{ "data": { "a": { "slug": "deepseek-v4-1-flash", "name": "DeepSeek V4.1 Flash", "vendor": "DeepSeek", "vendorUrl": "https://www.deepseek.com", "kind": "weights", "category": "open-weights", "summary": "DeepSeek V4.1 Flash is DeepSeek's open-weight multimodal mixture-of-experts model, 552B backbone parameters with 8B active in prefill and 16B in decode and a 1M-token context, published on Hugging Face under MIT. The 17 October update is graded.", "url": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash", "markdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json", "repo": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash-1017" }, { "registry": "huggingface", "name": "deepseek-ai/DeepSeek-V4.1-Flash" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence. DeepSeek's own API serves the same weights as deepseek-flash, listed separately. Running them yourself costs the hardware, 510 GB of files per repository (checked 2026-10-20).", "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 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "mit", "safetensors", "fp8", "self-hosted", "new" ], "lastRelease": "2026-10-17", "graded": true, "anchor": { "graded": true, "score": 59, "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": 56, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 69, "interface": 75, "reach": 50, "risk": 50, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.", "bestFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.", "strengths": [ "MIT licence with no use policy, downloadable with no account", "1M tokens of context trained in, with an effort budget from 1 to 100 and a non-thinking mode", "Tool-call format documented both ways with test cases, and DeepSeek ships the parser", "JSON Schema output documented in DeepSeek's deepseek-recipe library", "Released support in vLLM 0.32.0 and SGLang 0.5.22, and DeepSeek's API serves the same update" ], "weaknesses": [ "510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine", "No Jinja template in the 17 October repository, prompts are built by running DeepSeek's code", "No safety evaluation or agent-use guidance, and only a company-wide security address", "None of the 40 most recent discussions had a reply from DeepSeek", "Not in llama.cpp, Transformers, LM Studio or Ollama's local library" ], "agentNotes": [ "DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive", "In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command", "Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding", "Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4", "Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75", "Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends", "Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "capability": 69, "interface": 75, "reach": 50, "risk": 50, "upkeep": 48 }, "provenanceScore": 73, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "lastMoved": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# DeepSeek's reference demo, from the repository's inference folder (8 GPUs)\npip install -r inference/requirements.txt\npython convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" \\\n --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\"\ntorchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/deepseek-v4-1-flash" }, "sameCompany": [ "deepseek-search-api", "deepseek-v3-2", "deepseek-v4-flash-0731" ], "sameModel": [ "deepseek-api" ], "weights": { "trainedBy": "DeepSeek", "params": "552B", "active": "8B prefill, 16B decode", "context": 1048576, "goodFor": "Multimodal mixture-of-experts model for agents and long context, text and images in, with tool calls and a reasoning budget.", "runtimes": [ "vLLM", "SGLang" ], "builds": [ { "name": "DeepSeek-V4.1-Flash-1017", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017", "who": "publisher", "precision": "fp8+fp4", "sizeGB": 510.3, "memoryGB": 1024, "format": "safetensors", "note": "FP8 with FP4 experts (F8_E4M3 and I8 tensors); the graded 17 October update. The September repository, deepseek-ai/DeepSeek-V4.1-Flash, is the same size" } ], "run": "pip install -r inference/requirements.txt \u0026\u0026 python convert.py --hf-ckpt-path \"$HF_CKPT_PATH\" --save-path \"$SAVE_PATH\" --model-parallel 8 --expert-dtype fp4 --tokenizer-path \"$HF_CKPT_PATH\" \u0026\u0026 torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive", "runBuild": "DeepSeek-V4.1-Flash-1017", "checked": "2026-10-21", "minMemoryGB": 1024 }, "family": { "lab": "DeepSeek", "family": "DeepSeek V4", "url": "https://www.anchorterminal.com/open-weights/#deepseek", "others": [ { "name": "DeepSeek-V4-Flash-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Base", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-DSpark", "params": "284B total, 13B activated" }, { "name": "DeepSeek-V4-Flash-0731", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731", "params": "284B total, 13B activated (V4-Flash figures; the 0731 card does not restate them)", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-flash-0731" }, { "name": "DeepSeek-V4-Flash-Vision-Exp", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-Vision-Exp", "params": "not stated on card" }, { "name": "DeepSeek-V4-Pro-Base", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-Base", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-DSpark", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-DSpark", "params": "1.6T total, 49B activated" }, { "name": "DeepSeek-V4-Pro-0813", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4-Pro-0813", "params": "1.6T total, 49B activated (V4-Pro figures; the 0813 card does not restate them)" }, { "name": "DeepSeek-V4.1-Flash", "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash", "params": "552B backbone, 8B activated in prefill / 16B in decode", "listing": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash" } ], "olderCount": 72, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "DeepSeek (the MIT licence names DeepSeek, and DeepSeek's platform terms name Hangzhou DeepSeek Artificial Intelligence Co., Ltd.)", "domain": "deepseek.com", "domainRegistered": "2000-08-13", "domainNote": "deepseek.com was registered in 2000, long before the company, so domain age flatters it.