{ "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "tool": { "slug": "hunyuanocr", "name": "HunyuanOCR 1.5", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "weights", "category": "document-extraction", "summary": "HunyuanOCR 1.5 is Tencent's open-weight OCR model of 1.1B parameters for document parsing, text spotting, information extraction and translating text in images, with a 128K context, under a licence that doesn't apply in the EU, the UK or South Korea.", "url": "https://www.anchorterminal.com/tools/hunyuanocr", "markdownUrl": "https://www.anchorterminal.com/tools/hunyuanocr.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hunyuanocr.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hunyuanocr.json", "dns": "hunyuanocr.grade.anchorterminal.com", "repo": "https://huggingface.co/tencent/HunyuanOCR", "license": "Tencent Hunyuan Community License (not licensed in the EU, the UK or South Korea)", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/HunyuanOCR" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download. The Tencent Hunyuan Community License allows commercial use under Tencent's acceptable use policy, except for anyone with more than 100 million monthly active users, and it doesn't apply in the European Union, the United Kingdom or South Korea, where it grants no rights. Running it yourself costs little hardware, 2.24 GB in BF16 (checked 2026-11-10).", "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 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/Tencent-Hunyuan/HunyuanOCR", "capabilities": [ "docs.ocr", "docs.parse", "docs.extract", "docs.tables", "model.open-weights" ], "tags": [ "open-weights", "model", "ocr", "document-parsing", "text-spotting", "information-extraction", "translation", "vision", "custom-licence", "territory-limit", "safetensors", "self-hosted" ], "lastRelease": "2026-08-28", "graded": true, "anchor": { "graded": true, "score": 56.6, "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": 1518, "categoryRank": 19, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 80, "reach": 60, "risk": 45, "upkeep": 48 }, "pending": [ "performance", "tasks" ], "breakdown": [ { "key": "capability", "name": "Capability", "weight": 35, "effectiveWeight": 0, "pending": true, "points": 0, "reason": "Pending for this listing. Nobody independent has published results for it in at least two areas yet, so its weight is shared across the other four and the total is capped at 61 until they do or our own runs start." }, { "key": "reach", "name": "Reach", "weight": 25, "effectiveWeight": 38.46, "score": 60, "points": 23.08, "reason": "Memory 30, the only build from Tencent is the BF16 safetensors file at the root, 2.24 GB, and 2.24 GB × 1.2 = 2.7 GB, within 16 GB. ggml-org's GGUF (Q8_0 0.58 GB with a 0.73 GB projector) dates from 21 May 2026 and wasn't matched to version 1.5. Runtimes 15, Released Transformers 5.22.2 has the hunyuan_vl model, vLLM 0.33.0 registers HunYuanVLForConditionalGeneration, and llama.cpp b12242 has the architecture, which the card says the community build runs after conversion. Builds 0, Tencent publishes the BF16 weights only and tells users to convert to GGUF themselves. Terms 5, A custom licence that allows free commercial use under a use policy and below 100 million monthly active users, but not for anyone: it doesn't apply in the European Union, the United Kingdom or South Korea. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no Tencent API that serves it. 30 + 15 + 0 + 5 + 10 + 0 = 60.", "checked": "2026-11-10" }, { "key": "risk", "name": "Risk", "weight": 20, "effectiveWeight": 30.77, "score": 45, "points": 13.85, "reason": "File safety 25, safetensors only, no .bin, .pt or .pth files, and no Python at the root. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or use section, and nothing on text in an image that tries to instruct the reader. Licence risk 0, 15 less 5 for limits beyond the law (an acceptable use policy and three territories where the licence doesn't apply), 5 for terms the licensor can change (Tencent may update the policy from time to time) and 5 for the 100 million monthly active user threshold. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 0 + 5 = 45.", "checked": "2026-11-10" }, { "key": "interface", "name": "Agent interface", "weight": 10, "effectiveWeight": 15.38, "score": 80, "points": 12.31, "reason": "Chat template 20, chat_template.jinja in the repository (930 characters). Tool-calling format 25, Read for a document parser as what it takes and returns. Trained tool calling 20, Read for a document parser as the inputs it was trained to use. Structured output 5, Read for a document parser as typed outputs. Reasoning control 0, A document parser with no reasoning. Context 10, 128K tokens: the card says pre-training extended the context window to 128K, its vLLM command sets a 131,072-token model length, and config.json gives the language model 131,072 positions. 20 + 25 + 20 + 5 + 0 + 10 = 80.", "checked": "2026-11-10" }, { "key": "upkeep", "name": "Documentation and upkeep", "weight": 10, "effectiveWeight": 15.38, "score": 48, "points": 7.38, "reason": "Model card 8, Intended use, the architecture by reference to 1.0's unchanged backbone, and the context (128K) are on the card. Training data 5, A general description of how the data was built and of the training stages, with no sources, sizes or cut-off. Last change 15, the last commit, on 28 August 2026, changed config.json, 74 days before this check and within 90. Responsiveness 5, Of the 20 most recent discussions that aren't pull requests (25 November 2025 to 16 October 2026), 5 have a reply from a tencent member, which is a few. Version policy 5, Version 1.5 replaced 1.0 at the root of the same repository. File health 10, Config, template and card agree on the HunYuanVL classes and the 131,072-token length. 8 + 5 + 15 + 5 + 5 + 10 = 48.", "checked": "2026-11-10" } ], "assessment": { "date": "2026-11-10", "basis": "public evidence", "confidence": "low", "notes": { "capability": "Pending for this listing. Independent results were found in 0 areas (none), and Capability needs 2. Its weight is shared across the other four categories and the total is capped at 61 until there are more, or our own runs start.", "interface": "Chat template 20, chat_template.jinja in the repository (930 characters). Tool-calling format 25, Read for a document parser as what it takes and returns. Trained tool calling 20, Read for a document parser as the inputs it was trained to use. Structured output 5, Read for a document parser as typed outputs. Reasoning control 0, A document parser with no reasoning. Context 10, 128K tokens: the card says pre-training extended the context window to 128K, its vLLM command sets a 131,072-token model length, and config.json gives the language model 131,072 positions. 20 + 25 + 20 + 5 + 0 + 10 = 80.", "reach": "Memory 30, the only build from Tencent is the BF16 safetensors file at the root, 2.24 GB, and 2.24 GB × 1.2 = 2.7 GB, within 16 GB. ggml-org's GGUF (Q8_0 0.58 GB with a 0.73 GB projector) dates from 21 May 2026 and wasn't matched to version 1.5. Runtimes 15, Released Transformers 5.22.2 has the hunyuan_vl model, vLLM 0.33.0 registers HunYuanVLForConditionalGeneration, and llama.cpp b12242 has the architecture, which the card says the community build runs after conversion. Builds 0, Tencent publishes the BF16 weights only and tells users to convert to GGUF themselves. Terms 5, A custom licence that allows free commercial use under a use policy and below 100 million monthly active users, but not for anyone: it doesn't apply in the European Union, the United Kingdom or South Korea. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and the card names no Tencent API that serves it. 30 + 15 + 0 + 5 + 10 + 0 = 60.", "risk": "File safety 25, safetensors only, no .bin, .pt or .pth files, and no Python at the root. Provenance 15, the files sit in the tencent organisation, which Tencent's GitHub repository for the model links to (10), and the Hub gives a SHA-256 for the large files (5). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or use section, and nothing on text in an image that tries to instruct the reader. Licence risk 0, 15 less 5 for limits beyond the law (an acceptable use policy and three territories where the licence doesn't apply), 5 for terms the licensor can change (Tencent may update the policy from time to time) and 5 for the 100 million monthly active user threshold. Security programme 5, Tencent's Security Response Center takes vulnerability reports company-wide, as the Hy3 listing records (5). 