{ "data": { "a": { "slug": "lfm3-audio-2b", "name": "LFM3-Audio-2B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM3-Audio-2B is Liquid AI's open-weight 2B audio model for on-device use, taking text or speech and answering in text, speech or both, with transcription, translation, tool calling and a 32K-token context, released on 27 October 2026.", "url": "https://www.anchorterminal.com/tools/lfm3-audio-2b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-audio-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-audio-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json", "dns": "lfm3-audio-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/LiquidAI/LFM3-Audio-2B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM3-Audio-2B" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Audio-2B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Audio-2B-MLX-4bit" }, { "registry": "ollama", "name": "lfm3-audio" } ], "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 2026-11-10).", "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, 1.10 GB for Liquid's Q4_K_M GGUF with its 0.17 GB projector file and 4.16 GB in BF16. Liquid's API also serves it, at prices shown in its console (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://docs.liquid.ai/lfm/models/lfm3-audio-2b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "audio", "speech", "speech-to-speech", "transcription", "small", "edge", "tool-calling", "custom-licence", "safetensors", "gguf", "mlx", "onnx", "self-hosted" ], "lastRelease": "2026-11-03", "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": 1518, "categoryRank": 56, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 80, "risk": 40, "upkeep": 52 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.", "bestFor": "Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model", "strengths": [ "Speech in and speech out on an 8 GB machine, streamed", "Transcribes, speaks and translates 19 languages and locales", "Tool calls from spoken requests, in a documented format", "Reasoning can be turned on per text reply", "Liquid's own API serves it" ], "weaknesses": [ "Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang", "A 32K-token context", "Voice chat is English only", "Neither question on the repository has a reply from Liquid", "Loading in Transformers runs code from the repository", "No safety results, security contact or training data description" ], "agentNotes": [ "Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`", "llama.cpp ships examples/realtime_client.py for live speech with this model", "Prefill the assistant turn with an open think tag to let it reason before a text reply", "The context is 32K", "Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales", "Liquid doesn't recommend it for agentic coding or complex multi-step reasoning" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 80, "risk": 40, "upkeep": 52 }, "provenanceScore": 73, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-audio", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/lfm3-audio-2b" }, "sameCompany": [ "liquid-d1", "lfm2-5-2-6b", "lfm3-1-7b", "lfm3-omni-2b", "lfm3-vl-2b" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "The LFM3 backbone (Liquid's own; the metadata names no base model)", "params": "2.00B", "context": 32000, "goodFor": "Small on-device audio model for voice chat, transcription, speech synthesis and translation in 19 languages and locales, questions about recordings and tool calls from spoken requests. Voice chat is English only.", "runtimes": [ "llama.cpp", "Ollama" ], "builds": [ { "name": "LFM3-Audio-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.16, "memoryGB": 8, "format": "safetensors", "runsOn": [ "Transformers" ], "note": "Loads in Transformers with the repository's own code. Liquid also publishes an ONNX repository. It has no FP8 or NVFP4 build of this model" }, { "name": "LFM3-Audio-2B-GGUF Q8_0", "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 2.15, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Audio-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-Audio-2B-Q8_0.gguf (0.30 GB)" }, { "name": "LFM3-Audio-2B-GGUF Q4_K_M", "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.27, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Audio-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-Audio-2B-Q4_K_M.gguf (0.17 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files" }, { "name": "LFM3-Audio-2B-MLX-4bit", "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 1.73, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX-VLM" ], "note": "Liquid also publishes 8-bit and BF16 MLX builds" }, { "name": "ollama lfm3-audio", "url": "https://ollama.com/library/lfm3-audio", "who": "ollama", "precision": "q4_k_m", "sizeGB": 1.7, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Ollama's library build, size as its tags page shows it on 10 November 2026. The library also has q8_0, bf16, mxfp8 and nvfp4 tags" } ], "run": "llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja", "runBuild": "LFM3-Audio-2B-GGUF Q4_K_M", "checked": "2026-11-10", "minMemoryGB": 8 }, "family": { "lab": "Liquid AI", "family": "LFM3", "url": "https://www.anchorterminal.com/open-weights/#liquid", "others": [ { "name": "LFM3-1.7B-Base", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B-Base", "params": "1.73B" }, { "name": "LFM3-1.7B", "url": "https://huggingface.co/LiquidAI/LFM3-1.7B", "params": "1.73B", "listing": "https://www.anchorterminal.com/tools/lfm3-1-7b" }, { "name": "LFM3-VL-2B", "url": "https://huggingface.co/LiquidAI/LFM3-VL-2B", "params": "1.92B", "listing": "https://www.anchorterminal.com/tools/lfm3-vl-2b" }, { "name": "LFM3-Omni-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "params": "2.17B", "listing": "https://www.anchorterminal.com/tools/lfm3-omni-2b" } ] }, "area": "models", "provenance": { "legalEntity": "Liquid AI, Inc. (the licensor named in the LFM Open License)", "domain": "liquid.ai", "domainRegistered": "2017-12-16", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/LiquidAI/LFM3-Audio-2B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "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", "www.liquid.ai/.well-known/security.txt answered 404 on 10 November 2026" ], "score": 73 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/lfm3-audio-2b.json" }, "answer": "LFM3-Audio-2B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach and risk.", "b": { "slug": "ornith-1-5-9b", "name": "Ornith-1.5-9B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-9B is Ornith's open-weight 9B dense reasoning model for coding and agent work, built on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, GGUF, MLX and NVFP4 and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-9b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json", "dns": "ornith-1-5-9b.grade.anchorterminal.com", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-GGUF" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-MLX-4bit" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-9B-NVFP4" }, { "registry": "ollama", "name": "ornith-1.5:9b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the MIT licence named in the card's metadata. The repository has no LICENSE file, and the card's licence link returns 404. Running it yourself costs the hardware, about 19 GB for the BF16 files or 5.8 GB for Ornith's Q4_K_M GGUF (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "mlx", "self-hosted" ], "lastRelease": "2026-08-23", "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": 1518, "categoryRank": 41, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 95, "risk": 50, "upkeep": 31 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.", "bestFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required", "strengths": [ "Fits in about 6 to 8 GB from Ornith's own MLX 4-bit, Q4_K_M GGUF or Ollama's build", "Runs in vLLM, SGLang, Transformers, llama.cpp, Ollama and LM Studio from their released versions", "The card names the tool and reasoning parsers for vLLM and SGLang, with a tool-call example", "262,144 tokens of native context", "Safetensors and GGUF only, no gate and no code in the repository" ], "weaknesses": [ "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "The fourth weight shard and every GGUF were replaced in place on 23 and 24 August 2026 with no note", "Ornith answered none of the 15 discussions", "The two chat templates disagree on keeping earlier reasoning, and structured output isn't documented" ], "agentNotes": [ "Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file", "In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed", "Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it", "Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card", "Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 95, "risk": 50, "upkeep": 31 }, "provenanceScore": 19, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run ornith-1.5:9b\n# or, with the card's vLLM command:\nvllm serve ornith-ai/Ornith-1.5-9B --served-model-name Ornith-1.5-9B --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"ornith-1.5:9b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-9b" }, "sameCompany": [ "ornith-1-5-122b", "ornith-1-5-35b-a3b", "ornith-1-5-397b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Ornith-1.0 (which Ornith says it built on Qwen3.5 and Gemma 4); qwen3_5 architecture", "params": "9B", "context": 262144, "goodFor": "Coding and terminal-agent model with tool calling and thinking on by default, text and images in.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "LM Studio", "Transformers" ], "builds": [ { "name": "Ornith-1.5-9B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 5.8, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input" }, { "name": "Ornith-1.5-9B-MLX-4bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 5, "memoryGB": 8, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "ollama ornith-1.5:9b", "url": "https://ollama.com/library/ornith-1.5:9b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 6.6, "memoryGB": 8, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M with a BF16 vision projector, linked from Ornith's card; the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB; the Linux build is the llama.cpp variant" }, { "name": "Ornith-1.5-9B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 9.8, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 7.6, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-MLX-8bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-8bit", "who": "publisher", "precision": "8bit", "sizeGB": 