{ "data": { "a": { "slug": "spark-x2-6-4b", "name": "Spark-X2.6-4B", "vendor": "SparkLLM (XHToken)", "vendorUrl": "https://huggingface.co/XHToken", "kind": "weights", "category": "open-weights", "summary": "Spark-X2.6-4B is SparkLLM's open-weight 4B text model, Spark-X2.5-4B's architecture with new post-training, a 1M-token context and three thinking levels, on Hugging Face with FP8, INT8, INT4 and GGUF builds under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/spark-x2-6-4b", "markdownUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json", "repo": "https://huggingface.co/XHToken/Spark-X2.6-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-GGUF" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-FP8" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-INT8" }, { "registry": "huggingface", "name": "XHToken/Spark-X2.6-4B-INT4" }, { "registry": "ollama", "name": "SparkLLM/Spark-X2.6-4B" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the six X2.6 repositories is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Nothing is hosted by SparkLLM or by an inference provider listed on Hugging Face. Running it yourself costs the hardware, 8.24 GB for the BF16 files and 3.19 GB for SparkLLM's Q4_K_M GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/XHToken/Spark-X2.6-4B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "long-context", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gguf", "fp8", "self-hosted" ], "lastRelease": "2026-10-20", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1217, "categoryRank": 44, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 85, "risk": 50, "upkeep": 41 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card.", "bestFor": "Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.", "strengths": [ "SparkLLM's Q4_K_M GGUF fits in about 4 GB, and FP8, INT8, INT4 and Q8_0 builds come from the publisher", "Plain Apache 2.0 on its own base, with no gate or account", "1,048,576 tokens of context with sliding-window layers that keep the cache small", "Released support in llama.cpp, SGLang and Ollama, with SGLang's own spark25 tool parser", "Thinking that turns off per request or runs at low or high effort, and several tool calls a turn", "A private security reporting route in SparkLLM's GitHub" ], "weaknesses": [ "No safety results and no agent or prompt-injection guidance", "Transformers and vLLM need code from SparkLLM's repositories", "Tool format and structured output aren't documented, and the template uses its own arg_key format", "The card has no limitations, cut-off, architecture description or training data, and leans on X2.5's", "Released on 19 and 20 October 2026, with no downloads, discussions or independent results yet" ], "agentNotes": [ "Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)", "For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs", "Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low", "SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1", "The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list", "The template raises an error if tool_call arguments are a JSON string, so pass them as an object", "Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 70, "reach": 85, "risk": 50, "upkeep": 41 }, "provenanceScore": 40, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q8_0 --jinja -c 131072\n# or, with SGLang 0.5.22 or later:\npython -m sglang.launch_server --model-path XHToken/Spark-X2.6-4B \\\n --tool-call-parser spark25 --reasoning-parser qwen3 --context-length 131072", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"XHToken/Spark-X2.6-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/spark-x2-6-4b" }, "weights": { "trainedBy": "SparkLLM (XHToken)", "builtOn": "XHToken/Spark-X2.6-4B-Base (SparkLLM's own checkpoint, Apache 2.0, declared as the base in the card's metadata). The card's text says X2.6 builds on Spark-X2.5-4B with improved post-training. The Base repository's card is a cut-down copy of this card that calls its files the post-trained model", "params": "4.11B", "context": 1048576, "goodFor": "Small dense text model for coding, tool use and agent loops on a laptop or small GPU, with a 1M-token context and thinking that is off, low or high per request.", "runtimes": [ "llama.cpp", "Ollama", "SGLang" ], "builds": [ { "name": "Spark-X2.6-4B-FP8", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 4.47, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors float-quantized" }, { "name": "Spark-X2.6-4B-INT8", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-INT8", "who": "publisher", "precision": "int8", "sizeGB": 4.47, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors int-quantized, 8-bit weights and activations" }, { "name": "Spark-X2.6-4B-INT4", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-INT4", "who": "publisher", "precision": "int4", "sizeGB": 3.43, "memoryGB": 8, "format": "safetensors", "note": "compressed-tensors pack-quantized, 4-bit weights in groups of 32 on the attention and MLP projections, the rest in BF16" }, { "name": "Spark-X2.6-4B-Q4_K_M.