{ "data": { "a": { "slug": "apertus-v1-5-8b", "name": "Apertus v1.5 8B", "vendor": "Swiss AI Initiative (EPFL and ETH Zurich)", "vendorUrl": "https://www.swiss-ai.org", "kind": "weights", "category": "open-weights", "summary": "Apertus v1.5 8B is the Swiss AI Initiative's open-data, open-weight multilingual model of 8.9B parameters that takes text, images and audio and answers in text, with optional thinking and a 262,144-token context. The download is gated behind a click-through.", "url": "https://www.anchorterminal.com/tools/apertus-v1-5-8b", "markdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json", "repo": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B", "license": "Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate", "transports": [], "packages": [ { "registry": "huggingface", "name": "swiss-ai/Apertus-v1.5-8B" } ], "auth": "none", "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account, accepting Apache 2.0, the Apertus Acceptable Use Policy and a privacy policy, and sharing your contact details, company and institutional email with the authors; Hugging Face approves the request automatically. The card, the file list, the chat template and the gate's text are public. We didn't accept the terms (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 plus the Acceptable Use Policy, which adds an indemnity for ETH Zurich and EPFL and requires anyone who redistributes the model to pass the policy on with an express act of assent. Running it costs the hardware, 18.4 GB of files in BF16 with the image and audio tokenizers in F32 (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/swiss-ai/Apertus-v1.5-8B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "multilingual", "open-data", "audio-input", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gated", "self-hosted" ], "lastRelease": "2026-10-14", "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": 1197, "categoryRank": 28, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 40, "risk": 75, "upkeep": 84 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.", "bestFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.", "strengths": [ "Open training data, with every dataset listed by version and licence and the post-training data published", "Safety benchmarks reported for the released 8B, plus outside evaluators given early access", "Tool format documented by the publisher, with a parser named and several calls a turn", "Thinking that turns on per request, and 262,144 tokens of context", "Safetensors only, with a checksum on every file", "Swiss AI answered every question in the repository's discussions" ], "weaknesses": [ "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers", "No quantised build from Swiss AI, and the report warns 4-bit NVFP4 in attention gives NaN logits", "Gated download that collects contact details, under a use policy you must pass on when you redistribute", "Tool calling and thinking don't combine, and parallel calls are weaker on the 8B", "No structured output documentation and no knowledge cut-off" ], "agentNotes": [ "Accept the terms on the card and set HF_TOKEN before downloading", "Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet", "Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools", "For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously", "Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames", "Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32", "If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "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": 85, "reach": 40, "risk": 75, "upkeep": 84 }, "provenanceScore": 49, "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": "# accept the terms on the Hugging Face card first, then set HF_TOKEN\ndocker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64\nvllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string \\\n --gpu-memory-utilization 0.6 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser apertus", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"swiss-ai/Apertus-v1.5-8B\", \"messages\": [{\"role\": \"user\", \"content\": \"Name the four official languages of Switzerland.\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/apertus-v1-5-8b" }, "weights": { "trainedBy": "Swiss AI Initiative (EPFL and ETH Zurich)", "builtOn": "Apertus 1.0 8B (continued pretraining of Swiss AI's own model, per the card)", "params": "8.9B", "context": 262144, "goodFor": "Fully open multilingual model for text, images and audio in with text out, instruction following, tool calls and optional thinking, for teams that want open data and a European publisher.", "runtimes": [], "builds": [ { "name": "Apertus-v1.5-8B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B", "who": "publisher", "precision": "bf16", "sizeGB": 18.4, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (Swiss AI fork)", "Transformers (Swiss AI fork)" ], "note": "Gated (click-through with contact details). Size is the Hub's file list summed, 17.2 GB of BF16 language-model shards plus 1.2 GB of F32 vision and audio tokenizers. No quantised