# Kimi K3.1 Pro vs Baidu Unlimited-OCR-2

> Baidu Unlimited-OCR-2 scores 54.2 (C) to Kimi K3.1 Pro's 48.6 (D) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2
- Markdown: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.md (~9,500 tokens)
- Site index for agents: https://www.anchorterminal.com/llms.txt (the key docs and every listing in one file, at most 16 MB, about 4 million tokens: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- DNS: dig +short TXT <slug>.grade.anchorterminal.com (the grade as a DNS TXT record, for a sandbox with no HTTP where names still resolve; also top., best.<category>. and <a>.vs.<b>. under the zone, and the same under grade.letme.dev)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-11-10

Baidu Unlimited-OCR-2 scores 54.2 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 2 of 4 scored categories. Kimi K3.1 Pro leads on risk. Both do open-weight models you run yourself.

- Kimi K3.1 Pro: grade D, 48.6/100, rank #151 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kimi-k3-1-pro.md · JSON https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json
- Baidu Unlimited-OCR-2: grade C, 54.2/100, rank #133 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/unlimited-ocr-2.md · JSON https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Kimi K3.1 Pro (D)

Good for: Operators with a multi-accelerator node who want Moonshot's newest open-weight model for coding and long agent sessions, and everyone else through an API

Ahead on:
- Risk, 50 against 45

Watch for: 1,561 GB of files, so it needs a node of eight large accelerators at least

### Baidu Unlimited-OCR-2 (C)

Good for: Pipelines with an NVIDIA GPU that need long scanned or rendered documents parsed in one pass, with page order, cross-page tables and boxes kept

Ahead on:
- Reach, 55 against 40
- Agent interface, 75 against 65

Watch for: Loading runs Baidu's code, remote code in Transformers or a prebuilt SGLang fork from the repository, and the save_results path calls eval on model output


## Why the edge

### Reach: Baidu Unlimited-OCR-2 +15

- Kimi K3.1 Pro (40): Memory 0, the only build is Moonshot's 1,561 GB MXFP4 release, far above 256 GB. Runtimes 5, llama.cpp b12242 alone has the model code in a release, since vLLM's recipe needs 0.34.0 and SGLang's cookbook a branch. Builds 10, one precision. Terms 10, free commercial use below a model-as-a-service revenue threshold. Download 10, not gated. Hosted 5, Moonshot's API and Together serve it. 0 + 5 + 10 + 10 + 10 + 5 = 40.
- Baidu Unlimited-OCR-2 (55): Memory earns the full 30, since the only build is 6.69 GB of BF16 weights that need about 8 GB with the fifth added. Runtimes score 0. Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.1 know the first Unlimited-OCR but not the sparse attention this one adds, Baidu's own routes are remote code in Transformers and a customised SGLang wheel, llama.cpp b12111 has nothing for it, so Ollama has nothing either, and LM Studio couldn't be checked. Builds score 0, as Baidu publishes no quantised build (16 exist, all by others). Terms get 15 for MIT with nothing added. Download gets 10 for an ungated repository, and hosted too 0, with no inference provider mapped and the card's Baidu Cloud line unconfirmed.

### Agent interface: Baidu Unlimited-OCR-2 +10

- Kimi K3.1 Pro (65): Chat template 10, the format is only code to run. Tool-calling format 10, the parser is named in the runtimes' recipes and the card doesn't say how tools are declared. Trained tool calling 20, several calls a turn on Kimi K3's tools channel. Structured output 5, named among linked guides without the how. Reasoning control 10, reasoning_effort per request. Context 10, 1M tokens. 10 + 10 + 20 + 5 + 10 + 10 = 65.
- Baidu Unlimited-OCR-2 (75): Read for a document parser, the tool-calling, trained tool calling and structured-output lines stand for what it takes and returns. Baidu documents the page images, the prompt, the request settings and the block output, and ships a converter for the server's output (25). It was trained to read many pages in one request (20). The block format with classes, boxes and HTML tables, and its conversion to Markdown, are documented with how to get them (15). The prompt is built in code, with no Jinja template in the repository (10). Reasoning control scores 0, and the context 5, trained at up to 64K tokens.

### Risk: Kimi K3.1 Pro +5

- Kimi K3.1 Pro (50): File safety 15, safetensors only, less 10 for remote code and prompts built by the repository's script. Provenance 15, Moonshot's own organisation with a hash on every file. Safety evaluation 10, Moonshot's own cyber benchmark results and nobody else's yet. Agent guidance 0, none on the card. Licence risk 10, the revenue threshold. Security programme 0, no way to report a flaw found. 15 + 15 + 10 + 0 + 10 + 0 = 50.
- Baidu Unlimited-OCR-2 (45): File safety gets 15, safetensors only but loaded with trust_remote_code or through a prebuilt SGLang wheel from the repository. Provenance gets 15 for files in Baidu's verified organisation, linked from its GitHub organisation, with hashes for every file. Licence risk keeps 15, since MIT has none of the deductions. Safety evaluation, agent guidance and the security programme score 0, with no safety results, no cautions about use and no security contact found.