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/commits/main", "securityTxt": "none", "checked": "2026-10-20", "notes": [ "The legal entity is the one DeepSeek's platform terms name, read for the deepseek-api listing. The weights' licence names only DeepSeek.", "Nothing is hosted, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the September repository's commit history, whose messages say what changed (an encoding fix on 16 October, an indexer cache fix on 12 October, the chat template on 1 October).", "deepseek.com/.well-known/security.txt and www.deepseek.com/.well-known/security.txt answered 404 on 20 October 2026.", "DeepSeek's site footer has a Report Security Issue link to security@deepseek.com, a company-wide address, read from https://www.deepseek.com/en/transparency/ on 21 October 2026." ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/deepseek-v4-1-flash.json", "live": { "slug": "deepseek-v4-1-flash", "securityTxt": { "url": "https://deepseek.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-10-27T15:31:54.004589507Z" }, "domain": { "domain": "deepseek.com", "registered": "2000-08-13", "source": "https://rdap.verisign.com/com/v1/domain/deepseek.com", "checkedAt": "2026-10-25T13:50:05.058593646Z" }, "pages": [ { "url": "https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-27T03:48:05.696936299Z", "changedAt": "2026-10-27T03:48:05.696936299Z", "fingerprint": "93907218c5f6" } ], "updatedAt": "2026-10-27T15:31:54.004589507Z" } }, "answer": "DeepSeek V4.1 Flash scores 59 (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 reach.", "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": "DeepSeek", "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": "MIT", "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": "2026-10-17", "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": "2.8/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "DeepSeek V4.1 Flash scores 59 (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 reach.", "question": "Which is better for AI agents, DeepSeek V4.1 Flash or Baidu Unlimited-OCR-2?" } ], "goodFor": [ { "aheadOn": [ "Risk, 50 against 45" ], "also": null, "goodFor": "Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.", "slug": "deepseek-v4-1-flash", "watchFor": "510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine" }, { "aheadOn": [ "Reach, 55 against 50" ], "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": 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"https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2.json", "title": "occamy-1.0 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2.json", "title": "Olmo 3 7B Think vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-35B-A3B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-397B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-9B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-unlimited-ocr-2.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2.json", "title": "Qwen3-4B-Instruct-2507 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-0.8B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-122B-A10B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-2B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-397B-A17B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2.json", "title": "Qwen3.5-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-unlimited-ocr-2" }, { "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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-unlimited-ocr-2.json", "title": "Spark-X2.6-4B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-unlimited-ocr-2" }, { "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": 5, "deepseek-v4-1-flash": 50, "edge": "unlimited-ocr-2", "key": "reach", "name": "Reach", "unlimited-ocr-2": 55, "weight": 25 }, { "by": 5, "deepseek-v4-1-flash": 50, "edge": "deepseek-v4-1-flash", "key": "risk", "name": "Risk", "unlimited-ocr-2": 45, "weight": 20 }, { "by": 0, "deepseek-v4-1-flash": 75, "edge": "", "key": "interface", "name": "Agent interface", "unlimited-ocr-2": 75, "weight": 10 }, { "by": 3, "deepseek-v4-1-flash": 48, "edge": "deepseek-v4-1-flash", "key": "upkeep", "name": "Documentation and upkeep", "unlimited-ocr-2": 45, "weight": 10 } ], "summary": "DeepSeek V4.1 Flash scores 59 (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 reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "deepseek-v4-1-flash": "A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.", "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/deepseek-v4-1-flash-vs-unlimited-ocr-2", "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.min.md" }, "markdown": "DeepSeek V4.1 Flash scores 59 (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 reach. Both do open-weight models you run yourself.\n\n- DeepSeek V4.1 Flash: grade C, 59/100, rank #68 of 113 open-weight models. Markdown https://www.anchorterminal.com/tools/deepseek-v4-1-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.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### DeepSeek V4.1 Flash (C)\n\nGood for: Teams with a multi-GPU server who want DeepSeek's agent model under MIT, with the hosted API as a fallback.