{ "data": { "a": { "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" }, "answer": "Hy3 scores 62.6 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach.", "b": { "slug": "lfm2-5-2-6b", "name": "LFM2.5-2.6B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM2.5-2.6B is Liquid AI's open-weight 2.6B hybrid model of convolution and attention layers for on-device agents, with tool calling, thinking always on and a 131,072-token context, published on 4 August 2026 with Liquid's own GGUF, MLX and ONNX builds.", "url": "https://www.anchorterminal.com/tools/lfm2-5-2-6b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json", "repo": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM2.5-2.6B-MLX" }, { "registry": "ollama", "name": "LiquidAI/lfm2.5-2.6b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository and Liquid's GGUF, MLX and ONNX repositories are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use under the LFM Open License v1.0, commercial use included for organisations with under $10M in annual revenue; above that, commercial use isn't licensed. Running it yourself costs little hardware, 1.6 GB for Liquid's Q4_0 GGUF and 5.4 GB in BF16 (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://docs.liquid.ai/lfm/models/lfm25-2.6b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "edge", "reasoning", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-09-22", "graded": true, "anchor": { "graded": true, "score": 51.5, "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": 1197, "categoryRank": 71, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.", "bestFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "strengths": [ "Liquid's own 4-bit GGUF and MLX builds are 1.6 GB, under 2 GB of memory with the fifth added", "Runs in released llama.cpp, Ollama, vLLM, SGLang and Transformers, with an lfm2 tool parser in vLLM and SGLang", "Tool-call format documented by Liquid, several calls per turn, and 131,072 tokens of trained context", "Safetensors and GGUF only, no repository code, a checksum on every file and no gate", "Builds at many precisions from Liquid itself, refreshed in September 2026" ], "weaknesses": [ "Commercial use isn't licensed for organisations with $10M or more of annual revenue", "Thinking is always on, with no way to turn it off or budget it", "No safety evaluation, agent guidance or security contact", "No structured-output documentation and no knowledge cut-off on the card", "Liquid answered 2 of 16 discussions" ], "agentNotes": [ "Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`", "With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately", "Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set", "Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead", "Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought", "Earlier thinking is dropped from the prompt unless `preserve_thinking` is true", "Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "D", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 51.5 } ], "editorialScores": { "capability": 13, "interface": 75, "reach": 95, "risk": 50, "upkeep": 57 }, "provenanceScore": 55, "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": "llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF --conversation --temp 0.1 --top-k 50 --repeat-penalty 1.1\n# or\nollama run LiquidAI/lfm2.5-2.6b\n# or, with vLLM:\nvllm serve LiquidAI/LFM2.5-2.6B --enable-auto-tool-choice --tool-call-parser lfm2 --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"LiquidAI/lfm2.5-2.6b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm2-5-2-6b" }, "sameCompany": [ "liquid-d1" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "LiquidAI/LFM2.5-2.6B-Base", "params": "2.69B", "context": 131072, "goodFor": "Small on-device agent model for tool use, data extraction, RAG and long-context work on a laptop or phone, always thinking, not for agentic coding or knowledge-heavy tasks.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "LFM2.5-2.6B", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B", "who": "publisher", "precision": "bf16", "sizeGB": 5.4, "memoryGB": 8, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] }, { "name": "LFM2.5-2.6B-GGUF Q4_0", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-GGUF", "who": "publisher", "precision": "q4_0", "sizeGB": 1.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp", "LM Studio" ], "note": "LFM2.5-2.6B-Q4_0.gguf, 1.59 GB from the Hub's file list. The repository also holds Q4_K_M (1.67 GB), Q5_K_M, Q6_K, Q8_0 (2.87 GB), F16 and BF16, and a quantisation-aware LFM2.5-2.6B-QAD-Q4_0.gguf added on 19 August 2026" }, { "name": "LFM2.5-2.6B-MLX 4bit", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-MLX", "who": "publisher", "precision": "4bit", "sizeGB": 1.6, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX" ], "note": "4bit/model.safetensors (1.58 GB) and tokenizer, 1.60 GB summed. The repository also holds 5, 6 and 8-bit, BF16, MXFP4, MXFP8 and NVFP4 folders, each also published as its own LFM2.5-2.6B-MLX-* repository" }, { "name": "ollama LiquidAI/lfm2.5-2.6b:q4_k_m", "url": "https://ollama.com/LiquidAI/lfm2.5-2.6b", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.7, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Liquid's