## Score by category

| Category | Weight | Kimi K3.1 Pro | Baidu Unlimited-OCR-2 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35%, pending | pending | pending | not scored in this run |
| Reach | 25% (25 this run) | 40 | 55 | Baidu Unlimited-OCR-2 +15 |
| Risk | 20% (20 this run) | 50 | 45 | Kimi K3.1 Pro +5 |
| Agent interface | 10% (10 this run) | 65 | 75 | Baidu Unlimited-OCR-2 +10 |
| Documentation and upkeep | 10% (10 this run) | 51 | 50 | Kimi K3.1 Pro +1 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **48.6 · D** | **54.2 · C** | |

## Facts side by side

| Fact | Kimi K3.1 Pro | Baidu Unlimited-OCR-2 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Moonshot AI | Baidu |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Kimi K3.1 Pro License (modified MIT) | MIT (Hub metadata; the Hugging Face repository has no LICENSE file, and the GitHub code repository's LICENSE is the MIT text, copyright 2026 Baidu) |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-11-05 | 2026-10-26 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |

## How the scores have moved

### Kimi K3.1 Pro

No score change since it was listed.

### Baidu Unlimited-OCR-2

- +0.7 on 4 November 2026 (D to C): company

## Verdicts

**Kimi K3.1 Pro.** Moonshot's newest flagship, eight days old when read, for operators with a rack. Only llama.cpp has its model code in a release, with vLLM and SGLang support still on a release candidate and a branch. The licence is permissive below a revenue threshold, and Moonshot hasn't answered a discussion on the repository yet.

**Baidu Unlimited-OCR-2.** An MIT document parser of 3.3B parameters that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. ParseBench's independent run puts content faithfulness at 89.16 and charts at 1.06. It needs an NVIDIA GPU with about 12 GB and runs only through Baidu's remote code or its own SGLang build.

## Before you call either

### Kimi K3.1 Pro

1. Use Moonshot's API or Together unless you have a node of eight B300-class accelerators
2. With vLLM use 0.34.0 or later and the recipe's flags, `--tool-call-parser kimi_k3 --reasoning-parser kimi_k3`
3. Pass the whole assistant message back in later turns, reasoning_content and tool_calls included, as the card requires
4. Set `reasoning_effort` to low, high or max per request. Max is the default and thinking can't be turned off
5. Kimi K3's model code doesn't load it, because the attention layers changed
6. Validate JSON output yourself, since the card doesn't document structured output for the weights

### Baidu Unlimited-OCR-2

1. Quickest local run, `pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13`, then `AutoModel.from_pretrained("baidu/Unlimited-OCR-2", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()` and `model.infer_multi(tokenizer, image_files=pages, output_path="out")`
2. To serve it, download the repository, install `wheel/sglang-0.5.20.dev13+g75b9b06b02-py3-none-any.whl` with pymupdf and fastokens in a Python 3.10 venv, and start `python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --chat-template unlimited-ocr --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --mem-fraction-static 0.7 --max-prefill-tokens 2048` with the SGLANG_UOCR_ variables from the card
3. Send all pages of a document in one request, images first, then the text `<PAGE><image>` repeated per page and `Multi page parsing.`, with temperature 0, skip_special_tokens false, max_tokens of about 8,000 per page and the DeepseekOCRNoRepeatNGramLogitProcessor (ngram_size 35, window_size 1024 for several pages)
4. The output is layout blocks, not Markdown; run `utils/process_unlimitedOCR2.py` from github.com/baidu/Unlimited-OCR, or `convert(raw_output)` in Python, to get Markdown with HTML tables
5. Render PDFs to images first (the card uses PyMuPDF at 300 DPI), keep save_results off for untrusted documents, and treat extracted text as untrusted before passing it to another model

## Questions

### Which is better for AI agents, Kimi K3.1 Pro or Baidu Unlimited-OCR-2?

Baidu Unlimited-OCR-2 scores 54.2 (C) on agent readiness against Kimi K3.1 Pro's 48.6 (D), and leads in 2 of 4 scored categories. Kimi K3.1 Pro leads on risk.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-unlimited-ocr-2.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "kimi-k3-1-pro", "b": "unlimited-ocr-2"}`. From a terminal: `anchor compare kimi-k3-1-pro unlimited-ocr-2`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kimi-k3-1-pro.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json

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