25 + 15 + 0 + 0 + 0 + 5 = 45.", "upkeep": "Model card 8, Intended use, the architecture by reference to 1.0's unchanged backbone, and the context (128K) are on the card. Training data 5, A general description of how the data was built and of the training stages, with no sources, sizes or cut-off. Last change 15, the last commit, on 28 August 2026, changed config.json, 74 days before this check and within 90. Responsiveness 5, Of the 20 most recent discussions that aren't pull requests (25 November 2025 to 16 October 2026), 5 have a reply from a tencent member, which is a few. Version policy 5, Version 1.5 replaced 1.0 at the root of the same repository. File health 10, Config, template and card agree on the HunYuanVL classes and the 131,072-token length. 8 + 5 + 15 + 5 + 5 + 10 = 48." }, "sources": [ { "what": "model card", "url": "https://huggingface.co/tencent/HunyuanOCR", "seen": "2026-11-10" }, { "what": "gating, dates, parameter count, downloads, likes, file list and hashes", "url": "https://huggingface.co/api/models/tencent/HunyuanOCR?blobs=true", "seen": "2026-11-10" }, { "what": "inference providers, none mapped", "url": "https://huggingface.co/api/models/tencent/HunyuanOCR?expand[]=inferenceProviderMapping", "seen": "2026-11-10" }, { "what": "commit history", "url": "https://huggingface.co/api/models/tencent/HunyuanOCR/commits/main", "seen": "2026-11-10" }, { "what": "architecture and positions", "url": "https://huggingface.co/tencent/HunyuanOCR/raw/main/config.json", "seen": "2026-11-10" }, { "what": "chat template", "url": "https://huggingface.co/tencent/HunyuanOCR/raw/main/chat_template.jinja", "seen": "2026-11-10" }, { "what": "the licence and its acceptable use policy", "url": "https://huggingface.co/tencent/HunyuanOCR/raw/main/LICENSE", "seen": "2026-11-10" }, { "what": "discussions, replies counted by script from authors, dates and member flags", "url": "https://huggingface.co/api/models/tencent/HunyuanOCR/discussions", "seen": "2026-11-10" }, { "what": "ggml-org's GGUF of HunyuanOCR, dated 21 May 2026", "url": "https://huggingface.co/api/models/ggml-org/HunyuanOCR-GGUF?blobs=true", "seen": "2026-11-10" }, { "what": "vLLM v0.33.0 registry, HunYuanVLForConditionalGeneration", "url": "https://raw.githubusercontent.com/vllm-project/vllm/v0.33.0/vllm/model_executor/models/registry.py", "seen": "2026-11-10" }, { "what": "Transformers v5.22.2 hunyuan_vl", "url": "https://github.com/huggingface/transformers/tree/v5.22.2/src/transformers/models/hunyuan_vl", "seen": "2026-11-10" }, { "what": "llama.cpp b12242, the hunyuan architecture (shallow clone)", "url": "https://raw.githubusercontent.com/ggml-org/llama.cpp/b12242/src/llama-arch.cpp", "seen": "2026-11-10" }, { "what": "our Hy3 and Hy-Memory listings, for company facts", "url": "https://www.anchorterminal.com/tools/hy3", "seen": "2026-11-10" } ], "openQuestions": [ "Terms is 5 because the licence doesn't apply in three regions, which the 10 band's word anyone rules out. A reading that takes the band by its threshold and use policy gives 10", "It's listed on a site run from London, where its licence doesn't apply. The listing says so first in the notes for agents", "Structured output is 5 on the card alone. Tencent's GitHub docs may document typed output for spotting and extraction", "ggml-org publishes a GGUF from May 2026. Whether it's of version 1.5 wasn't established, so it isn't recorded as a build", "Capability is pending because no independent results were looked up on the day", "unchecked on 10 November: the GitHub repository's docs and issues, the paper (arXiv 2607.04884), the dflash folder's code, and Tencent's security pages, read for the Hy3 listing" ] }, "negative": 0, "verdict": "A small OCR model that does documents, text spotting, extraction and translation in one, with a 128K context and code in Transformers, vLLM and llama.cpp. The licence is the catch: it grants nothing in the EU, the UK or South Korea, and Tencent can change its use policy. No quantised build, safety results or data sources.", "bestFor": "Teams outside the EU, the UK and South Korea that want one small model for OCR, extraction and image translation", "strengths": [ "Four OCR jobs in one 1.1B model, with several images in a request", "A 128K context, trained for it per the card", "Released Transformers, vLLM and llama.cpp have the architecture", "Fits any machine: 2.24 GB in BF16", "The card documents prompts, settings and three ways to run it" ], "weaknesses": [ "The licence doesn't apply in the EU, the UK or South Korea", "Tencent can update the acceptable use policy, and a 100 million user threshold applies", "No GGUF