own namespace in Ollama's registry, latest and q4_k_m share digest 83f596831da1; q4_0 is 1.6 GB. Sizes as Ollama's tags page shows them" } ], "run": "llama-server -hf LiquidAI/LFM2.5-2.6B-GGUF:Q4_0 --jinja -c 131072 --temp 0.1 --top-k 50 --repeat-penalty 1.1", "runBuild": "LFM2.5-2.6B-GGUF Q4_0", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM2 (LFM2 and LFM2.5)", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM2.5-24B-A2B", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-24B-A2B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-24B-A2B-Base", "params": "24B total, 2B active (MoE) (Hub safetensors count 23.97B)" }, { "name": "LFM2.5-VL-3B", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-3B", "params": "3B (Hub safetensors count 3.12B)" }, { "name": "LFM2.5-2.6B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-2.6B-Base", "params": "2.6B (Hub safetensors count 2.70B)" }, { "name": "LFM2.5-Encoder-350M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-Prompt-Router", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Prompt-Router", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Policy-Linter", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Policy-Linter", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-Encoder-350M-Diffusion", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Diffusion", "params": "350M (Hub safetensors count 354M)" }, { "name": "LFM2.5-Encoder-350M-Spellchecker", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-Spellchecker", "params": "350M (Hub safetensors count 357M)" }, { "name": "LFM2.5-230M", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-230M-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-230M-Base", "params": "230M (Hub safetensors count 230M)" }, { "name": "LFM2.5-Encoder-350M-PII-Detector", "url": "https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M-PII-Detector", "params": "350M (Hub safetensors count 355M)" }, { "name": "LFM2.5-8B-A1B-Base", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B-Base", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-8B-A1B", "url": "https://huggingface.co/LiquidAI/LFM2.5-8B-A1B", "params": "8B total, 1B active (MoE) (Hub safetensors count 8.47B)" }, { "name": "LFM2.5-VL-450M-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-450M-Extract", "params": "450M (Hub safetensors count 449M)" }, { "name": "LFM2.5-VL-1.6B-Extract", "url": "https://huggingface.co/LiquidAI/LFM2.5-VL-1.6B-Extract", "params": "1.6B (Hub 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(the licensor named in the LFM Open License)", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The licence names Liquid AI, Inc. as licensor. The domain's registration date is as recorded on the liquid-d1 listing on 8 October 2026.", "Nothing is hosted for this listing, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commits are README edits, a config fix and the DSpark link; Liquid's deprecations page records which earlier models this one replaces, without dates.", "/.well-known/security.txt answered 404 on www.liquid.ai on 21 October 2026, and Liquid4All/.github has no SECURITY.md.", "Liquid's site and docs link to huggingface.co/LiquidAI, so the repositories are the publisher's own." ], "score": 55 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm2-5-2-6b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Tencent (Hy team)", "b": "Liquid AI", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "yes", "name": "llms.txt" }, { "a": "2026-07-20", "b": "2026-09-22", "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": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Hy3 scores 62.6 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach.", "question": "Which is better for AI agents, Hy3 or LFM2.5-2.6B?" } ], "goodFor": [ { "aheadOn": [ "Capability, 57 against 13", "Risk, 60 against 50" ], "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" }, { "aheadOn": [ "Reach, 95 against 70" ], "also": null, "goodFor": "Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold", "slug": "lfm2-5-2-6b", "watchFor": "Commercial use isn't licensed for organisations with $10M or more of annual revenue" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "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/agents-a1-4b-vs-lfm2-5-2-6b.json", "title": "Agents-A1-4B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.json", "title": "Apertus v1.5 8B vs Hy3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.json", "title": "Apertus v1.5 8B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3.json", "title": "Apodex-1.1-mini vs Hy3", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.json", "title": "Apodex-1.1-mini vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3.json", "title": "BigBang-v1 vs Hy3", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.json", "title": "BigBang-v1 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy3.json", "title": "Cosmos-Reason2-2B vs Hy3", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.json", "title": "Cosmos-Reason2-2B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy3.json", "title": "Darwin-36B-Opus vs