9.5, "memoryGB": 12, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-9B-MLX-6bit", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX-6bit", "who": "publisher", "precision": "6bit", "sizeGB": 7.3, "memoryGB": 12, "format": "mlx", "note": "MLX runs on Apple silicon only" }, { "name": "Ornith-1.5-9B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 8.8, "memoryGB": 12, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell" }, { "name": "Ornith-1.5-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "who": "publisher", "precision": "bf16", "sizeGB": 19.3, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] }, { "name": "Ornith-1.5-9B-BF16", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-GGUF/blob/main/Ornith-1.5-9B-BF16.gguf", "who": "publisher", "precision": "bf16", "sizeGB": 18.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "Ornith-1.5-9B-MLX", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-MLX", "who": "publisher", "precision": "bf16", "sizeGB": 17.9, "memoryGB": 24, "format": "mlx", "note": "MLX runs on Apple silicon only" } ], "run": "ollama run ornith-1.5:9b", "runBuild": "ollama ornith-1.5:9b", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "Ornith", "family": "Ornith-1", "url": "https://www.anchorterminal.com/open-weights/#ornith", "others": [ { "name": "Ornith-1.0-9B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-9B", "params": "9B (dense)" }, { "name": "Ornith-1.0-35B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-35B", "params": "35B (MoE)" }, { "name": "Ornith-1.0-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.0-397B", "params": "397B (MoE)" }, { "name": "Ornith-1.5-9B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B-DFlash", "params": "1.29B (Hub safetensors count)" }, { "name": "Ornith-1.5-35B-A3B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B", "params": "35B total, 3B active (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-35b-a3b" }, { "name": "Ornith-1.5-35B-A3B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-35B-A3B-DFlash", "params": "0.39B (Hub safetensors count)" }, { "name": "Ornith-1.5-122B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-122B", "params": "122B (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-122b" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "params": "397B (MoE)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-397b" }, { "name": "Ornith-1.5-397B-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "", "domain": "ornith.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "No legal entity named on the card, the blog or the repository. The blog's footer reads 2026 Ornith Team, and the Hugging Face organisation ornith-ai isn't verified.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored. The licence is MIT in the card's metadata only; the LICENSE file the card links returns 404.", "No changelog for the files. Ollama's library page carries Ornith's dated release lines (19 August and 20 October 2026), and the commit messages are uploads and README updates.", "ornith.ai/.well-known/security.txt redirects to the home page, so there is no security.txt.", "unchecked: the domain's registration date, since RDAP answered 429 on 21 October 2026." ], "score": 19 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-9b.json", "live": { "slug": "ornith-1-5-9b", "securityTxt": { "url": "https://ornith.ai/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:07:27.802812649Z" }, "domain": { "domain": "ornith.ai", "registered": "2026-06-16", "source": "https://rdap.identitydigital.services/rdap/domain/ornith.ai", "checkedAt": "2026-11-08T14:47:46.372430234Z" }, "updatedAt": "2026-11-10T16:07:27.802812649Z" } }, "edges": [ { "by": 21, "category": "upkeep", "leads": "lfm3-audio-2b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52.", "b": "Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 15, the newest change to the model's files was Ornith's NVFP4 build on 26 August 2026, 69 days ago, and no card of the model or its builds has changed since. Responsiveness 0, Ornith answered none of the 15 questions and reports. Version policy 0, the fourth safetensors shard and every GGUF file were replaced in place on 23 and 24 August with no note. File health 0, the card links a missing LICENSE and an unpublished Mobile build, and the two chat templates differ. 11 + 5 + 15 + 0 + 0 + 0 = 31." }, "scores": { "a": 52, "b": 31 } }, { "by": 15, "category": "reach", "leads": "ornith-1-5-9b", "name": "Reach", "reasons": { "a": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.", "b": "Memory 30, since Ornith's own MLX 4-bit build is 5.0 GB and needs about 6.0 GB with the fifth added, and its Q4_K_M GGUF and Ollama's build fit 16 GB too. Runtimes 25, the most the line gives, from vLLM 0.19.1 (still in v0.33.0), SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35 architecture, Ollama's library and LM Studio's catalogue for the qwen35 architecture. Builds 15, Ornith publishes GGUF, MLX and NVFP4 at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and only one inference provider (featherless-ai) serves it. 30 + 25 + 15 + 15 + 10 + 0 = 95." }, "scores": { "a": 80, "b": 95 } }, { "by": 10, "category": "risk", "leads": "ornith-1-5-9b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.", "b": "File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact." }, "scores": { "a": 40, "b": 50 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Liquid AI", "b": "Ornith", "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": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "yes", "b": "no", "name": "llms.txt" }, { "a": "2026-11-03", "b": "2026-08-23", "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": "none", "b": "2.