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 3.19, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ], "note": "The smallest build at 4-bit or better. 0.59 GB larger than X2.5's Q4_K_M" }, { "name": "Spark-X2.6-4B-Q8_0.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 4.38, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "Ollama" ] }, { "name": "Spark-X2.6-4B", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B", "who": "publisher", "precision": "bf16", "sizeGB": 8.24, "memoryGB": 12, "format": "safetensors", "runsOn": [ "SGLang" ], "note": "Transformers loads it only with trust_remote_code (modeling_spark.py), and vLLM only with SparkLLM's out-of-tree plugin" }, { "name": "Spark-X2.6-4B.gguf", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-GGUF/blob/main/Spark-X2.6-4B.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 8.23, "memoryGB": 12, "format": "gguf" }, { "name": "ollama SparkLLM/Spark-X2.6-4B:latest", "url": "https://ollama.com/SparkLLM/Spark-X2.6-4B", "who": "publisher", "precision": "bf16", "sizeGB": 8.2, "memoryGB": 12, "format": "ollama", "runsOn": [ "Ollama" ], "note": "SparkLLM's own namespace on Ollama, not Ollama's library. The only tag is 8.2 GB (digest 44ca4d700630), so it is the unquantised build; needs Ollama 0.34.1 or later. Size as Ollama shows it" } ], "run": "llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q8_0 --jinja -c 131072", "runBuild": "Spark-X2.6-4B-Q8_0.gguf", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "SparkLLM (XHToken)", "family": "Spark-X2 (X2.5, X2.6)", "url": "https://www.anchorterminal.com/open-weights/#sparkllm", "others": [ { "name": "Spark-X2.6-4B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.6-4B-Base", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-4B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.5-4B-Base", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-1.7B-Base", "url": "https://huggingface.co/XHToken/Spark-X2.5-1.7B-Base", "params": "1.7B (Hub safetensors count 1.71B)" }, { "name": "Spark-X2.5-4B", "url": "https://huggingface.co/XHToken/Spark-X2.5-4B", "params": "4B (Hub safetensors count 4.11B)" }, { "name": "Spark-X2.5-1.7B", "url": "https://huggingface.co/XHToken/Spark-X2.5-1.7B", "params": "1.7B (Hub safetensors count 1.71B)" } ] }, "area": "models", "provenance": { "legalEntity": "XHToken (the licence's copyright line); the Hugging Face organisation's name is SparkLLM", "domain": "", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation XHToken (fullname SparkLLM, 18 members) isn't verified. No company website was found on the card or the organisation page; the card links Slack, Discord, YouTube, dev.to, Bluesky, X, Zhihu and WeChat, and SparkLLM's GitHub repositories.", "SparkLLM's GitHub organisation XHToken documents its Hugging Face repositories by name (Spark-MLX-LLM's README), and the X2.6 card points to Spark-plugin, Spark-MLX-LLM and XHToken/LlamaFactory there, which ties the two together.", "XHToken/community on GitHub has a SECURITY.md with a private advisory route and a security email, which Spark-plugin's SECURITY.md points to.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commits from 19 and 20 October 2026 are uploads titled only \"Upload Spark-X2.6-4B model files\"." ], "score": 40 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/spark-x2-6-4b.json" }, "answer": "Spark-X2.6-4B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface.", "b": { "slug": "unlimited-ocr-2", "name": "Baidu Unlimited-OCR-2", "vendor": "Baidu", "vendorUrl": "https://github.com/baidu", "kind": "weights", "category": "open-weights", "summary": "Unlimited-OCR-2 is Baidu's open-weight document OCR model, about 3.3B parameters, that parses page images, from one page to documents of hundreds of pages in one request, into layout blocks with boxes and HTML tables, under MIT.", "url": "https://www.anchorterminal.com/tools/unlimited-ocr-2", "markdownUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json", "repo": "https://huggingface.co/baidu/Unlimited-OCR-2", "license": "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)", "transports": [], "packages": [ { "registry": "huggingface", "name": "baidu/Unlimited-OCR-2" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository isn't gated (checked 2026-10-27).