Hy3", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.json", "title": "Darwin-36B-Opus vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3.json", "title": "DeepSeek-V3.2 vs Hy3", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.json", "title": "DeepSeek-V3.2 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3.json", "title": "DeepSeek V4.1 Flash vs Hy3", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.json", "title": "DeepSeek V4.1 Flash vs 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"https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy3.json", "title": "Gemma 4 12B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 12B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3.json", "title": "Gemma 4 26B A4B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 26B A4B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.json", "title": "Gemma 4 31B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 31B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3.json", "title": "Gemma 4 E2B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 E2B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3.json", "title": "Gemma 4 E4B IT vs Hy3", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.json", "title": "Gemma 4 E4B IT vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3.json", "title": "GLM-4.5-Air vs Hy3", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.json", "title": "GLM-4.5-Air vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3.json", "title": "GLM-4.7-Flash vs Hy3", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.json", "title": "GLM-4.7-Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3.json", "title": "GLM-5.3-Flash vs Hy3", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.json", "title": "GLM-5.3-Flash vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-hy3.json", "title": "GLM-5.3 vs Hy3", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.json", "title": "GLM-5.3 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy3.json", "title": "gpt-oss-120b vs Hy3", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.json", "title": "gpt-oss-120b vs LFM2.5-2.6B", "url": 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{ "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.json", "title": "Hy3 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1.json", "title": "Hy3 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.json", "title": "Hy3 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Hy3 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5.json", "title": "Hy3 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5" }, { "json": 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"https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b.json", "title": "Hy3 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b.json", "title": "Hy3 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Hy3 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-super-120b-a12b.json", "title": "Hy3 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b.json", "title": "Hy3 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.json", "title": "Hy3 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.json", "title": "Hy3 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b.json", "title": "Hy3 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.json", "title": "Hy3 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.json", "title": "Hy3 vs Ornith-1.5-9B", "url": 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"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" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.json", "title": "Inkling-Small vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.json", "title": "Inkling vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.json", "title": "InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B", "url": 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"https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.json", "title": "LFM2.5-2.6B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.json", "title": "LFM2.5-2.6B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.json", "title": "LFM2.5-2.6B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b.json", "title": "LFM2.5-2.6B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.json", "title": "LFM2.5-2.6B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.json", "title": "LFM2.5-2.6B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.json", "title": "LFM2.5-2.6B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507.json", "title": "LFM2.5-2.6B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b.json", "title": "LFM2.5-2.6B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b.json", "title": "LFM2.5-2.6B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b.json", "title": "LFM2.5-2.6B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b.json", "title": "LFM2.5-2.6B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b.json", "title": "LFM2.5-2.6B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b.json", "title": "LFM2.5-2.6B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b.json", "title": "LFM2.5-2.6B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b.json", "title": "LFM2.5-2.6B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next.json", "title": "LFM2.5-2.6B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next.json", "title": "LFM2.5-2.6B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct.json", "title": "LFM2.5-2.6B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM2.5-2.6B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b.json", "title": "LFM2.5-2.6B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b.json", "title": "LFM2.5-2.6B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM2.5-2.6B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "by": 