or other quantised build from Tencent", "Version 1.5 replaced 1.0 in the same repository", "A few of the questions on the repository answered", "No limitations, safety results or data sources on the card" ], "agentNotes": [ "Check where you and your users are first. The licence grants no rights in the European Union, the United Kingdom or South Korea", "Download with `--exclude \"v1.0/*\"` so the old version isn't fetched", "Serve it with vLLM 0.25.1 or later at `--max-model-len 131072`, or use Tencent's scripts on GitHub for its sampling and post-processing", "Use temperature 0, top_p 1 and repetition_penalty 1.08", "The default document prompt is in Chinese and is on the card. Other tasks take their own instruction as the prompt", "For llama.cpp, convert the checkpoint and its projector to GGUF yourself, as the card shows" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 56.6 } ], "editorialScores": { "interface": 80, "reach": 60, "risk": 45, "upkeep": 48 }, "provenanceScore": 68, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 }, "scoreHistory": [ { "date": "2026-11-10", "grade": "C", "kind": "listed", "method": "0.7", "score": 56.6 } ] }, "connect": { "install": "pip install \"vllm\u003e=0.25.1\"\nhf download tencent/HunyuanOCR --local-dir ./HunyuanOCR --exclude \"v1.0/*\"\nvllm serve ./HunyuanOCR --served-model-name tencent/HunyuanOCR --max-model-len 131072\n# Tencent's own scripts, with its sampling and post-processing, are in github.com/Tencent-Hunyuan/HunyuanOCR (inference/vLLM/serve.sh)", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1. Send one image with a task prompt,\n# temperature 0, top_p 1 and repetition_penalty 1.08, per the card" }, "letme": { "capability": "https://letme.dev/docs.ocr", "tool": "https://letme.dev/hunyuanocr" }, "sameCompany": [ "hy-memory", "tencentdb-agent-memory", "hy-memory-30b-a3b", "hy3", "wevisone-vl-2b", "wevisone-vl-4b" ], "weights": { "trainedBy": "Tencent Hunyuan", "params": "1.1B", "context": 131072, "goodFor": "Small, fast OCR for documents, text in photos, field extraction and translating text in images, where the licence applies. It doesn't in the EU, the UK or South Korea.", "runtimes": [ "llama.cpp", "vLLM", "Transformers" ], "builds": [ { "name": "HunyuanOCR 1.5 (BF16 safetensors)", "url": "https://huggingface.co/tencent/HunyuanOCR/blob/main/model.safetensors", "who": "publisher", "precision": "bf16", "sizeGB": 2.24, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "The root of the repository. Leave out the v1.0 folder when downloading, as the card says" } ], "run": "vllm serve tencent/HunyuanOCR --max-model-len 131072", "runBuild": "HunyuanOCR 1.5 (BF16 safetensors)", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "HunyuanOCR", "url": "https://www.anchorterminal.com/open-weights/#tencent", "olderCount": 22 }, "notable": [ "625,120 downloads in the last 30 days and 846 likes on Hugging Face on 10 November 2026 (https://huggingface.co/api/models/tencent/HunyuanOCR)", "The licence says it doesn't apply in the European Union, the United Kingdom and South Korea, and its acceptable use policy lists use outside the territory first among what's not allowed (https://huggingface.co/tencent/HunyuanOCR/raw/main/LICENSE)", "Version 1.5 replaced 1.0 at the root of the same repository, with 1.0 kept in a v1.0 folder and a draft model for speculative decoding in a dflash folder (https://huggingface.co/tencent/HunyuanOCR/tree/main)", "Tencent ships no GGUF. The card tells you to convert the checkpoint yourself for llama.cpp, and ggml-org's GGUF dates from May 2026, before the root files last changed (https://huggingface.co/tencent/HunyuanOCR)", "The default prompt for document parsing is in Chinese and asks for Markdown with tables as HTML and formulas as LaTeX, in reading order (https://huggingface.co/tencent/HunyuanOCR)", "A Tencent member replied in 5 of the 20 most recent discussions (https://huggingface.co/api/models/tencent/HunyuanOCR/discussions)" ], "area": "web-data", "details": [ { "label": "Surface graded", "value": "The open weights at huggingface.co/tencent/HunyuanOCR, version 1.5 at the root of the repository, read from the model card, the files and their metadata, the config, the chat template, the licence, the commit history and the runtimes' own code at their latest releases on 10 November 2026. Tencent's company facts come from our other Tencent listings. The GitHub repository's