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under MIT by the repository's metadata and the LICENSE file in Baidu's GitHub code repository. Baidu sells no per-call access to these weights in this listing. Running them costs the hardware, 6.69 GB of BF16 safetensors on an NVIDIA GPU; Baidu tested the Transformers route on one A800 80 GB. No inference provider serves it on Hugging Face (checked 2026-10-27).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-27).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-27" }, "docsUrl": "https://huggingface.co/baidu/Unlimited-OCR-2", "capabilities": [ "docs.ocr", "docs.parse", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "multi-page", "long-documents", "tables", "layout", "bounding-boxes", "vision", "mixture-of-experts", "safetensors", "self-hosted", "new" ], "lastRelease": "2026-10-26", "graded": true, "anchor": { "graded": true, "score": 53.5, "grade": "D", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1217, "categoryRank": 74, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 75, "reach": 55, "risk": 45, "upkeep": 45 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-27" }, "negative": 0, "verdict": "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.", "bestFor": "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", "strengths": [ "Small, 3.3B parameters with about 0.5B active per token, in 6.69 GB of BF16 safetensors", "Reads many pages in one request with cross-page context, merging tables and text that continue across pages", "MIT with nothing added, ungated, files hashed in Baidu's verified organisation", "An OpenAI-compatible server through SGLang with page-window scheduling for long documents, and a documented converter from its block output to Markdown" ], "weaknesses": [ "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", "No released runtime runs its sparse attention yet, and no quantised build", "The card states no parameters, languages, context length or limitations, and no safety results or security contact were found", "Every accuracy figure is Baidu's own, one of its two long-document benchmarks is Baidu's own, and no independent result exists for this checkpoint", "Charts and figures come back as boxes to crop, not as data, and the report says heading hierarchy is weak over long documents" ], "agentNotes": [ "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 `\u003cPAGE\u003e\u003cimage\u003e` 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" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 53.5 } ], "editorialScores": { "interface": 75, "reach": 55, "risk": 45, "upkeep": 45 }, "provenanceScore": 73, "checked": { "categories": { "interface": "2026-10-27", "reach": "2026-10-27", "risk": "2026-10-27", "upkeep": "2026-10-27" }, "last": "2026-10-27", "oldest": "2026-10-27", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "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\n# then AutoModel.from_pretrained(\"baidu/Unlimited-OCR-2\", trust_remote_code=True, use_safetensors=True, torch_dtype=torch.bfloat16).eval().cuda()\n# and model.infer_multi(tokenizer, image_files=[\"page_1.jpg\", \"page_2.jpg\"], output_path=\"out\")", "http": "# after: hf download baidu/Unlimited-OCR-2 --local-dir uocr2 \u0026\u0026 uv venv --python 3.10 \u0026\u0026 . .venv/bin/activate\n# uv pip install uocr2/wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl pymupdf==1.27.2.2 fastokens==0.3.2\n# SGLANG_UOCR_EANABLE_DOC_LIMIT=1 python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --served-model-name Unlimited-OCR-2 --chat-template unlimited-ocr --tokenizer-backend fastokens --mem-fraction-static 0.7 --max-prefill-tokens 2048 --enable-custom-logit-processor --enable-streaming-session --disable-radix-cache --port 10000\ncurl http://127.0.0.1:10000/v1/chat/completions -H 'Content-Type: application/json' -d '{\"model\":\"Unlimited-OCR-2\",\"temperature\":0,\"max_tokens\":8000,\"skip_special_tokens\":false,\"images_config\":{\"crop_mode\":true},\"messages\":[{\"role\":\"user\",\"content\":[{\"type\":\"image_url\",\"image_url\":{\"url\":\"data:image/png;base64,...\"}},{\"type\":\"text\",\"text\":\"\u003cPAGE\u003e\u003cimage\u003eMulti page parsing.