `<filename>_metadata.json` - Metadata (page info, token count, etc.)
- Extracted images are saved directly in the output directory

### Streamlit Web App

Launch the interactive demo for single-page processing:

```bash
chandra_app
```

### vLLM Server (Optional)

For production deployments or batch processing, use the vLLM server:

```bash
chandra_vllm
```

This launches a Docker container with optimized inference settings. Configure via environment variables:

- `VLLM_API_BASE`: Server URL (default: `http://localhost:8000/v1`)
- `VLLM_MODEL_NAME`: Model name for the server (default: `chandra`)
- `VLLM_GPUS`: GPU device IDs (default: `0`)

You can also start your own vllm server with the `datalab-to/chandra-ocr-2` model.

### Configuration

Settings can be configured via environment variables or a `local.env` file:

```bash
# Model settings
MODEL_CHECKPOINT=datalab-to/chandra-ocr-2
MAX_OUTPUT_TOKENS=12384

# vLLM settings
VLLM_API_BASE=http://localhost:8000/v1
VLLM_MODEL_NAME=chandra
VLLM_GPUS=0
```

# Commercial usage

This code is Apache 2.0, and our model weights use a modified OpenRAIL-M license (free for research, personal use, and startups under $2M funding/revenue, cannot be used competitively with our API). To remove the OpenRAIL license requirements, or for broader commercial licensing, visit our pricing page [here](https://www.datalab.to/pricing?utm_source=gh-chandra).

# Benchmark table

| **Model**                 |  ArXiv   | Old Scans Math |  Tables  | Old Scans | Headers and Footers | Multi column | Long tiny text |   Base   |    Overall     |     Source     |
|:--------------------------|:--------:|:--------------:|:--------:|:---------:|:-------------------:|:------------:|:--------------:|:--------:|:--------------:|:--------------:|
| Datalab API               | **90.4** |    **90.2**    |   90.7   | **54.6**  |        91.6         |     83.7     |      92.3      | **99.9** | **86.7 ± 0.8** | Own benchmarks |
| Chandra 2                 |   86.9   |      89.1      | **92.1** |   51.1    |        91.4         |     82.1     |    **93.7**    | **99.9** |   85.8 ± 0.8   | Own benchmarks |
| dots.ocr 1.5              |   85.9   |      85.5      |   90.7   |   48.2    |        94.0         |   **85.3**   |      81.6      |   99.7   |      83.9      | dots.ocr repo  |
| Chandra 1                 |   82.2   |      80.3      |   88.0   |   50.4    |        90.8         |     81.2     |      92.3      | **99.9** |   83.1 ± 0.9   | Own benchmarks |
| olmOCR 2                  |   83.0   |      82.3      |   84.9   |   47.7    |      **96.1**       |     83.7     |      81.9      |   99.6   |      82.4      |  olmocr repo   |
| dots.ocr                  |   82.1   |      64.2      |   88.3   |   40.9    |        94.1         |     82.4     |      81.2      |   99.5   |   79.1 ± 1.0   | dots.ocr repo  |
| olmOCR v0.3.0             |   78.6   |      79.9      |   72.9   |   43.9    |        95.1         |     77.3     |      81.2      |   98.9   |   78.5 ± 1.1   |  olmocr repo   |
| Datalab Marker v1.10.0    |   83.8   |      69.7      |   74.8   |   32.3    |        86.6         |     79.4     |      85.7      |   99.6   |   76.5 ± 1.0   | Own benchmarks |
| Deepseek OCR              |   75.2   |      72.3      |   79.7   |   33.3    |      **96.1**       |     66.7     |      80.1      |   99.7   |   75.4 ± 1.0   | Own benchmarks |
| Mistral OCR API           |   77.2   |      67.5      |   60.6   |   29.3    |        93.6         |     71.3     |      77.1      |   99.4   |   72.0 ± 1.1   |  olmocr repo   |
| GPT-4o (Anchored)         |   53.5   |      74.5      |   70.0   |   40.7    |        93.8         |     69.3     |      60.6      |   96.8   |   69.9 ± 1.1   |  olmocr repo   |
| Qwen 3 VL 8B              |   70.2   |      75.1      |   45.6   |   37.5    |        89.1         |     62.1     |      43.0      |   94.3   |   64.6 ± 1.1   | Own benchmarks |
| Gemini Flash 2 (Anchored) |   54.5   |      56.1      |   72.1   |   34.2    |        64.7         |     61.5     |      71.5      |   95.6   |   63.8 ± 1.2   |  olmocr repo   |


# Multilingual benchmark table

The table below covers the 43 most common languages, benchmarked across multiple models. For a comprehensive evaluation across 90 languages (Chandra 2 vs Gemini 2.5 Flash only), see the [full 90-language benchmark](#full-90-language-benchmark-table).