build from Swiss AI" } ], "run": "docker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64 \u0026\u0026 vllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string --gpu-memory-utilization 0.6 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser apertus", "runBuild": "Apertus-v1.5-8B", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Swiss AI Initiative (EPFL, ETH Zurich, CSCS)", "family": "Apertus (v1 2509, v1.1, v1.5)", "url": "https://www.anchorterminal.com/open-weights/#swiss-ai", "others": [ { "name": "Apertus-v1.5-8B-base", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-base", "params": "8B (Hub safetensors count 8.84B)" }, { "name": "Apertus-v1.5-70B-base", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-base", "params": "70B (Hub safetensors count 71.94B)" }, { "name": "Apertus-v1.5-8B-SFT", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-SFT", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-8B-RL", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-RL", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-8B-DPO", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-DPO", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-70B-SFT", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-SFT", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.5-70B-RL", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-RL", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.5-70B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.1-1.5B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B-Instruct", "params": "1.5B (Hub safetensors count 1.51B)" }, { "name": "Apertus-v1.1-4B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B-Instruct", "params": "4B (Hub safetensors count 3.83B)" }, { "name": "Apertus-v1.1-0.5B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B-Instruct", "params": "0.5B (Hub safetensors count 573M)" }, { "name": "Apertus-v1.1-4B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B", "params": "4B (Hub safetensors count 3.83B)" }, { "name": "Apertus-v1.1-0.5B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B", "params": "0.5B (Hub safetensors count 438M)" }, { "name": "Apertus-v1.1-1.5B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B", "params": "1.5B (Hub safetensors count 1.51B)" }, { "name": "Apertus-8B-2509", "url": "https://huggingface.co/swiss-ai/Apertus-8B-2509", "params": "8B (Hub safetensors count 8.05B)" }, { "name": "Apertus-70B-2509", "url": "https://huggingface.co/swiss-ai/Apertus-70B-2509", "params": "70B (Hub safetensors count 70.60B)" }, { "name": "Apertus-70B-Instruct-2509", "url": "https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509", "params": "70B (Hub safetensors count 70.60B)" }, { "name": "Apertus-8B-Instruct-2509", "url": "https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509", "params": "8B (Hub safetensors count 8.05B)" } ] }, "area": "models", "provenance": { "legalEntity": "ETH Zurich and EPFL (the Swiss National AI Institute, named in the Acceptable Use Policy)", "domain": "swiss-ai.org", "domainRegistered": "2023-08-10", "endpointOnVendorDomain": null, "terms": "https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/USAGE_POLICY.pdf", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Acceptable Use Policy names the Swiss National AI Institute, a partnership of ETH Zurich and EPFL. RDAP gives swiss-ai.org a registration date of 10 August 2023 and apertus-ai.org 5 August 2025.", "Nothing is hosted for this listing, so the licence and the Acceptable Use Policy stand in for terms of service and no privacy policy is scored. A separate privacy policy covers the contact details the gate collects.", "The Hugging Face commit history is behind the gate. The repository was last modified on 14 October 2026 per the Hub.", "www.swiss-ai.org answers 400 for /.well-known/security.txt and apertus-ai.org didn't answer through our proxy on 21 October 2026. ETH Zurich's security.txt and disclosure policy cover ETH's IT infrastructure." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.json" }, "answer": "Hy3 scores 62.6 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk, agent interface and documentation and upkeep.", "b": { "slug": "hy3", "name": "Hy3", "vendor": "Tencent (Hy team)", "vendorUrl": "https://aistudio.tencent.com", "kind": "weights", "category": "open-weights", "summary": "Hy3 is Tencent's open-weight mixture-of-experts language model, 295B parameters with 21B active, a 256K-token context and per-request reasoning effort, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/hy3", "markdownUrl": "https://www.anchorterminal.com/tools/hy3.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/hy3.