Head to head · Open-weight models you run yourself · October 2026 research run
Baidu Unlimited-OCR-2 vs ZAYA1-8B
ZAYA1-8B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
Which one, for what
Best 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
- Agent interface, 75 against 60
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
ZAYA1-8B C
Best for People who want a small, fast reasoning model for maths and code and can live without tool calling
Ahead on
- Reach, 80 against 55
- Risk, 55 against 45
Watch for
Tool calling needs a parser that only Zyphra's vLLM fork has
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Reach ZAYA1-8B +25
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.
ZAYA1-8B 80
Memory 30, the smallest build from Zyphra is the MXFP4-Experts repository, 5.85 GB, and 5.85 GB × 1.2 = 7.0 GB, within 16 GB. Runtimes 10, Released Transformers 5.22.2 has the zaya model and SGLang 0.5.23 has srt/models/zaya.py. Builds 15, Quantised builds from Zyphra at two precisions, FP8 and MXFP4 for the experts, each in its own repository. Terms 15, Apache 2.0 in the card's metadata, an OSI licence with nothing added. Download 10, the Hub API reports gated false for the model and its builds 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, and the card names no API. Zyphra's site lists a cloud product for its open models, which wasn't checked for this model. 30 + 10 + 15 + 15 + 10 + 0 = 80.
Agent interface Baidu Unlimited-OCR-2 +15
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.
ZAYA1-8B 60
Chat template 20, chat_template.jinja in the repository (10,700 characters). Tool-calling format 10, only the template and a fork say it. Trained tool calling 10, the template takes tools and renders calls, and the card recommends settings for agent use, but it doesn't say the model was trained for tool calling or for several calls in a turn, so the lower band. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, the template has an enable_thinking switch, and the card names the qwen3 reasoning parser, which returns the reasoning apart from the answer. Context 10, 131,072 positions in config.json, for an architecture Zyphra trained end to end, so the config is the model's own. 20 + 10 + 10 + 0 + 10 + 10 = 60.
Risk ZAYA1-8B +10
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.
ZAYA1-8B 55
File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the Zyphra organisation, which the card ties to zyphra.com's blog post for the model (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and www.zyphra.com has no security.txt. 25 + 15 + 0 + 0 + 15 + 0 = 55.
Score by category
| Category | Weight this run | Baidu Unlimited-OCR-2 | ZAYA1-8B | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 55 | 80 | ZAYA1-8B +25 |
| Risk | 20%20 | 45 | 55 | ZAYA1-8B +10 |
| Agent interface | 10%10 | 75 | 60 | Baidu Unlimited-OCR-2 +15 |
| Documentation and upkeep | 10%10 | 50 | 50 | even |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 54.2/100 | C 61/100 |
Facts side by side
| Fact | Baidu Unlimited-OCR-2 | ZAYA1-8B |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Baidu | Zyphra |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | 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) | Apache-2.0 |
| Read-only variant documented | no | no |
| llms.txt | no | no |
| Last release | 2026-10-26 | 2026-07-02 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
How the scores have moved
ZAYA1-8B
No score change since it was listed.
Verdicts
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.
ZAYA1-8B
A small reasoning model trained from scratch by one lab, with a clean licence, 4-bit and FP8 builds and a team that answers questions. It's the hardest of the small models to run: its own architecture, a card that points at forks, and tool calling that needs a parser no release has. Little on limits, data or safety.
Before you call either
Baidu Unlimited-OCR-2
- 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, thenAutoModel.from_pretrained("baidu/Unlimited-OCR-2", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()andmodel.infer_multi(tokenizer, image_files=pages, output_path="out") - To serve it, download the repository, install
wheel/sglang-0.5.20.dev13+g75b9b06b02-py3-none-any.whlwith pymupdf and fastokens in a Python 3.10 venv, and startpython -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 2048with the SGLANG_UOCR_ variables from the card - Send all pages of a document in one request, images first, then the text
<PAGE><image>repeated per page andMulti 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) - The output is layout blocks, not Markdown; run
utils/process_unlimitedOCR2.pyfrom github.com/baidu/Unlimited-OCR, orconvert(raw_output)in Python, to get Markdown with HTML tables - 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
ZAYA1-8B
- Use it for reasoning, maths and code. Don't count on tool calling outside Zyphra's vLLM fork
- Run it with Transformers 5.22.2 or SGLang 0.5.23, which have the model built in
- Use temperature 1.0 and top_p 0.95 in general, and temperature 0.6 for agent and code work
- Download
Zyphra/ZAYA1-8B, not the legacy repository, for the released runtimes - The quantised repositories have no licence tag. The main one is Apache 2.0
Questions
Which is better for AI agents, Baidu Unlimited-OCR-2 or ZAYA1-8B?
ZAYA1-8B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 54.2 (C), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface.
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Machine-readable
- This page as Markdown
/compare/unlimited-ocr-2-vs-zaya1-8b.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/unlimited-ocr-2.json·/api/v1/tools/zaya1-8b.json - From a terminal
anchor compare unlimited-ocr-2 zaya1-8b(the CLI) - Over MCP
compare_tools {"a": "unlimited-ocr-2", "b": "zaya1-8b"}at/mcp, no key