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "LFM3-Audio-2B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach and risk.", "question": "Which is better for AI agents, LFM3-Audio-2B or Ornith-1.5-9B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 52 against 31" ], "also": null, "goodFor": "Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model", "slug": "lfm3-audio-2b", "watchFor": "Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang" }, { "aheadOn": [ "Reach, 95 against 80", "Risk, 50 against 40" ], "also": null, "goodFor": "Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-9b", "watchFor": "No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on" } ], "history": null, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-audio-2b.json", "title": "Agents-A1-4B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.json", "title": "Agents-A1-4B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b.json", "title": "Apertus v1.5 8B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-audio-2b.json", "title": "Apodex-1.1-mini vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.json", "title": "Apodex-1.1-mini vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b.json", "title": "Arete-27B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b.json", "title": "Arete-27B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-audio-2b.json", "title": "BigBang-v1 vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b.json", "title": "BigBang-v1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-audio-2b.json", "title": "Carbon-X vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-9b.json", "title": "Carbon-X vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-audio-2b.json", "title": "Cola2 vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-9b.json", "title": "Cola2 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-audio-2b.json", "title": "Cosmos-Reason2-2B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b.json", "title": "Cosmos-Reason2-2B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-audio-2b.json", "title": "Darwin-36B-Opus vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b.json", "title": "Darwin-36B-Opus vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-audio-2b.json", "title": "DeepSeek-V3.2 vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b.json", "title": "DeepSeek-V3.2 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-audio-2b.json", "title": "DeepSeek V4.1 Flash vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b.json", "title": "DeepSeek V4.1 Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-audio-2b.json", "title": "DeepSeek-V4-Flash-0731 vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-lfm3-audio-2b" }, { "json": 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vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-audio-2b.json", "title": "Gemma 4 26B A4B IT vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 26B A4B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-audio-2b.json", "title": "Gemma 4 31B IT vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b.json", "title": "Gemma 4 31B IT vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-ornith-1-5-9b" }, { "json": 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"https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b.json", "title": "GLM-5.3-Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-audio-2b.json", "title": "GLM-5.3 vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-9b.json", "title": "GLM-5.3 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-audio-2b.json", "title": "gpt-oss-120b vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-ornith-1-5-9b.json", "title": "gpt-oss-120b vs Ornith-1.5-9B", "url": 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"title": "Kolibri-1 vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b.json", "title": "Kolibri-1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-audio-2b.json", "title": "Kronos vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/kronos-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-9b.json", "title": "Kronos vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/kronos-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-audio-2b.json", "title": "Laguna M.1 vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.json", "title": 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"https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-9b.json", "title": "MiniCPM-V 4.7 35B-A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm-v-4-7-35b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b.json", "title": "MiniCPM5-2B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minicpm5-2b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b.json", "title": "MiniMax-M2.5 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b.json", "title": "MiniMax-M2.7 