\"}]}]}'" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/unlimited-ocr-2" }, "weights": { "trainedBy": "Baidu (ERNIE Team)", "builtOn": "baidu/Unlimited-OCR (post-trained from its checkpoint per the report, MIT by its metadata), whose backbone is deepseek-ai/DeepSeek-OCR (MIT by its metadata); not declared in the card's metadata", "params": "3.3B (3B-A0.5B mixture-of-experts decoder per the report)", "context": 131072, "goodFor": "Parsing long scanned or rendered documents, tens to hundreds of pages, into ordered layout blocks with boxes and HTML tables in one request, on an NVIDIA GPU, if Baidu's own results hold up.", "runtimes": [], "builds": [ { "name": "Unlimited-OCR-2 (BF16 safetensors)", "url": "https://huggingface.co/baidu/Unlimited-OCR-2", "who": "publisher", "precision": "bf16", "sizeGB": 6.69, "memoryGB": 12, "format": "safetensors", "runsOn": [ "Transformers (remote code)", "SGLang (Baidu's customised wheel)" ], "note": "Two shards, 6,688,305,576 bytes, so 6.69 GB × 1.2 = 8.03 GB, within 12 GB. The whole repository is 6.87 GB with the report, the wheel and a 156 MB GIF, which comes to the same band. The only build from Baidu; no copy by anyone else exists yet" } ], "run": "hf download baidu/Unlimited-OCR-2 --local-dir uocr2 \u0026\u0026 uv venv --python 3.10 \u0026\u0026 . .venv/bin/activate \u0026\u0026 uv pip install uocr2/wheel/sglang-0.5.20.dev10+g8d62f5fb9c-py3-none-any.whl pymupdf==1.27.2.2 fastokens==0.3.2 \u0026\u0026 SGLANG_UOCR_EANABLE_DOC_LIMIT=1 SGLANG_UOCR_DOC_GUNDAM_PAGE_WINDOW=10 SGLANG_UOCR_DOC_BASE_PAGE_WINDOW=10 SGLANG_UOCR_MAX_PAGE_TOKENS=20000 python -m sglang.launch_server --model-path baidu/Unlimited-OCR-2 --served-model-name Unlimited-OCR-2 --chat-template unlimited-ocr --tokenizer-backend fastokens --mem-fraction-static 0.7 --max-prefill-tokens 2048 --enable-custom-logit-processor --enable-streaming-session --allow-auto-truncate --disable-radix-cache --port 10000", "runBuild": "Unlimited-OCR-2 (BF16 safetensors)", "checked": "2026-10-27", "minMemoryGB": 12 }, "family": { "lab": "Baidu", "family": "Unlimited OCR 2", "url": "https://www.anchorterminal.com/open-weights/#baidu", "olderCount": 1 }, "area": "models", "provenance": { "legalEntity": "Baidu, Inc.", "domain": "baidu.com", "domainRegistered": "1999-10-11", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/baidu/Unlimited-OCR-2/commits/main", "securityTxt": "unknown", "checked": "2026-10-27", "notes": [ "The files sit in Baidu's verified baidu organisation on Hugging Face, which Baidu's GitHub organisation profile links to, and the GitHub code repository's README links this repository. The report's byline is the Baidu ERNIE Team. The legal entity is the card's Baidu Inc., not checked against a registry. The domain registration date is from Verisign's RDAP record for baidu.com, read 2026-10-27.", "Nothing is hosted in this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, twelve commits on 26 October 2026 by one account (WEISHU), the weights at 04:38 UTC and the last README change at 14:05 UTC. The card's release line dates the release 27 October 2026.", "www.baidu.com's robots.txt disallows every path to all agents not named in it, so its security.txt is not read for this listing, and bsrc.baidu.com answered Access denied to its robots.txt. No security contact is named on the card or in the GitHub repository." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/unlimited-ocr-2.json", "live": { "slug": "unlimited-ocr-2", "securityTxt": { "url": "https://baidu.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-27T15:32:20.71581076Z" }, "updatedAt": "2026-10-27T15:32:20.71581076Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "SparkLLM (XHToken)", "b": "Baidu", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "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)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-20", "b": "2026-10-26", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Spark-X2.6-4B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface.", "question": "Which is better for AI agents, Spark-X2.6-4B or Baidu Unlimited-OCR-2?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 55", "Risk, 50 against 45" ], "also": null, "goodFor": "Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.", "slug": "spark-x2-6-4b", "watchFor": "No safety results and no agent or prompt-injection guidance" }, { "aheadOn": [ "Agent interface, 75 against 70" ], "also": null, "goodFor": "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", "slug": "unlimited-ocr-2", "watchFor": "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" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-spark-x2-6-4b.json", "title": "Agents-A1-4B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2.json", "title": "Agents-A1-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-spark-x2-6-4b.json", "title": "Apertus v1.5 8B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2.json", "title": "Apertus v1.5 8B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-spark-x2-6-4b.json", "title": "Apodex-1.1-mini vs 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vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-spark-x2-6-4b.json", "title": "DeepSeek-V3.2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2.json", "title": "DeepSeek-V3.2 