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/hy3.json", "repo": "https://huggingface.co/tencent/Hy3", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "tencent/Hy3" }, { "registry": "huggingface", "name": "tencent/Hy3-FP8" }, { "registry": "huggingface", "name": "AngelSlim/Hy3-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither tencent/Hy3 nor tencent/Hy3-FP8 is gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 597.6 GB for the BF16 repository, 299.9 GB for FP8 and 89.4 GB for the smallest GGUF from Tencent's AngelSlim team (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/tencent/Hy3", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "gguf", "self-hosted" ], "lastRelease": "2026-07-20", "graded": true, "anchor": { "graded": true, "score": 62.6, "grade": "B", "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": 1197, "categoryRank": 22, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 57, "interface": 75, "reach": 70, "risk": 60, "upkeep": 56 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.", "bestFor": "Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0", "strengths": [ "Plain Apache 2.0, downloadable with no account, safetensors only with a SHA-256 for every shard", "Model code in released Transformers, vLLM, SGLang and llama.cpp, with no remote code to run", "Several tool calls in one turn, and reasoning effort of no_think, low or high per request", "262,144 tokens of native context", "FP8 from Tencent and GPTQ-Int4 and GGUF builds from its AngelSlim team, and two live inference providers" ], "weaknesses": [ "597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build", "No safety evaluation and no prompt-injection or refusal guidance", "The card leaves out limitations, languages, knowledge cut-off and training data, and doesn't describe the tool-call format", "No change to the files since 20 July 2026, and no Tencent reply in the discussions since 22 July", "No Ollama library entry and nothing in LM Studio's hub" ], "agentNotes": [ "Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3", "In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows", "Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error", "With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content", "Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe", "Use temperature 0.9 and top_p 1.0, the card's recommended sampling", "On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers", "Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 62.6 } ], "editorialScores": { "capability": 57, "interface": 75, "reach": 70, "risk": 60, "upkeep": 56 }, "provenanceScore": 60, "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", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# Tencent's command from the card (8 GPUs, H20-3e or larger)\nvllm serve tencent/Hy3 --tensor-parallel-size 8 \\\n --tool-call-parser hy_v3 --reasoning-parser hy_v3 \\\n --enable-auto-tool-choice --served-model-name hy3\n# or AngelSlim's IQ1_M GGUF (89.4 GB) with llama.cpp:\nllama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"hy3\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 0.9, \"top_p\": 1.0, \"chat_template_kwargs\": {\"reasoning_effort\": \"high\"}}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/hy3" }, "sameCompany": [ "hy-memory" ], "weights": { "trainedBy": "Tencent (Hy team)", "params": "295B", "active": "21B", "context": 262144, "goodFor": "Mixture-of-experts language model with tool calling and per-request reasoning effort (off by default), text only.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Hy3-Q2_K_XL", "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q2_K_XL.gguf", "who": "AngelSlim", "precision": "q2_k_xl", "sizeGB": 100.1, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer" }, { "name": "Hy3-IQ1_M", "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-IQ1_M.gguf", "who": "AngelSlim", "precision": "iq1_m", "sizeGB": 89.4, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer" }, { "name": "Hy3-Q3_K_M", "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q3_K_M.gguf", "who": "AngelSlim", "precision": "q3_k_m", "sizeGB": 134.6, "memoryGB": 192, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer" }, { "name": "Hy3-Q4_K_M", "url": "https://huggingface.co/AngelSlim/Hy3-GGUF/blob/main/Hy3-Q4_K_M.gguf", "who": "AngelSlim", "precision": "q4_k_m", "sizeGB": 182.3, "memoryGB": 256, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "From Tencent's AngelSlim compression project; a -mtp variant of each file adds the MTP layer" }, { "name": "Hy3-GPTQ-Int4", "url": "https://huggingface.co/AngelSlim/Hy3-GPTQ-Int4", "who": "AngelSlim", "precision": "gptq-int4", "sizeGB": 168.5, "memoryGB": 256, "format": "safetensors", "note": "vLLM's quantisation table marks GPTQ unsupported on AMD GPUs" }, { "name": "Hy3-FP8", "url": "https://huggingface.co/tencent/Hy3-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 299.9, "memoryGB": 512, "format": "safetensors" }, { "name": "Hy3", "url": "https://huggingface.co/tencent/Hy3", "who": "publisher", "precision": "bf16", "sizeGB": 597.6, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3", "runBuild": "Hy3-FP8", "checked": "2026-10-21", "minMemoryGB": 128 }, "family": { "lab": "Tencent (Hunyuan / Hy)", "family": "Hy3", "url": "https://www.anchorterminal.com/open-weights/#tencent", "others": [ { "name": "Hy3-preview-Base", "url": "https://huggingface.co/tencent/Hy3-preview-Base", "params": "295B total, 21B activated (+3.8B MTP)" }, { "name": "Hy3-preview", "url": "https://huggingface.co/tencent/Hy3-preview", "params": "295B total, 21B activated (+3.8B MTP)" } ], "olderCount": 22, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Tencent (named in the LICENSE file's copyright line)", "domain": "tencent.com", "domainRegistered": "1998-09-14", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The LICENSE file reads Copyright (C) 2026 Tencent above the Apache 2.0 text. No fuller legal name was found.", "The card's official website is aistudio.tencent.com (Hy AI Studio). RDAP at Verisign gives tencent.com a registration date of 14 September 1998.