\n\nAhead on:\n- Risk, 50 against 45\n\nWatch for: 510.3 GB of files and no smaller build anywhere, so it won't fit the 107.4 GB reference machine\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- Reach, 55 against 50\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 | DeepSeek V4.1 Flash | Baidu Unlimited-OCR-2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 55 | Baidu Unlimited-OCR-2 +5 |\n| Risk | 20% (20 this run) | 50 | 45 | DeepSeek V4.1 Flash +5 |\n| Agent interface | 10% (10 this run) | 75 | 75 | even |\n| Documentation and upkeep | 10% (10 this run) | 48 | 45 | DeepSeek V4.1 Flash +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **59 · C** | **53.5 · D** | |\n\n## Facts side by side\n\n| Fact | DeepSeek V4.1 Flash | Baidu Unlimited-OCR-2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | DeepSeek | 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 | MIT | 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 | 2026-10-17 | 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 | 2.8/5 (4) | none |\n\n## Verdicts\n\n**DeepSeek V4.1 Flash.** A large multimodal agent model with a 1M-token context, documented tool calls, JSON Schema output and a reasoning budget, under MIT. It needs about 612 GB of memory across several GPUs in vLLM or SGLang. The main risks are prompts built from DeepSeek's code rather than a template, no published safety results and no answers in its repositories.\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### DeepSeek V4.1 Flash\n\n1. DeepSeek's reference run is its inference folder, convert.py with --model-parallel 8 --expert-dtype fp4, then torchrun --nproc-per-node 8 generate.py --ckpt-path \"$SAVE_PATH\" --config config.json --interactive\n2. In vLLM 0.32.0 the parsers are named deepseek_v41 for both --tool-call-parser and --reasoning-parser. That's vLLM's naming, DeepSeek doesn't document a vLLM command\n3. Build prompts with encoding.py or pip install deepseek-recipe, not the September repository's chat_template.jinja, which an open pull request says trails the 16 October encoding\n4. Tool calls come back in a DSML calls block with a space before calls, invoke and parameter, unlike V4\n5. Set reasoning_effort to 1 to 100, or low, high or max (50, 75, 100), in thinking mode only. Default is 75\n6. Sample at temperature 1.0 and top_p 1.0 and allow 256K or more output tokens, as the card recommends\n7. Without the hardware, call DeepSeek's API as deepseek-flash, which serves this exact update\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, DeepSeek V4.1 Flash or Baidu Unlimited-OCR-2?\n\nDeepSeek V4.1 Flash scores 59 (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 reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-unlimited-ocr-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"deepseek-v4-1-flash\", \"b\": \"unlimited-ocr-2\"}`. From a terminal: `anchor compare deepseek-v4-1-flash unlimited-ocr-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/deepseek-v4-1-flash.json and https://www.anchorterminal.com/api/v1/tools/unlimited-ocr-2.json\n\n## Other comparisons with DeepSeek V4.1 Flash or Baidu Unlimited-OCR-2\n\n- [Agents-A1-4B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.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 DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.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 DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-deepseek-v4-1-flash.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 DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-deepseek-v4-1-flash.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 DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/carbon-x-vs-deepseek-v4-1-flash.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 DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-deepseek-v4-1-flash.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 DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-deepseek-v4-1-flash.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 DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-deepseek-v4-1-flash.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 DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-deepseek-v4-flash-0731.md)\n- [DeepSeek V4.1 Flash vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-diffusiongemma-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-12b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-26b-a4b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-31b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e2b-it.md)\n- [DeepSeek V4.1 Flash vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gemma-4-e4b-it.md)\n- [DeepSeek V4.1 Flash vs GLM-4.5-Air](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-5-air.md)\n- [DeepSeek V4.1 Flash vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-4-7-flash.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3.md)\n- [DeepSeek V4.1 Flash vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash.md)\n- [DeepSeek V4.1 Flash vs gpt-oss-120b](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-120b.md)\n- [DeepSeek V4.1 Flash vs gpt-oss-20b](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-gpt-oss-20b.md)\n- [DeepSeek V4.1 Flash vs Granite 4.2 3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-granite-4-2-3b.md)\n- [DeepSeek V4.1 Flash vs Hy3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3.md)\n- [DeepSeek V4.1 Flash vs Inkling](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling.md)\n- [DeepSeek V4.1 Flash vs Inkling-Small](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-inkling-small.md)\n- [DeepSeek V4.1 Flash vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [DeepSeek V4.1 Flash vs Kimi K3](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kimi-k3.md)\n- [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md)\n- [DeepSeek V4.1 Flash vs Kronos](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kronos.md)\n- [DeepSeek V4.1 Flash vs Laguna M.