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.json", "title": "DeepSeek V4.1 Flash vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.json", "title": "DeepSeek V4.1 Flash vs Baidu Unlimited-OCR-2", "url": 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"url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-9B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b.json", "title": "Qwen3.6-27B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.json", "title": "Qwen3.6-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b.json", "title": "Qwen3.6-35B-A3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2.json", "title": "Qwen3.6-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b.json", "title": "Qwen3.8-27B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "Qwen3.8-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b.json", "title": "Qwen3.8-Flash-Next vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2.json", "title": "Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b.json", "title": "Qwen3-Coder-Next vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2.json", "title": "Qwen3-Coder-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-2B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-2B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-30B-A3B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-32B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-32B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-4B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-4B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b.json", "title": "Qwen3-VL-8B-Instruct vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2.json", "title": "Qwen3-VL-8B-Instruct vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b.json", "title": "Qwopus3.8-27B-Flash-V2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2.json", "title": "Qwopus3.8-27B-Flash-V2 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b.json", "title": "Recursive Flint vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2.json", "title": "Recursive Flint vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/recursive-flint-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b.json", "title": "SmolLM3-3B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2.json", "title": "SmolLM3-3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Spark-X2.6-4B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "Swift 1.5 Qwen3.8-27B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2.json", "title": "ThinkingCap (Qwen3.8 27B) vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/thinkingcap-qwen3-8-27b-vs-unlimited-ocr-2" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 30, "edge": "spark-x2-6-4b", "key": "reach", "name": "Reach", "spark-x2-6-4b": 85, "unlimited-ocr-2": 55, "weight": 25 }, { "by": 5, "edge": "spark-x2-6-4b", "key": "risk", "name": "Risk", "spark-x2-6-4b": 50, "unlimited-ocr-2": 45, "weight": 20 }, { "by": 5, "edge": "unlimited-ocr-2", "key": "interface", "name": "Agent interface", "spark-x2-6-4b": 70, "unlimited-ocr-2": 75, "weight": 10 }, { "by": 4, "edge": "unlimited-ocr-2", "key": "upkeep", "name": "Documentation and upkeep", "spark-x2-6-4b": 41, "unlimited-ocr-2": 45, "weight": 10 } ], "summary": "Spark-X2.6-4B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "spark-x2-6-4b": "A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2", "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.min.md" }, "markdown": "Spark-X2.6-4B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface. Both do open-weight models you run yourself.\n\n- Spark-X2.6-4B: grade C, 61/100, rank #52 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/spark-x2-6-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json\n- Baidu Unlimited-OCR-2: grade D, 53.5/100, rank #92 of 113 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\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 3504 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Spark-X2.6-4B (C)\n\nGood for: Agents that want a tiny local model for tool loops and long documents on a laptop, CPU or small GPU.