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The Hugging Face commit messages are mostly uploads; the last change, on 20 July 2026, added a dtype line to config.json.", "security.txt answered 404 on www.tencent.com, and aistudio.tencent.com answers /.well-known/security.txt with its web app, so there is none." ], "score": 60 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/hy3.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Swiss AI Initiative (EPFL and ETH Zurich)", "b": "Tencent (Hy team)", "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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-14", "b": "2026-07-20", "name": "Last release" }, { "a": "", "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": "2.8/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Hy3 scores 62.6 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk, agent interface and documentation and upkeep.", "question": "Which is better for AI agents, Apertus v1.5 8B or Hy3?" } ], "goodFor": [ { "aheadOn": [ "Risk, 75 against 60", "Agent interface, 85 against 75", "Documentation and upkeep, 84 against 56" ], "also": null, "goodFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.", "slug": "apertus-v1-5-8b", "watchFor": "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers" }, { "aheadOn": [ "Reach, 70 against 40" ], "also": null, "goodFor": "Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0", "slug": "hy3", "watchFor": "597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.json", "title": "Agents-A1-4B vs Apertus v1.5 8B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.json", "title": "Agents-A1-4B vs Hy3", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.json", "title": "Apertus v1.5 8B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.json", "title": "Apertus v1.5 8B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.json", "title": "Apertus v1.5 8B vs Cosmos-Reason2-2B", "url": 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"https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-27b.json", "title": "Hy3 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-flash-next.json", "title": "Hy3 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Hy3 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-next.json", "title": "Hy3 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Hy3 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-2b-instruct.json", "title": "Hy3 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Hy3 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-32b-instruct.json", "title": "Hy3 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-4b-instruct.json", "title": "Hy3 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-8b-instruct.json", "title": "Hy3 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-qwopus3-8-27b-flash-v2.json", "title": "Hy3 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/hy3-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-smollm3-3b.json", "title": "Hy3 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-spark-x2-6-4b.json", "title": "Hy3 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/hy3-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.json", "title": "Hy3 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Hy3 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-thinkingcap-qwen3-8-27b.json", "title": "Hy3 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/hy3-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apertus-v1-5-8b": 40, "by": 30, "edge": "hy3", "hy3": 70, "key": "reach", "name": "Reach", "weight": 25 }, { "apertus-v1-5-8b": 75, "by": 15, "edge": "apertus-v1-5-8b", "hy3": 60, "key": "risk", "name": "Risk", "weight": 20 }, { "apertus-v1-5-8b": 85, "by": 10, "edge": "apertus-v1-5-8b", "hy3": 75, "key": "interface", "name": "Agent interface", "weight": 10 }, { "apertus-v1-5-8b": 84, "by": 28, "edge": "apertus-v1-5-8b", "hy3": 56, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Hy3 scores 62.6 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "apertus-v1-5-8b": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.", "hy3": "Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3", "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.min.md" }, "markdown": "Hy3 scores 62.6 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk, agent interface and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Apertus v1.5 8B: grade C, 61/100, rank #35 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json\n- Hy3: grade B, 62.6/100, rank #27 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/hy3.md · JSON https://www.anchorterminal.com/api/v1/tools/hy3.