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

SmolLM3-3B vs Baidu Unlimited-OCR-2

SmolLM3-3B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in every scored category. 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

SmolLM3-3BHugging Face · Open-weight model C61/100#51 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

SmolLM3-3B C

Best for Agents that want a small, fully transparent tool-calling cousin on a laptop, CPU or edge device

Ahead on

  • Reach, 85 against 55
  • Risk, 65 against 45
  • Agent interface, 80 against 75
  • Documentation and upkeep, 54 against 45

Watch for

65,536 tokens trained; 131,072 needs a YaRN edit to config.json

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

CategoryWeight this runSmolLM3-3BBaidu Unlimited-OCR-2Edge
Capability35%pendingpendingpendingnot scored in this run
Reach25%258555SmolLM3-3B +30
Risk20%206545SmolLM3-3B +20
Agent interface10%108075SmolLM3-3B +5
Documentation and upkeep10%105445SmolLM3-3B +9
Negative events≤1500
TotalC 61/100D 53.5/100

Facts side by side

FactSmolLM3-3BBaidu Unlimited-OCR-2
KindOpen-weight modelOpen-weight model
VendorHugging FaceBaidu
Hosted endpointno (local only)no (local only)
Transports
AuthNoneNone
PricingFreeFree
x402nono
LicenceApache-2.0MIT (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 release2025-09-102026-10-26
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 reviews3.3/5 (4)none

Verdicts

SmolLM3-3B

A tiny, fully open cousin with documented tool calling and switchable thinking, for local subtasks and routing on laptops and CPUs. It needs about 8 GB in BF16, or under 3 GB in Hugging Face's 4-bit ONNX build for ONNX Runtime. The main risks are a 64K native context, no safety results and no repository changes since September 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

SmolLM3-3B

  1. Start it with vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes, or tool calls come back as text
  2. Pass chat_template_kwargs with enable_thinking false for short tasks, since thinking is on by default; a /think or /no_think flag in the system prompt overrides the kwarg
  3. Put earlier tool calls in the assistant message content, since the template ignores a tool_calls field when it renders history
  4. Add the YaRN rope_scaling block (factor 2.0, original 65536) to config.json before sending more than 65,536 tokens
  5. Use temperature 0.6 and top_p 0.95, the card's recommended sampling
  6. Needs Transformers v4.53.0 or later; for llama.cpp use a GGUF from ggml-org or another converter, since Hugging Face publishes none
  7. Validate JSON output yourself or use the runtime's guided decoding

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, SmolLM3-3B or Baidu Unlimited-OCR-2?

SmolLM3-3B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in every scored category.

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