\n\nAhead on:\n- Reach, 85 against 55\n- Risk, 50 against 45\n\nWatch for: No safety results and no agent or prompt-injection guidance\n\n### Baidu Unlimited-OCR-2 (D)\n\nGood 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\n\nAhead on:\n- Agent interface, 75 against 70\n\nWatch 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\n\n\n## Score by category\n\n| Category | Weight | Spark-X2.6-4B | Baidu Unlimited-OCR-2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 55 | Spark-X2.6-4B +30 |\n| Risk | 20% (20 this run) | 50 | 45 | Spark-X2.6-4B +5 |\n| Agent interface | 10% (10 this run) | 70 | 75 | Baidu Unlimited-OCR-2 +5 |\n| Documentation and upkeep | 10% (10 this run) | 41 | 45 | Baidu Unlimited-OCR-2 +4 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **53.5 · D** | |\n\n## Facts side by side\n\n| Fact | Spark-X2.6-4B | Baidu Unlimited-OCR-2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | SparkLLM (XHToken) | Baidu |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| 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) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-20 | 2026-10-26 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3/5 (4) | none |\n\n## Verdicts\n\n**Spark-X2.6-4B.** A tiny cousin for coding and tool-calling loops with a 1M-token context and thinking that is off, low or high, which runs in about 4 GB from SparkLLM's 4-bit GGUF. Use llama.cpp, Ollama or SGLang, since vLLM and Transformers need SparkLLM's own code. The risks are no safety work or agent guidance and a thin, two-day-old card.\n\n**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.\n\n## Before you call either\n\n### Spark-X2.6-4B\n\n1. Quickest start is `llama-server -hf XHToken/Spark-X2.6-4B-GGUF:Q4_K_M --jinja`, which needs llama.cpp b10828 or later, or `ollama run SparkLLM/Spark-X2.6-4B` (8.2 GB, Ollama 0.34.1 or later)\n2. For parsed tool calls serve it with SGLang 0.5.22 or later, `--tool-call-parser spark25 --reasoning-parser qwen3`, since the calls come back as tool_call blocks with arg_key and arg_value pairs\n3. Thinking is on by default. Pass `chat_template_kwargs` with `enable_thinking` false to turn it off, or `reasoning_effort` low for short thinking; the template raises an error on any effort other than high or low\n4. SparkLLM's recommended sampling is temperature 1.0, top_p 0.95 and top_k -1\n5. The template always opens with its own default system line, with the effort instruction, and puts your system message after the tool list\n6. The template raises an error if tool_call arguments are a JSON string, so pass them as an object\n7. Set the context yourself, the card's 1,048,576-token example needs far more memory than the weights\n\n### Baidu Unlimited-OCR-2\n\n1. 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\")`\n2. 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\n3. Send all pages of a document in one request, images first, then the text `\u003cPAGE\u003e\u003cimage\u003e` 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)\n4. 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\n5. 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\n\n## Questions\n\n### Which is better for AI agents, Spark-X2.6-4B or Baidu Unlimited-OCR-2?\n\nSpark-X2.6-4B scores 61 (C) on agent readiness against Baidu Unlimited-OCR-2's 53.5 (D), and leads in 2 of 4 scored categories. Baidu Unlimited-OCR-2 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"spark-x2-6-4b\", \"b\": \"unlimited-ocr-2\"}`. From a terminal: `anchor compare spark-x2-6-4b unlimited-ocr-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/spark-x2-6-4b.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json\n\n## Other comparisons with Spark-X2.6-4B or Baidu Unlimited-OCR-2\n\n- [Agents-A1-4B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-spark-x2-6-4b.md)\n- [Agents-A1-4B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-unlimited-ocr-2.md)\n- [Apertus v1.5 8B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-spark-x2-6-4b.md)\n- [Apertus v1.5 8B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-unlimited-ocr-2.md)\n- [Apodex-1.1-mini vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-spark-x2-6-4b.md)\n- [Apodex-1.1-mini vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-unlimited-ocr-2.md)\n- [BigBang-v1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/bigbang-v1-vs-spark-x2-6-4b.md)\n- [BigBang-v1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/bigbang-v1-vs-unlimited-ocr-2.md)\n- [Carbon-X vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b.md)\n- [Carbon-X vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/carbon-x-vs-unlimited-ocr-2.md)\n- [Cosmos-Reason2-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-spark-x2-6-4b.md)\n- [Cosmos-Reason2-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-unlimited-ocr-2.md)\n- [Darwin-36B-Opus vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-spark-x2-6-4b.md)\n- [Darwin-36B-Opus vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-unlimited-ocr-2.md)\n- [DeepSeek-V3.2 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-spark-x2-6-4b.md)\n- [DeepSeek-V3.2 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-unlimited-ocr-2.md)\n- [DeepSeek V4.1 Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-spark-x2-6-4b.md)\n- [DeepSeek