44, "edge": "hy3", "hy3": 57, "key": "capability", "lfm2-5-2-6b": 13, "name": "Capability", "weight": 35 }, { "by": 25, "edge": "lfm2-5-2-6b", "hy3": 70, "key": "reach", "lfm2-5-2-6b": 95, "name": "Reach", "weight": 25 }, { "by": 10, "edge": "hy3", "hy3": 60, "key": "risk", "lfm2-5-2-6b": 50, "name": "Risk", "weight": 20 }, { "by": 0, "edge": "", "hy3": 75, "key": "interface", "lfm2-5-2-6b": 75, "name": "Agent interface", "weight": 10 }, { "by": 1, "edge": "lfm2-5-2-6b", "hy3": 56, "key": "upkeep", "lfm2-5-2-6b": 57, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Hy3 scores 62.6 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "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.", "lfm2-5-2-6b": "A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b", "json": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.min.md" }, "markdown": "Hy3 scores 62.6 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach. Both do open-weight models you run yourself.\n\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- LFM2.5-2.6B: grade D, 51.5/100, rank #89 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm2-5-2-6b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.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### 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- Capability, 57 against 13\n- Risk, 60 against 50\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### LFM2.5-2.6B (D)\n\nGood for: Agents that want a small local helper for tool calls, extraction and RAG on a laptop, phone or CPU, at organisations under the licence's revenue threshold\n\nAhead on:\n- Reach, 95 against 70\n\nWatch for: Commercial use isn't licensed for organisations with $10M or more of annual revenue\n\n\n## Score by category\n\n| Category | Weight | Hy3 | LFM2.5-2.6B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35% (35 this run) | 57 | 13 | Hy3 +44 |\n| Reach | 25% (25 this run) | 70 | 95 | LFM2.5-2.6B +25 |\n| Risk | 20% (20 this run) | 60 | 50 | Hy3 +10 |\n| Agent interface | 10% (10 this run) | 75 | 75 | even |\n| Documentation and upkeep | 10% (10 this run) | 56 | 57 | LFM2.5-2.6B +1 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **62.6 · B** | **51.5 · D** | |\n\n## Facts side by side\n\n| Fact | Hy3 | LFM2.5-2.6B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Tencent (Hy team) | Liquid AI |\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 | LFM Open License v1.0 (free commercial use below $10M annual revenue) |\n| Read-only variant documented | no | no |\n| llms.txt | no | yes |\n| Last release | 2026-07-20 | 2026-09-22 |\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) | 2.5/5 (4) |\n\n## Verdicts\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**LFM2.5-2.6B.** A small, fast cousin for tool calls, extraction and long-context steps on a laptop or phone. Liquid's own 4-bit build is 1.6 GB, and every common runtime runs it. It always thinks, isn't meant for coding or knowledge-heavy work, and organisations over $10M of revenue need another licence. There are no safety results or security contact.\n\n## Before you call either\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### LFM2.5-2.6B\n\n1. Quickest start is `ollama run LiquidAI/lfm2.5-2.6b`, 1.7 GB, or `llama-cli -hf LiquidAI/LFM2.5-2.6B-GGUF`\n2. With vLLM or SGLang pass `--tool-call-parser lfm2`; for vLLM add `--reasoning-parser qwen3` so the thinking comes back separately\n3. Sample at temperature 0.1, top_k 50 and repetition_penalty 1.1, as the card and generation_config.json set\n4. Put tools in the system prompt as `List of tools: [...]` JSON, or pass tools to apply_chat_template; calls come back as a Python list of function calls, and the system prompt can ask for JSON calls instead\n5. Leave max_tokens unset or generous, since a low cap cuts the reasoning off mid-thought\n6. Earlier thinking is dropped from the prompt unless `preserve_thinking` is true\n7. Liquid's 328M LFM2.5-2.6B-DSpark drafter speeds decoding about 2.6 times in SGLang and on Apple silicon, per Liquid\n8. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Hy3 or LFM2.5-2.6B?