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

gpt-oss-20b vs Baidu Unlimited-OCR-2

gpt-oss-20b scores 69.9 (B) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories. 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

gpt-oss-20bOpenAI · Open-weight model B69.9/100#8 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

gpt-oss-20b B

Best for Local agents on a 24 GB GPU or Mac that want a permissive licence, wide runtime support and documented structured output, one tool call at a time

Ahead on

Reach, 90 against 55
Risk, 85 against 45
Agent interface, 90 against 75

Watch for

No reply from OpenAI to any discussion since August 2025, and template reports wait unanswered

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

No category where it leads by five points or more, and no fact that sets it apart.

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

Category	Weight this run	gpt-oss-20b	Baidu Unlimited-OCR-2	Edge
Capability	35%pending	pending	pending	not scored in this run
Reach	25%25	90	55	gpt-oss-20b +35
Risk	20%20	85	45	gpt-oss-20b +40
Agent interface	10%10	90	75	gpt-oss-20b +15
Documentation and upkeep	10%10	42	45	Baidu Unlimited-OCR-2 +3
Negative events	≤15	0	0
Total		B 69.9/100	D 53.5/100

Facts side by side

Fact	gpt-oss-20b	Baidu Unlimited-OCR-2
Kind	Open-weight model	Open-weight model
Vendor	OpenAI	Baidu
Hosted endpoint	no (local only)	no (local only)
Transports
Auth	None	None
Pricing	Free	Free
x402	no	no
Licence	Apache-2.0	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	2025-08-26	2026-10-26
Terms last updated	no document linked	no document linked
Privacy policy last updated	no document linked	no 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 reviews	3.5/5 (4)	none

Verdicts

gpt-oss-20b

gpt-oss-20b is a cousin for reasoning, coding and single tool calls with structured output, under plain Apache 2.0. It needs about 17 GB with a working context, and five inference providers serve it. The main risks are that it trails o4-mini at keeping system instructions above user ones, and OpenAI hasn't answered a report since August 2025.

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

gpt-oss-20b

Start it with ollama run gpt-oss:20b (the build that fits the reference machine), or vllm serve openai/gpt-oss-20b with a current vLLM release rather than the card's pinned vllm==0.10.1+gptoss wheel
Use the harmony format through the chat template or openai-harmony; OpenAI says the model doesn't work correctly without it
Parse tool calls and reasoning with openai-harmony; calls come on the commentary channel ending in <|call|>, reasoning on the analysis channel
Set effort with Reasoning: high (or low, medium) in the system message, or reasoning_effort in the template; medium is the default
Ask for JSON by putting a JSON Schema under # Response Formats in the developer message
Drop earlier reasoning after a final answer but pass it back after tool calls, and don't show the analysis channel to users
Expect one tool call per turn; run several in sequence rather than in parallel

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, 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")
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
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)
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
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, gpt-oss-20b or Baidu Unlimited-OCR-2?

gpt-oss-20b scores 69.9 (B) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 3 of 4 scored categories.

Other comparisons with gpt-oss-20b or Baidu Unlimited-OCR-2

Machine-readable

This page as Markdown /compare/gpt-oss-20b-vs-unlimited-ocr-2.md · slim .min.md · JSON .json (or send Accept: text/markdown)
Each listing in full /api/v1/tools/gpt-oss-20b.json · /api/v1/tools/unlimited-ocr-2.json
From a terminal anchor compare gpt-oss-20b unlimited-ocr-2 (the CLI)
Over MCP compare_tools {"a": "gpt-oss-20b", "b": "unlimited-ocr-2"} at /mcp, no key

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