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m2-7-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-9b.json", "title": "MiniMax-M3.1-Flash vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/minimax-m3-1-flash-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b.json", "title": "Mistral Large 4 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/mistral-large-4-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b.json", "title": "Muse Glimmer 30B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-9b.json", "title": "Nanbeige4.2-3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-2-3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b.json", "title": "Nanbeige4.3-33B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-9b.json", "title": "NVIDIA Nemotron 3.5 Bolt E4B 4GB vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-bolt-e4b-4gb-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b.json", "title": "Nemotron 3.5 Lightning 30B A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-5-lightning-30b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano 30B A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-30b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano 4B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-4b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Nano Omni 30B A3B Reasoning vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-nano-omni-30b-a3b-reasoning-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Super 120B A12B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-super-120b-a12b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b.json", "title": "Nemotron 3 Ultra 550B A55B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/nemotron-3-ultra-550b-a55b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-9b.json", "title": "North Mini Code 1.0 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/north-mini-code-1-0-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b.json", "title": "occamy-1.0 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b.json", "title": "Olmo 3 7B Think vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-9b.json", "title": "Ornith-1.5-122B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-122b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b.json", "title": "Ornith-1.5-35B-A3B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b.json", "title": "Ornith-1.5-397B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-otel-2-0-llm-31b-it.json", "title": "Ornith-1.5-9B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Ornith-1.5-9B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507.json", "title": "Ornith-1.5-9B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b.json", "title": "Ornith-1.5-9B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b.json", "title": "Ornith-1.5-9B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b.json", "title": "Ornith-1.5-9B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b.json", "title": "Ornith-1.5-9B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b.json", "title": "Ornith-1.5-9B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b.json", "title": "Ornith-1.5-9B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b.json", "title": "Ornith-1.5-9B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b.json", "title": "Ornith-1.5-9B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next.json", "title": "Ornith-1.5-9B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next.json", "title": "Ornith-1.5-9B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct.json", "title": "Ornith-1.5-9B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2.json", "title": "Ornith-1.5-9B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint.json", "title": "Ornith-1.5-9B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview.json", "title": "Ornith-1.5-9B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-sensenova-loop-9b-preview.json", "title": "Ornith-1.5-9B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b.json", "title": "Ornith-1.5-9B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b.json", "title": "Ornith-1.5-9B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-step-3-7-flash.json", "title": "Ornith-1.5-9B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Ornith-1.5-9B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-9B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-9B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-9B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-zaya1-8b.json", "title": "Ornith-1.5-9B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 15, "edge": "ornith-1-5-9b", "key": "reach", "lfm3-audio-2b": 80, "name": "Reach", "ornith-1-5-9b": 95, "weight": 25 }, { "by": 10, "edge": "ornith-1-5-9b", "key": "risk", "lfm3-audio-2b": 40, "name": "Risk", "ornith-1-5-9b": 50, "weight": 20 }, { "by": 0, "edge": "", "key": "interface", "lfm3-audio-2b": 85, "name": "Agent interface", "ornith-1-5-9b": 85, "weight": 10 }, { "by": 21, "edge": "lfm3-audio-2b", "key": "upkeep", "lfm3-audio-2b": 52, "name": "Documentation and upkeep", "ornith-1-5-9b": 31, "weight": 10 } ], "summary": "LFM3-Audio-2B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach and risk. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "lfm3-audio-2b": "A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.", "ornith-1-5-9b": "A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-9b", "json": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-9b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-9b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-9b.min.md" }, "markdown": "LFM3-Audio-2B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach and risk. Both do open-weight models you run yourself.\n\n- LFM3-Audio-2B: grade C, 61/100, rank #70 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-audio-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json\n- Ornith-1.5-9B: grade C, 61/100, rank #51 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-9b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.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 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### LFM3-Audio-2B (C)\n\nGood for: Voice agents on a laptop or phone that need speech in, speech out and tool calls from one small model\n\nAhead on:\n- Documentation and upkeep, 52 against 31\n\nWatch for: Only llama.cpp and Ollama run it in a release, though the card lists vLLM and SGLang\n\n### Ornith-1.5-9B (C)\n\nGood for: Local coding and terminal agents on a laptop or a single consumer GPU, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 95 against 80\n- Risk, 50 against 40\n\nWatch for: No LICENSE file though the card links one, and no notice for the Qwen3.5 and Gemma 4 work it builds on\n\n\n## Why the edge\n\n### Documentation and upkeep: LFM3-Audio-2B +21\n\n- LFM3-Audio-2B (52): Model card 17, Intended use (local agents, tool calling, structured outputs, fine-tuning), limitations (not recommended for agentic coding or complex multi-step reasoning, voice chat in English only), an architecture section, parameters (2B), context (32K) and 19 speech languages and locales. Training data 5, the card has no training section. Last change 20, the last commit, on 3 November 2026, changed the card's navigation icons, seven days before this check, and the one before it, on 31 October, edited the card. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (27 and 31 October 2026) has a reply from a LiquidAI member. Version policy 5, LFM3 came out in new repositories and the LFM2.5 models stay in theirs. File health 5, the card gives a 32K context while config.json gives the language model 128,000 positions and Liquid's API lists 128,000 for the hosted model, so the card and the files don't agree on context. 17 + 5 + 20 + 0 + 5 + 5 = 52.\n- Ornith-1.5-9B (31): Model card 11, it covers intended use, architecture, parameters and context but not limitations, languages or a knowledge cut-off. Training data 5, a description of the method with no data or cut-off. Last change 15, the newest change to the model's files was Ornith's NVFP4 build on 26 August 2026, 69 days ago, and no card of the model or its builds has changed since. Responsiveness 0, Ornith answered none of the 15 questions and reports. Version policy 0, the fourth safetensors shard and every GGUF file were replaced in place on 23 and 24 August with no note. File health 0, the card links a missing LICENSE and an unpublished Mobile build, and the two chat templates differ. 11 + 5 + 15 + 0 + 0 + 0 = 31.\n\n### Reach: Ornith-1.5-9B +15\n\n- LFM3-Audio-2B (80): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.17 GB projector file, and 1.27 GB × 1.2 = 1.5 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 audio projector and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example that starts this model), and Ollama's library has an lfm3-audio entry. Builds 15, Liquid's GGUF repository holds Q4_0, Q4_K_M, Q8_0, F16 and BF16 files, and Liquid publishes 4-bit and 8-bit MLX builds and an ONNX repository, all in the LiquidAI organisation. Terms 10, the LFM Open License v1.0, a custom licence that lets anyone use it commercially for free below a threshold of $10M annual revenue. Download 10, the Hub API reports gated false for the model and its GGUF, MLX and ONNX repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, Liquid's own API lists lfm3-audio-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.\n- Ornith-1.5-9B (95): Memory 30, since Ornith's own MLX 4-bit build is 5.0 GB and needs about 6.0 GB with the fifth added, and its Q4_K_M GGUF and Ollama's build fit 16 GB too. Runtimes 25, the most the line gives, from vLLM 0.19.1 (still in v0.33.0), SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35 architecture, Ollama's library and LM Studio's catalogue for the qwen35 architecture. Builds 15, Ornith publishes GGUF, MLX and NVFP4 at several precisions. Terms 15, MIT as named in the card's metadata on 21 October 2026, with nothing added, read by its terms though the repository has no licence file. Download 10, nothing is gated. Hosted 0, Ornith runs no API and only one inference provider (featherless-ai) serves it. 30 + 25 + 15 + 15 + 10 + 0 = 95.\n\n### Risk: Ornith-1.5-9B +10\n\n- LFM3-Audio-2B (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, files in the LiquidAI organisation, which Liquid's docs page for the model and its blog post link to (10). Safety evaluation 0, no safety results on the card or in Liquid's blog post for the LFM3 family, neither of which mentions safety testing. Agent guidance 0, Nothing on prompt injection, an instruction hierarchy, what refusals cover or safeguards to add. Licence risk 10, 15 less 5 for the $10M annual revenue threshold on commercial use. Security programme 0, no way to report a flaw found. 