V4.1 Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.md)\n- [DeepSeek-V4-Flash-0731 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-spark-x2-6-4b.md)\n- [DeepSeek-V4-Flash-0731 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-unlimited-ocr-2.md)\n- [DiffusionGemma 26B A4B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-spark-x2-6-4b.md)\n- [DiffusionGemma 26B A4B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 12B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 12B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 26B A4B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 26B A4B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 31B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 31B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 E2B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 E2B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-unlimited-ocr-2.md)\n- [Gemma 4 E4B IT vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-spark-x2-6-4b.md)\n- [Gemma 4 E4B IT vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-unlimited-ocr-2.md)\n- [GLM-4.5-Air vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-spark-x2-6-4b.md)\n- [GLM-4.5-Air vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-4-5-air-vs-unlimited-ocr-2.md)\n- [GLM-4.7-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-spark-x2-6-4b.md)\n- [GLM-4.7-Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2.md)\n- [GLM-5.3-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-spark-x2-6-4b.md)\n- [GLM-5.3-Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-unlimited-ocr-2.md)\n- [GLM-5.3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/glm-5-3-vs-spark-x2-6-4b.md)\n- [GLM-5.3 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-5-3-vs-unlimited-ocr-2.md)\n- [gpt-oss-120b vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-spark-x2-6-4b.md)\n- [gpt-oss-120b vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-unlimited-ocr-2.md)\n- [gpt-oss-20b vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-spark-x2-6-4b.md)\n- [gpt-oss-20b vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2.md)\n- [Granite 4.2 3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-spark-x2-6-4b.md)\n- [Granite 4.2 3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-unlimited-ocr-2.md)\n- [Hy3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/hy3-vs-spark-x2-6-4b.md)\n- [Hy3 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2.md)\n- [Inkling-Small vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b.md)\n- [Inkling-Small vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.md)\n- [Inkling vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/inkling-vs-spark-x2-6-4b.md)\n- [Inkling vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-vs-unlimited-ocr-2.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-spark-x2-6-4b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-unlimited-ocr-2.md)\n- [Kimi K3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kimi-k3-vs-spark-x2-6-4b.md)\n- [Kimi K3 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kimi-k3-vs-unlimited-ocr-2.md)\n- [Kolibri-1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b.md)\n- [Kolibri-1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.md)\n- [Kronos vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/kronos-vs-spark-x2-6-4b.md)\n- [Kronos vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2.md)\n- [Laguna M.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.md)\n- [Laguna M.1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/laguna-m-1-vs-unlimited-ocr-2.md)\n- [Laguna S 2.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-spark-x2-6-4b.md)\n- [Laguna S 2.1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2.md)\n- [Laguna XS 2.1 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-spark-x2-6-4b.md)\n- [Laguna XS 2.1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-unlimited-ocr-2.md)\n- [LBS-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lbs-27b-vs-spark-x2-6-4b.md)\n- [LBS-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.md)\n- [LFM2.5-2.6B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.md)\n- [LFM2.5-2.6B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-unlimited-ocr-2.md)\n- [Ling-3.0-tiny vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-spark-x2-6-4b.md)\n- [Ling-3.0-tiny vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-unlimited-ocr-2.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-spark-x2-6-4b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-unlimited-ocr-2.md)\n- [MiniCPM-o 4.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-spark-x2-6-4b.md)\n- [MiniCPM-o 4.