| Language | Datalab API | Chandra 2 | Chandra 1 | Gemini 2.5 Flash | GPT-5 Mini |
|---|:---:|:---:|:---:|:---:|:---:|
| ar | 67.6% | 68.4% | 34.0% | 84.4% | 55.6% |
| bn | 85.1% | 72.8% | 45.6% | 55.3% | 23.3% |
| ca | 88.7% | 85.1% | 84.2% | 88.0% | 78.5% |
| cs | 88.2% | 85.3% | 84.7% | 79.1% | 78.8% |
| da | 90.1% | 91.1% | 88.4% | 86.0% | 87.7% |
| de | 93.8% | 94.8% | 83.0% | 88.3% | 93.8% |
| el | 89.9% | 85.6% | 85.5% | 83.5% | 82.4% |
| es | 91.8% | 89.3% | 88.7% | 86.8% | 97.1% |
| fa | 82.2% | 75.1% | 69.6% | 61.8% | 56.4% |
| fi | 85.7% | 83.4% | 78.4% | 86.0% | 84.7% |
| fr | 93.3% | 93.7% | 89.6% | 86.1% | 91.1% |
| gu | 73.8% | 70.8% | 44.6% | 47.6% | 11.5% |
| he | 76.4% | 70.4% | 38.9% | 50.9% | 22.3% |
| hi | 80.5% | 78.4% | 70.2% | 82.7% | 41.0% |
| hr | 93.4% | 90.1% | 85.9% | 88.2% | 81.3% |
| hu | 88.1% | 82.1% | 82.5% | 84.5% | 84.8% |
| id | 91.3% | 91.6% | 86.7% | 88.3% | 89.7% |
| it | 94.4% | 94.1% | 89.1% | 85.7% | 91.6% |
| ja | 87.3% | 86.9% | 85.4% | 80.0% | 76.1% |
| jv | 87.5% | 73.2% | 85.1% | 80.4% | 69.6% |
| kn | 70.0% | 63.2% | 20.6% | 24.5% | 10.1% |
| ko | 89.1% | 81.5% | 82.3% | 84.8% | 78.4% |
| la | 78.0% | 73.8% | 55.9% | 70.5% | 54.6% |
| ml | 72.4% | 64.3% | 18.1% | 23.8% | 11.9% |
| mr | 80.8% | 75.0% | 57.0% | 69.7% | 20.9% |
| nl | 90.0% | 88.6% | 85.3% | 87.5% | 83.8% |
| no | 89.2% | 90.3% | 85.5% | 87.8% | 87.4% |
| pl | 93.8% | 91.5% | 83.9% | 89.7% | 90.4% |
| pt | 97.0% | 95.2% | 84.3% | 89.4% | 90.8% |
| ro | 86.2% | 84.5% | 82.1% | 76.1% | 77.3% |
| ru | 88.8% | 85.5% | 88.7% | 82.8% | 72.2% |
| sa | 57.5% | 51.1% | 33.6% | 44.6% | 12.5% |
| sr | 95.3% | 90.3% | 82.3% | 89.7% | 83.0% |
| sv | 91.9% | 92.8% | 82.1% | 91.1% | 92.1% |
| ta | 82.9% | 77.7% | 50.8% | 53.9% | 8.1% |
| te | 69.4% | 58.6% | 19.5% | 33.3% | 9.9% |
| th | 71.6% | 62.6% | 47.0% | 66.7% | 53.8% |
| tr | 88.9% | 84.1% | 68.1% | 84.1% | 78.2% |
| uk | 93.1% | 91.0% | 88.5% | 87.9% | 81.9% |
| ur | 54.1% | 43.2% | 28.1% | 57.6% | 16.9% |
| vi | 85.0% | 80.4% | 81.6% | 89.5% | 83.6% |
| zh | 87.8% | 88.7% | 88.3% | 70.0% | 70.4% |
| **Average** | **80.4%** | **77.8%** | **69.4%** | **67.6%** | **60.5%** |

# Full 90-language benchmark table

We also have a more comprehensive evaluation covering 90 languages, comparing Chandra 2 against Gemini 2.5 Flash. The average scores are lower than the 43-language table above because this includes many lower-resource languages. Chandra 2 averages **72.7%** vs Gemini 2.5 Flash at **60.8%**.

See the [full 90-language results](FULL_BENCHMARKS.md).

## Throughput

Benchmarked with vLLM on a single NVIDIA H100 80GB GPU using a diverse mix of documents (math, tables, scans, multi-column layouts) from the olmOCR benchmark set.  This set is significantly slower than real-world usage - we estimate 2 pages/s in real-world usage.

| Configuration | Pages/sec | Avg Latency | P95 Latency | Failure Rate |
|---|:---:|:---:|:---:|:---:|
| vLLM, 96 concurrent sequences | 1.44 | 60s | 156s | 0% |

# Credits

Thank you to the following open source projects:

- [Huggingface Transformers](https://github.com/huggingface/transformers)
- [VLLM](https://github.com/vllm-project/vllm)
- [olmocr](https://github.com/allenai/olmocr)
- [Qwen 3.5](https://github.com/QwenLM/Qwen3)

--- METRICS ---
- Files Extracted: 2
- Estimated Token Budget: ~9802 tokens
- Recency Window: Active (< 180 days)
- Canonical Reference: https://codewiki.google/github.com/datalab-to/chandra