docs and the paper weren't read. Nothing was downloaded or run" }, { "label": "Model", "value": "1,119,925,744 parameters in the files. HunYuanVLForConditionalGeneration, a 24-layer language model with a 27-layer vision encoder. Version 1.5 keeps 1.0's backbone and changes the training, with images up to 4K and a 128K context. The card gives no parameter count" }, { "label": "Tasks", "value": "Document parsing, text spotting, information extraction and translation of text in images, chosen by the text prompt. The card says 1.5 adds low-resource and ancient-script OCR and questions over several images" }, { "label": "Languages", "value": "The metadata says multilingual, English and Chinese. The card names no others" }, { "label": "Files", "value": "One safetensors file in BF16 at the root (2.24 GB) with chat_template.jinja, config, processor and tokenizer files and the LICENSE. A dflash folder holds a 0.36 GB draft model and one Python file for speculative decoding, and a v1.0 folder holds the previous version. No pickle files. Hugging Face lists a SHA-256 for the large files" }, { "label": "Licence", "value": "The Tencent Hunyuan Community License Agreement, dated 25 November 2025. It doesn't apply in the European Union, the United Kingdom and South Korea. Elsewhere it allows use, changes and distribution under an acceptable use policy that Tencent may update, and anyone with more than 100 million monthly active users must ask Tencent for a licence. Tencent's Hy3 and Hy-Memory are Apache 2.0; this isn't" }, { "label": "Runtimes", "value": "The card documents Transformers (5.13.0 or later, with its own HunYuanVL classes), vLLM (0.25.1 or later, with Tencent's scripts) and llama.cpp after converting the checkpoint to GGUF yourself. Released Transformers 5.22.2, vLLM 0.33.0 and llama.cpp b12242 carry the architecture. SGLang 0.5.23 doesn't. Speculative decoding in llama.cpp needs a fork" }, { "label": "Input and output", "value": "One image, or several, with a task prompt. The default document prompt returns Markdown in reading order with tables as HTML and formulas as LaTeX, leaving out headers and footers. Tencent's client adds early stopping and Markdown normalisation for document parsing" }, { "label": "Recommended settings", "value": "temperature 0, top_p 1, top_k off and repetition_penalty 1.08, per the card, with a 131,072-token model length for vLLM and a 10,240-token context in its llama.cpp example" }, { "label": "Hardware", "value": "No memory figure on the card. The root file is 2.24 GB in BF16. The card's unified environment needs CUDA 13, and its llama.cpp route is for CPUs, consumer GPUs and laptops" }, { "label": "Training data", "value": "A general description on the card: an agent-driven system it calls Agentic Data Flow built new data for weak spots, the third pre-training stage added that data with multi-image and historical OCR data, and post-training refined the fine-tuning data and used reinforcement learning. No sources, sizes or cut-off" } ], "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/tencent/HunyuanOCR/raw/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/tencent/HunyuanOCR/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "The licence names Tencent. The domain's registration date is carried from our other Tencent listings.", "Nothing is hosted under this listing, so the model licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 50 commits or more since 18 November 2025, the last two on 26 and 28 August 2026 changing config.json.", "Tencent's Security Response Center takes reports company-wide, per the Hy3 listing; nothing on the card links it." ], "score": 68, "checks": [ { "check": "Legal entity named", "value": "Tencent (named in the LICENSE file's copyright line)", "points": 20, "max": 20, "state": "ok" }, { "check": "Domain age", "value": "tencent.com, registered 1998-09-14 (28 years)", "points": 15, "max": 15, "state": "ok" }, { "check": "Endpoint on the vendor's domain", "value": "no hosted endpoint", "points": 0, "max": 0, "state": "na" }, { "check": "Terms of service", "value": "published, not read by us yet", "points": 4, "max": 10, "state": "part" }, { "check": "Privacy policy", "value": "nothing hosted, not scored", "points": 0, "max": 0, "state": "na" }, { "check": "Status page", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "Changelog", "value": "published", "points": 10, "max": 10, "state": "ok" }, { "check": "security.txt", "value": "not found", "points": 0, "max": 10, "state": "no" }, { "check": "People named", "value": "6 named with their role, none with a public profile", "points": 7, "max": 10, "state": "part" }, { "check": "Support route", "value": "help pages, a community", "points": 5, "max": 5, "state": "ok" } ] }, "humans": { "slug": "hunyuanocr", "checked": "2026-11-10", "company": "Tencent (Hy team)", "people": [ { "name": "Ma Huateng", "role": "Co-founder, chairman and CEO, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-ma-huateng.