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-unlimited-ocr-2.md)\n- [MiniCPM5-2B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minicpm5-2b-vs-spark-x2-6-4b.md)\n- [MiniCPM5-2B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minicpm5-2b-vs-unlimited-ocr-2.md)\n- [MiniMax-M2.5 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.md)\n- [MiniMax-M2.5 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.md)\n- [MiniMax-M2.7 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m2-7-vs-spark-x2-6-4b.md)\n- [MiniMax-M2.7 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m2-7-vs-unlimited-ocr-2.md)\n- [MiniMax-M3.1-Flash vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-spark-x2-6-4b.md)\n- [MiniMax-M3.1-Flash vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-unlimited-ocr-2.md)\n- [Muse Glimmer 30B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-spark-x2-6-4b.md)\n- [Muse Glimmer 30B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-unlimited-ocr-2.md)\n- [Nanbeige4.3-33B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b.md)\n- [Nanbeige4.3-33B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-unlimited-ocr-2.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-spark-x2-6-4b.md)\n- [NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-unlimited-ocr-2.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-spark-x2-6-4b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-unlimited-ocr-2.md)\n- [Nemotron 3 Nano 30B A3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-spark-x2-6-4b.md)\n- [Nemotron 3 Nano 30B A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-unlimited-ocr-2.md)\n- [Nemotron 3 Nano 4B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-spark-x2-6-4b.md)\n- [Nemotron 3 Nano 4B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-unlimited-ocr-2.md)\n- [Nemotron 3 Nano Omni 30B A3B Reasoning vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-spark-x2-6-4b.md)\n- [Nemotron 3 Super 120B A12B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-spark-x2-6-4b.md)\n- [Nemotron 3 Super 120B A12B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-unlimited-ocr-2.md)\n- [Nemotron 3 Ultra 550B A55B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-spark-x2-6-4b.md)\n- [Nemotron 3 Ultra 550B A55B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-unlimited-ocr-2.md)\n- [occamy-1.0 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/occamy-1-0-vs-spark-x2-6-4b.md)\n- [occamy-1.0 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.md)\n- [Olmo 3 7B Think vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-spark-x2-6-4b.md)\n- [Olmo 3 7B Think vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2.md)\n- [Ornith-1.5-35B-A3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-spark-x2-6-4b.md)\n- [Ornith-1.5-35B-A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-unlimited-ocr-2.md)\n- [Ornith-1.5-397B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-spark-x2-6-4b.md)\n- [Ornith-1.5-397B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2.md)\n- [Ornith-1.5-9B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.md)\n- [Ornith-1.5-9B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2.md)\n- [Qwen3-30B-A3B-Instruct-2507 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-spark-x2-6-4b.md)\n- [Qwen3-30B-A3B-Instruct-2507 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-unlimited-ocr-2.md)\n- [Qwen3-4B-Instruct-2507 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-spark-x2-6-4b.md)\n- [Qwen3-4B-Instruct-2507 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2.md)\n- [Qwen3.5-0.8B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-spark-x2-6-4b.md)\n- [Qwen3.5-0.8B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2.md)\n- [Qwen3.5-122B-A10B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-spark-x2-6-4b.md)\n- 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Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-5-9b-vs-unlimited-ocr-2.md)\n- [Qwen3.6-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-spark-x2-6-4b.md)\n- [Qwen3.6-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-6-27b-vs-unlimited-ocr-2.md)\n- [Qwen3.6-35B-A3B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-spark-x2-6-4b.md)\n- [Qwen3.6-35B-A3B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-unlimited-ocr-2.md)\n- [Qwen3.8-27B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-spark-x2-6-4b.md)\n- [Qwen3.8-27B vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/qwen3-8-27b-vs-unlimited-ocr-2.md)\n- [Qwen3.8-Flash-Next vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-spark-x2-6-4b.md)\n- [Qwen3.8-Flash-Next vs Baidu 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