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 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Apertus v1.5 8B (C)\n\nGood for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.\n\nAhead on:\n- Risk, 75 against 60\n- Agent interface, 85 against 75\n- Documentation and upkeep, 84 against 56\n\nWatch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers\n\n### Hy3 (B)\n\nGood for: Teams with an 8-GPU server who want an agent-tuned MoE model under plain Apache 2.0\n\nAhead on:\n- Reach, 70 against 40\n\nWatch for: 597.6 GB in BF16, 299.9 GB in FP8 and 168.5 GB at the smallest 4-bit build\n\n\n## Score by category\n\n| Category | Weight | Apertus v1.5 8B | Hy3 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 70 | Hy3 +30 |\n| Risk | 20% (20 this run) | 75 | 60 | Apertus v1.5 8B +15 |\n| Agent interface | 10% (10 this run) | 85 | 75 | Apertus v1.5 8B +10 |\n| Documentation and upkeep | 10% (10 this run) | 84 | 56 | Apertus v1.5 8B +28 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **62.6 · B** | |\n\n## Facts side by side\n\n| Fact | Apertus v1.5 8B | Hy3 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Tencent (Hy team) |\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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2026-07-20 |\n| Terms last updated | | 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) | 2.8/5 (4) |\n\n## Verdicts\n\n**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.\n\n**Hy3.** Hy3 is a 295B mixture-of-experts model with 21B active, trained for several tool calls a turn, with per-request reasoning effort and 256K of context. It needs an 8-GPU server for FP8 or about 200 GB for a 4-bit build. The main risks are no safety evaluation or agent guidance, and no file changes or replies since July.\n\n## Before you call either\n\n### Apertus v1.5 8B\n\n1. Accept the terms on the card and set HF_TOKEN before downloading\n2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet\n3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools\n4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously\n5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames\n6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32\n7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent\n\n### Hy3\n\n1. Start it with vLLM on 8 GPUs, vllm serve tencent/Hy3-FP8 --tensor-parallel-size 8 --tool-call-parser hy_v3 --reasoning-parser hy_v3 --enable-auto-tool-choice --served-model-name hy3\n2. In SGLang use --tool-call-parser hunyuan --reasoning-parser hunyuan, as the card shows\n3. Pass reasoning_effort (no_think, low or high) in chat_template_kwargs; the default is no_think, and any other value raises a template error\n4. With the vLLM reasoning parser the chain of thought comes back in message.reasoning and the answer in message.content\n5. Earlier thinking stays in the prompt when tools are present; the template keys this on preserved_thinking, not the interleaved_thinking named in vLLM's recipe\n6. Use temperature 0.9 and top_p 1.0, the card's recommended sampling\n7. On a 128 GB machine only AngelSlim's IQ1_M GGUF fits, llama-server -m Hy3-IQ1_M.gguf -ctk q8_0 -ctv q8_0 -fa on -c 65536, with llama.cpp b11768 or later; at 89.4 GB × 1.2 it sits right at the 107.4 GB ceiling, so shorten the context if it fails to load, and expect a 1-bit build to fall short of Tencent's numbers\n8. Without the hardware, call it through Featherless AI or DeepInfra via Hugging Face's inference providers\n\n## Questions\n\n### Which is better for AI agents, Apertus v1.5 8B or Hy3?\n\nHy3 scores 62.6 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk, agent interface and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apertus-v1-5-8b\", \"b\": \"hy3\"}`. From a terminal: `anchor compare apertus-v1-5-8b hy3`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/hy3.json\n\n## Other comparisons with Apertus v1.5 8B or Hy3\n\n- [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md)\n- [Agents-A1-4B vs Hy3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.md)\n- [Apertus v1.5 8B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.md)\n- [Apertus v1.5 8B vs BigBang-v1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.md)\n- [Apertus v1.5 8B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.md)\n- [Apertus v1.5 8B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.md)\n- [Apertus v1.5 8B vs 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27B)](https://www.anchorterminal.com/compare/hy3-vs-thinkingcap-qwen3-8-27b.md)\n", "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": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "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" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Apertus v1.5 8B vs Hy3", "url": "" } ], "description": "Hy3 scores 62.6 (B) to Apertus v1.5 8B's 61 (C) for open-weight 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