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "bio": "Ma Huateng, known as Pony Ma, co-founded Tencent in 1998 with four fellow classmates and is its chairman and chief executive. He studied computer science at Shenzhen University and wrote pager software at China Motion Telecom Development before Tencent. Its first product, the OICQ instant messenger, launched in February 1999.", "bioSources": [ "https://www.tencent.com/who-we-are/our-team/", "https://en.wikipedia.org/wiki/Ma_Huateng" ], "page": "/people/ma-huateng/" }, { "name": "Xu Chenye", "role": "Co-founder and Chief Information Officer, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-xu-chenye.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "page": "/people/xu-chenye/" }, { "name": "Zhang Zhidong", "role": "Co-founder, Advisor Emeritus and Honorary Dean of Tencent Academy, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-zhang-zhidong.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "page": "/people/zhang-zhidong/" }, { "name": "Chen Yidan", "role": "Co-founder and Advisor Emeritus, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-chen-yidan.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "bio": "Chen Yidan, also known as Charles Chen, co-founded Tencent in 1998 and was its chief administrative officer until March 2013, also leading the Tencent Charity Foundation. He remains an Advisor Emeritus at Tencent and in 2016 founded the Yidan Prize, which recognises contributions to education research and development.", "bioSources": [ "https://www.tencent.com/who-we-are/our-team/", "https://en.wikipedia.org/wiki/Chen_Yidan" ], "page": "/people/chen-yidan/" }, { "name": "Zeng Liqing", "role": "Co-founder and Advisor Emeritus, Tencent", "founder": true, "photo": "/assets/img/people/tencent-hy-team-zeng-liqing.webp", "photoSource": "https://www.tencent.com/who-we-are/our-team/", "page": "/people/zeng-liqing/" }, { "name": "Yao Shunyu", "role": "Chief AI Scientist, Tencent", "bio": "Yao Shunyu is Tencent's chief AI scientist and spoke for the company when it released the Hy3 preview in April 2026. He joined Tencent from OpenAI in 2025, where he worked on language agents built on large language models. He studied in Tsinghua University's Yao Class and holds a doctorate in computer science from Princeton.", "bioSources": [ "https://www.tencent.com/tencent-unveils-hy3-preview-model-enhances-agent-capabilities-and-real-world-usability/", "https://en.wikipedia.org/wiki/Shunyu_Yao" ], "page": "/people/yao-shunyu/" } ], "peopleNote": "Tencent's five co-founders as its management team page names them (read 28 October 2026), and its chief AI scientist, whom Tencent's Hy3 preview announcement quotes on the model. The Hy3 model card credits only the Tencent Hy Team. Tencent's president, Martin Lau, and its other senior officers are on the same page but are left out to keep the list to founders and the person speaking for the model.", "companySocial": { "linkedin": "https://www.linkedin.com/company/tencentglobal", "x": "https://x.com/TencentGlobal", "github": "https://github.com/Tencent-Hunyuan" }, "support": { "urls": [ "https://huggingface.co/tencent/HunyuanOCR/discussions", "https://github.com/Tencent-Hunyuan/Hy-Memory/issues" ], "community": [ { "label": "Hugging Face discussions", "url": "https://huggingface.co/tencent/Hy3/discussions", "kind": "forum" } ] }, "sources": [ "https://huggingface.co/tencent/Hy3", "https://www.tencent.com/who-we-are/our-team/", "https://en.wikipedia.org/wiki/Ma_Huateng", "https://en.wikipedia.org/wiki/Chen_Yidan" ], "ownership": { "status": "public", "exchange": "Hong Kong Stock Exchange", "ticker": "0700", "checked": "2026-10-28", "sources": [ "https://www.tencent.com/investors/", "https://huggingface.co/tencent/Hy3" ] } }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hunyuanocr.json" } }