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-m-1.md)\n- [DeepSeek V4.1 Flash vs Laguna S 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-s-2-1.md)\n- [DeepSeek V4.1 Flash vs Laguna XS 2.1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-laguna-xs-2-1.md)\n- [DeepSeek V4.1 Flash vs LBS-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lbs-27b.md)\n- [DeepSeek V4.1 Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.md)\n- [DeepSeek V4.1 Flash vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ling-3-0-tiny.md)\n- [DeepSeek V4.1 Flash vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-mimo-v2-6-distill-qwen-9b.md)\n- [DeepSeek V4.1 Flash vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm-o-4-5.md)\n- [DeepSeek V4.1 Flash vs MiniCPM5-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minicpm5-2b.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.5](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M2.7](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-7.md)\n- [DeepSeek V4.1 Flash vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m3-1-flash.md)\n- [DeepSeek V4.1 Flash vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-muse-glimmer-30b.md)\n- [DeepSeek V4.1 Flash vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-3-33b.md)\n- [DeepSeek V4.1 Flash vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-30b-a3b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-4b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-super-120b-a12b.md)\n- [DeepSeek V4.1 Flash vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nemotron-3-ultra-550b-a55b.md)\n- [DeepSeek V4.1 Flash vs occamy-1.0](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-occamy-1-0.md)\n- [DeepSeek V4.1 Flash vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-olmo-3-7b-think.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-397b.md)\n- [DeepSeek V4.1 Flash vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.md)\n- [DeepSeek V4.1 Flash vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-30b-a3b-instruct-2507.md)\n- [DeepSeek V4.1 Flash vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-4b-instruct-2507.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-0-8b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-122b-a10b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-2B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-2b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-397b-a17b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-4B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-4b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.5-9B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-5-9b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.6-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-27b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-6-35b-a3b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.md)\n- [DeepSeek V4.1 Flash vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-flash-next.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-coder-next.md)\n- [DeepSeek V4.1 Flash vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-omni-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-2b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-30b-a3b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-32b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-4b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-vl-8b-instruct.md)\n- [DeepSeek V4.1 Flash vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwopus3-8-27b-flash-v2.md)\n- [DeepSeek V4.1 Flash vs Recursive Flint](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-recursive-flint.md)\n- [DeepSeek V4.1 Flash vs SmolLM3-3B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-smollm3-3b.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 Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-27b.md)\n- [DeepSeek V4.1 Flash vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-swift-1-5-qwen3-8-flash-next.md)\n- [DeepSeek V4.1 Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-thinkingcap-qwen3-8-27b.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-unlimited-ocr-2.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 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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/glm-5-3-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/hy3-vs-unlimited-ocr-2.md)\n- [Inkling-Small vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/inkling-small-vs-unlimited-ocr-2.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 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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kimi-k3-vs-unlimited-ocr-2.md)\n- [Kolibri-1 vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.md)\n- [Kronos vs Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/kronos-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/lbs-27b-vs-unlimited-ocr-2.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 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 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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m2-5-vs-unlimited-ocr-2.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 Baidu Unlimited-OCR-2](https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-unlimited-ocr-2.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 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 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 Baidu 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