15 + 15 + 0 + 0 + 10 + 0 = 40.\n- Ornith-1.5-9B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories, so the card's --trust-remote-code flag loads nothing from the repository. Provenance 15, the files come from the ornith-ai organisation Ornith's blog links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 10, MIT carries no policy, threshold or changeable terms, but the card's licence link is broken. Security programme 0, no security.txt, disclosure policy or security contact.\n\n## Score by category\n\n| Category | Weight | LFM3-Audio-2B | Ornith-1.5-9B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 80 | 95 | Ornith-1.5-9B +15 |\n| Risk | 20% (20 this run) | 40 | 50 | Ornith-1.5-9B +10 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 52 | 31 | LFM3-Audio-2B +21 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | LFM3-Audio-2B | Ornith-1.5-9B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Liquid AI | Ornith |\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 | LFM Open License v1.0 (free commercial use below $10M annual revenue) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | yes | no |\n| Last release | 2026-11-03 | 2026-08-23 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 2.3/5 (4) |\n\n## Verdicts\n\n**LFM3-Audio-2B.** A 2B model that listens and speaks on an 8 GB machine, transcribes and translates 19 languages and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, voice chat is English only, Liquid hasn't answered the repository's questions, and there are no safety results or data sources.\n\n**Ornith-1.5-9B.** A small coding and terminal-agent cousin with thinking on by default, XML tool calls that vLLM and SGLang parse, and 262,144 tokens of context. Ornith's own 4-bit builds run in under 8 GB in most local runtimes. The main risk is an absent publisher, with no licence file, no safety results, files replaced without a note and no replies.\n\n## Before you call either\n\n### LFM3-Audio-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-Audio-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-audio`\n2. llama.cpp ships examples/realtime_client.py for live speech with this model\n3. Prefill the assistant turn with an open think tag to let it reason before a text reply\n4. The context is 32K\n5. Voice chat is English only, while transcription, synthesis and translation cover 19 languages and locales\n6. Liquid doesn't recommend it for agentic coding or complex multi-step reasoning\n\n### Ornith-1.5-9B\n\n1. Start it with `ollama run ornith-1.5:9b`, or `llama-server -hf ornith-ai/Ornith-1.5-9B-GGUF` with the Q4_K_M file\n2. In vLLM pass `--enable-auto-tool-choice --tool-call-parser qwen3_xml --reasoning-parser qwen3`, and in SGLang `--tool-call-parser qwen3_coder --reasoning-parser qwen3`, so tool calls and reasoning come back parsed\n3. Thinking is on by default. Send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n4. Pin which template your runtime loads, since chat_template.jinja keeps earlier reasoning and the tokenizer_config.json template drops it\n5. Use temperature 0.6, top_p 0.95, top_k 20 for coding and temperature 1.0 with presence_penalty 1.5 for general tasks, per the card\n6. Pin a revision when you download, since Ornith has replaced files in place, and add mmproj-Ornith-1.5-9B-BF16.gguf (0.9 GB) for image input in llama.cpp\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, LFM3-Audio-2B or Ornith-1.5-9B?\n\nLFM3-Audio-2B and Ornith-1.5-9B score within a point of each other on agent readiness, 61 (C) and 61 (C). Ornith-1.5-9B leads on reach and risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-9b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/lfm3-audio-2b-vs-ornith-1-5-9b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"lfm3-audio-2b\", \"b\": \"ornith-1-5-9b\"}`. From a terminal: `anchor compare lfm3-audio-2b ornith-1-5-9b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/lfm3-audio-2b.json and https://www.anchorterminal.com/api/v1/tools/ornith-1-5-9b.json\n\n## Other comparisons with LFM3-Audio-2B or Ornith-1.5-9B\n\n- [Agents-A1-4B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm3-audio-2b.md)\n- [Agents-A1-4B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.md)\n- [Apertus v1.5 8B vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm3-audio-2b.md)\n- [Apertus v1.5 8B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.md)\n- [Apodex-1.1-mini vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-lfm3-audio-2b.md)\n- [Apodex-1.1-mini vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-ornith-1-5-9b.md)\n- [Arete-27B vs 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WeVisOne-VL-4B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-wevisone-vl-4b.md)\n- [Ornith-1.5-9B vs ZAYA1-8B](https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-zaya1-8b.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": "2026-11-10", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": 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