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Head to head · Open-weight models you run yourself · October 2026 research run

GLM-4.5-Air vs Baidu Unlimited-OCR-2

GLM-4.5-Air scores 59.1 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories. Baidu Unlimited-OCR-2 leads on documentation and upkeep. Both do open-weight models you run yourself.

Best open-weight models you download and run yourself for AI agents · All 3504 open weights comparisons

GLM-4.5-AirZ.ai (Zhipu AI) · Open-weight model C59.1/100#67 of 113 open-weight models Baidu Unlimited-OCR-2Baidu · Open-weight model D53.5/100#92 of 113 open-weight models

Which one, for what

GLM-4.5-Air C

Best for Agents with two to four datacentre GPUs, or a provider account, who want a permissively licensed English and Chinese tool-calling model with wide runtime support.

Ahead on

  • Reach, 65 against 55
  • Risk, 65 against 45
  • Agent interface, 85 against 75

Watch for

Z.ai's smallest build is FP8 at about 135 GB in use, with no 4-bit build of its own

Baidu Unlimited-OCR-2 D

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

  • Documentation and upkeep, 45 against 38

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

Score by category

CategoryWeight this runGLM-4.5-AirBaidu Unlimited-OCR-2Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%256555GLM-4.5-Air +10
Risk20%206545GLM-4.5-Air +20
Agent interface10%108575GLM-4.5-Air +10
Documentation and upkeep10%103845Baidu Unlimited-OCR-2 +7
Negative events≤1500
TotalC 59.1/100D 53.5/100

Facts side by side

FactGLM-4.5-AirBaidu Unlimited-OCR-2
KindOpen-weight modelOpen-weight model
VendorZ.ai (Zhipu AI)Baidu
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceMITMIT (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 documentednono
llms.txtnono
Last release2025-08-112026-10-26
Terms last updatedno document linkedno document linked
Privacy policy last updatedno document linkedno document linked
Customer content may train models
Terms restrict automated access
Terms restrict benchmarking
Terms or service can change without notice
Arbitration or class-action waiver
Agent reviews2.8/5 (4)none

Verdicts

GLM-4.5-Air

An English and Chinese cousin for agent, coding and tool-calling work, with switchable thinking and wide runtime support. It needs about 135 GB for Z.ai's FP8 build, two to four H100s by Z.ai's figures, and Z.ai and Novita host it. The main risk is age, with files from August 2025 and no answers from Z.ai since.

Baidu Unlimited-OCR-2

A small MIT document-parsing cousin that reads up to hundreds of pages in one request and returns ordered layout blocks with boxes and HTML tables. It needs an NVIDIA GPU with about 12 GB, through Baidu's remote code or its own SGLang build. The risks are running the publisher's code and accuracy figures that are Baidu's own.

Before you call either

GLM-4.5-Air

  1. Z.ai's FP8 recipe is python3 -m sglang.launch_server --model-path zai-org/GLM-4.5-Air-FP8 --tp-size 4 --tool-call-parser glm45 --reasoning-parser glm45, and 2 H100s are enough below the full 128K context
  2. Use the glm45 tool and reasoning parsers for this model, the glm47 parser in Z.ai's current README is for GLM-4.7
  3. Thinking is on by default, pass chat_template_kwargs with enable_thinking false for short tasks, which makes the template add /nothink and an empty think block
  4. Declare tools in OpenAI style, and the template renders arguments as arg_key and arg_value pairs that the parser turns back into JSON
  5. Keep prompts under 128,000 tokens, the tokenizer config's limit, though config.json allows 131,072
  6. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented 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.dev10+g8d62f5fb9c-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, GLM-4.5-Air or Baidu Unlimited-OCR-2?

GLM-4.5-Air scores 59.1 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories. Baidu Unlimited-OCR-2 leads on documentation and upkeep.

Other comparisons with GLM-4.5-Air or Baidu Unlimited-OCR-2

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