\n\nHy3 scores 62.6 (B) on agent readiness against LFM2.5-2.6B's 51.5 (D), and leads in 2 of 5 scored categories. LFM2.5-2.6B leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/hy3-vs-lfm2-5-2-6b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"hy3\", \"b\": \"lfm2-5-2-6b\"}`. From a terminal: `anchor compare hy3 lfm2-5-2-6b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/hy3.json and https://www.anchorterminal.com/api/v1/tools/lfm2-5-2-6b.json\n\n## Other comparisons with Hy3 or LFM2.5-2.6B\n\n- [Agents-A1-4B vs Hy3](https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.md)\n- [Agents-A1-4B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.md)\n- [Apertus v1.5 8B vs Hy3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.md)\n- [Apertus v1.5 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.md)\n- [Apodex-1.1-mini vs Hy3](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-hy3.md)\n- [Apodex-1.1-mini vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm2-5-2-6b.md)\n- [BigBang-v1 vs Hy3](https://www.anchorterminal.com/compare/bigbang-v1-vs-hy3.md)\n- [BigBang-v1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm2-5-2-6b.md)\n- [Cosmos-Reason2-2B vs Hy3](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-hy3.md)\n- [Cosmos-Reason2-2B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm2-5-2-6b.md)\n- [Darwin-36B-Opus vs Hy3](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-hy3.md)\n- [Darwin-36B-Opus vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm2-5-2-6b.md)\n- [DeepSeek-V3.2 vs Hy3](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-hy3.md)\n- [DeepSeek-V3.2 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm2-5-2-6b.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 LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm2-5-2-6b.md)\n- [DeepSeek-V4-Flash-0731 vs Hy3](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-hy3.md)\n- [DeepSeek-V4-Flash-0731 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm2-5-2-6b.md)\n- [DiffusionGemma 26B A4B IT vs Hy3](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-hy3.md)\n- [DiffusionGemma 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 12B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-hy3.md)\n- [Gemma 4 12B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 26B A4B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-hy3.md)\n- [Gemma 4 26B A4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 31B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-hy3.md)\n- [Gemma 4 31B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E2B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-hy3.md)\n- [Gemma 4 E2B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-lfm2-5-2-6b.md)\n- [Gemma 4 E4B IT vs Hy3](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-hy3.md)\n- [Gemma 4 E4B IT vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-lfm2-5-2-6b.md)\n- [GLM-4.5-Air vs Hy3](https://www.anchorterminal.com/compare/glm-4-5-air-vs-hy3.md)\n- [GLM-4.5-Air vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-5-air-vs-lfm2-5-2-6b.md)\n- [GLM-4.7-Flash vs Hy3](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-hy3.md)\n- [GLM-4.7-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3-Flash vs Hy3](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-hy3.md)\n- [GLM-5.3-Flash vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-lfm2-5-2-6b.md)\n- [GLM-5.3 vs Hy3](https://www.anchorterminal.com/compare/glm-5-3-vs-hy3.md)\n- [GLM-5.3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/glm-5-3-vs-lfm2-5-2-6b.md)\n- [gpt-oss-120b vs Hy3](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-hy3.md)\n- [gpt-oss-120b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm2-5-2-6b.md)\n- [gpt-oss-20b vs Hy3](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-hy3.md)\n- [gpt-oss-20b vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-lfm2-5-2-6b.md)\n- [Granite 4.2 3B vs Hy3](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-hy3.md)\n- [Granite 4.2 3B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-lfm2-5-2-6b.md)\n- [Hy3 vs Inkling](https://www.anchorterminal.com/compare/hy3-vs-inkling.md)\n- [Hy3 vs Inkling-Small](https://www.anchorterminal.com/compare/hy3-vs-inkling-small.md)\n- [Hy3 vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/hy3-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Hy3 vs Kimi K3](https://www.anchorterminal.com/compare/hy3-vs-kimi-k3.md)\n- [Hy3 vs Kolibri-1](https://www.anchorterminal.com/compare/hy3-vs-kolibri-1.md)\n- [Hy3 vs Laguna M.1](https://www.anchorterminal.com/compare/hy3-vs-laguna-m-1.md)\n- [Hy3 vs Laguna S 2.1](https://www.anchorterminal.com/compare/hy3-vs-laguna-s-2-1.md)\n- [Hy3 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/hy3-vs-laguna-xs-2-1.md)\n- [Hy3 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/hy3-vs-ling-3-0-tiny.md)\n- [Hy3 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/hy3-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Hy3 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/hy3-vs-minicpm-o-4-5.md)\n- [Hy3 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/hy3-vs-minicpm5-2b.md)\n- [Hy3 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-5.md)\n- [Hy3 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/hy3-vs-minimax-m2-7.md)\n- [Hy3 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/hy3-vs-muse-glimmer-30b.md)\n- [Hy3 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Hy3 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-30b-a3b.md)\n- [Hy3 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-4b.md)\n- [Hy3 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Hy3 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-super-120b-a12b.md)\n- [Hy3 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/hy3-vs-nemotron-3-ultra-550b-a55b.md)\n- [Hy3 vs occamy-1.0](https://www.anchorterminal.com/compare/hy3-vs-occamy-1-0.md)\n- [Hy3 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/hy3-vs-olmo-3-7b-think.md)\n- [Hy3 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-35b-a3b.md)\n- [Hy3 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-397b.md)\n- [Hy3 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/hy3-vs-ornith-1-5-9b.md)\n- [Hy3 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/hy3-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Hy3 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/hy3-vs-qwen3-4b-instruct-2507.md)\n- [Hy3 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-0-8b.md)\n- [Hy3 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-122b-a10b.md)\n- [Hy3 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-2b.md)\n- [Hy3 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-397b-a17b.md)\n- [Hy3 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-4b.md)\n- [Hy3 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-5-9b.md)\n- [Hy3 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-27b.md)\n- [Hy3 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-6-35b-a3b.md)\n- [Hy3 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-27b.md)\n- [Hy3 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/hy3-vs-qwen3-8-flash-next.md)\n- [Hy3 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Hy3 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/hy3-vs-qwen3-coder-next.md)\n- [Hy3 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Hy3 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-2b-instruct.md)\n- [Hy3 vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Hy3 vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-32b-instruct.md)\n- [Hy3 vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-4b-instruct.md)\n- [Hy3 vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/hy3-vs-qwen3-vl-8b-instruct.md)\n- [Hy3 vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/hy3-vs-qwopus3-8-27b-flash-v2.md)\n- [Hy3 vs SmolLM3-3B](https://www.anchorterminal.com/compare/hy3-vs-smollm3-3b.md)\n- [Hy3 vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/hy3-vs-spark-x2-6-4b.md)\n- [Hy3 vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-27b.md)\n- [Hy3 vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/hy3-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Hy3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/hy3-vs-thinkingcap-qwen3-8-27b.md)\n- [Inkling-Small vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-small-vs-lfm2-5-2-6b.md)\n- [Inkling vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/inkling-vs-lfm2-5-2-6b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-lfm2-5-2-6b.md)\n- [Kimi K3 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kimi-k3-vs-lfm2-5-2-6b.md)\n- [Kolibri-1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.md)\n- [Laguna M.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.md)\n- [Laguna S 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-lfm2-5-2-6b.md)\n- [Laguna XS 2.1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-lfm2-5-2-6b.md)\n- [LFM2.5-2.6B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ling-3-0-tiny.md)\n- [LFM2.5-2.6B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [LFM2.5-2.6B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm-o-4-5.md)\n- [LFM2.5-2.6B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minicpm5-2b.md)\n- [LFM2.5-2.6B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-5.md)\n- [LFM2.5-2.6B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-minimax-m2-7.md)\n- [LFM2.5-2.6B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-muse-glimmer-30b.md)\n- [LFM2.5-2.6B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-30b-a3b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-4b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [LFM2.5-2.6B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-super-120b-a12b.md)\n- [LFM2.5-2.6B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-nemotron-3-ultra-550b-a55b.md)\n- [LFM2.5-2.6B vs occamy-1.0](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-occamy-1-0.md)\n- [LFM2.5-2.6B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-olmo-3-7b-think.md)\n- [LFM2.5-2.6B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-35b-a3b.md)\n- [LFM2.5-2.6B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-397b.md)\n- [LFM2.5-2.6B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-ornith-1-5-9b.md)\n- [LFM2.5-2.6B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [LFM2.5-2.6B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-4b-instruct-2507.md)\n- [LFM2.5-2.6B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-0-8b.md)\n- [LFM2.5-2.6B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-122b-a10b.md)\n- [LFM2.5-2.6B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-qwen3-5-2b.md)\n- [LFM2.5-2.6B vs 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