{ "data": { "a": { "slug": "cola2", "name": "Cola2", "vendor": "ByteDance (Seed)", "vendorUrl": "https://github.com/ByteDance-Seed", "kind": "weights", "category": "open-weights", "summary": "Cola2 is a research checkpoint from ByteDance Seed, a base language model of 8.8B parameters that generates text through continuous latents with a diffusion transformer and a token decoder. Not a chat model, and the weights carry no stated licence.", "url": "https://www.anchorterminal.com/tools/cola2", "markdownUrl": "https://www.anchorterminal.com/tools/cola2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/cola2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/cola2.json", "dns": "cola2.grade.anchorterminal.com", "repo": "https://huggingface.co/ByteDance-Seed/Cola2", "license": "None stated for the weights (the code is Apache 2.0)", "transports": [], "packages": [ { "registry": "huggingface", "name": "ByteDance-Seed/Cola2" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download, but nothing says what you may do with the weights. The card says the code is Apache 2.0 and that the weights remain subject to their respective terms, without naming them. Running it yourself costs the hardware: 35.1 GB of F32 files, loaded as BF16 in the card's example (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://github.com/ByteDance-Seed/Cola2", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "base-model", "research", "diffusion", "continuous-latent", "no-licence", "safetensors", "custom-code", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 33.9, "grade": "F", "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": 113, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 20, "reach": 25, "risk": 40, "upkeep": 58 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.", "bestFor": "Researchers working on latent or diffusion language models who want a checkpoint to study", "strengths": [ "A documented new architecture with a paper and a reproduction protocol", "Evaluation data and checksums ship with the weights", "A security policy that points to ByteDance's security centre", "The model card says plainly what it isn't for" ], "weaknesses": [ "No licence stated for the weights", "A base model: no chat, no tools, 32 new tokens by default", "Loads only with the repository's own Python, on Transformers 4.x", "35 GB of F32 files and no quantised build", "Below Llama-3-8B on the card's own eight-task table", "No stated context length or cut-off" ], "agentNotes": [ "Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions", "Don't use it commercially on the strength of this listing. The weights have no stated licence", "If you study it, read modeling_cola2.py first: trust_remote_code runs it", "Pin Transformers below 5, as the card requires", "Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding", "Check the download with `sha256sum -c SHA256SUMS`" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "F", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 33.9 } ], "editorialScores": { "interface": 20, "reach": 25, "risk": 40, "upkeep": 58 }, "provenanceScore": 58, "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": "pip install \"torch\u003e=2.2\" \"transformers\u003e=4.51,\u003c5\" \"safetensors\u003e=0.4\" accelerate\n# then AutoModel.from_pretrained(\"ByteDance-Seed/Cola2\", trust_remote_code=True, torch_dtype=torch.bfloat16)\n# trust_remote_code runs the three Python files in the repository. Read them first", "http": "# No server. The model runs in process through Transformers, and the GitHub repository has a\n# command-line chat script (scripts/run_chat.sh) and a benchmark runner" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/cola2" }, "weights": { "trainedBy": "ByteDance Seed", "builtOn": "The decoder and encoder embedding are initialised from Qwen3-4B-Base, per the GitHub model card", "params": "8.8B", "goodFor": "Research on continuous-latent text generation: short completions, cloze and multiple-choice experiments, and reproducing the paper's numbers.", "runtimes": [], "builds": [ { "name": "Cola2 (F32 safetensors)", "url": "https://huggingface.co/ByteDance-Seed/Cola2/tree/main", "who": "publisher", "precision": "f32", "sizeGB": 35.07, "memoryGB": 48, "format": "safetensors", "note": "Eight shards in 32-bit floats. The card's example loads them as BF16" } ], "run": "python -c \"from transformers import AutoModel; AutoModel.from_pretrained('ByteDance-Seed/Cola2', trust_remote_code=True)\"", "runBuild": "Cola2 (F32 safetensors)", "checked": "2026-11-10", "minMemoryGB": 48 }, "family": { "lab": "ByteDance (Seed)", "family": "Cola", "url": "https://www.anchorterminal.com/open-weights/#bytedance-seed", "others": [ { "name": "Cola-DLM", "url": "https://huggingface.co/ByteDance-Seed/Cola-DLM" } ] }, "area": "models", "provenance": { "legalEntity": "ByteDance, named in the GitHub repository's security policy. The legal entity wasn't looked up", "domain": "bytedance.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/ByteDance-Seed/Cola2/commits/main", "securityTxt": "unknown", "checked": "2026-11-10", "notes": [ "The files are in the ByteDance-Seed organisation on Hugging Face and the code is in the ByteDance-Seed organisation on GitHub; each links the other.", "No licence is stated for the weights, so there are no terms to read.", "The domain's registration date and security.txt weren't looked up. The GitHub repository's SECURITY.md points to ByteDance's security centre.", "The changelog is the commit history, six commits on 6 November 2026." ], "score": 58 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/cola2.json" }, "answer": "LFM3-Omni-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 2 of 4 scored categories. Cola2 leads on documentation and upkeep.", "b": { "slug": "lfm3-omni-2b", "name": "LFM3-Omni-2B", "vendor": "Liquid AI", "vendorUrl": "https://www.liquid.ai", "kind": "weights", "category": "open-weights", "summary": "LFM3-Omni-2B is Liquid AI's open-weight 2.17B omni model for on-device use, taking text, images, video and audio and answering in text or speech, with tool calling and a 32K-token context, released on 27 October 2026 with GGUF and MLX builds.", "url": "https://www.anchorterminal.com/tools/lfm3-omni-2b", "markdownUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/lfm3-omni-2b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.json", "dns": "lfm3-omni-2b.grade.anchorterminal.com", "repo": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "license": "LFM Open License v1.0 (free commercial use below $10M annual revenue)", "transports": [], "packages": [ { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B-GGUF" }, { "registry": "huggingface", "name": "LiquidAI/LFM3-Omni-2B-MLX-4bit" }, { "registry": "ollama", "name": "lfm3-omni" } ], "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 0.29 GB projector file and 4.49 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-omni-2b", "llmsTxt": "https://docs.liquid.ai/llms.txt", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "omni", "vision", "video", "audio", "speech", "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": 57, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 80, "risk": 40, "upkeep": 49 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources.", "bestFor": "Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools", "strengths": [ "One model for text, images, video, audio and speech on an 8 GB machine", "Speech output and a realtime speech server in released llama.cpp", "Tool calls from spoken requests, in a documented format", "Updated a week before this check", "Liquid's own API serves it" ], "weaknesses": [ "Only llama.cpp and Ollama run it in a release", "A 32K-token context, which video and audio fill quickly", "It doesn't reason, and structured output isn't documented", "None of 12 questions on the repository has a reply from Liquid", "Loading in Transformers runs 14 Python files from the repository", "No safety results, security contact or training data description" ], "agentNotes": [ "Start it with `llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-omni`", "Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card", "The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames", "Parse tool calls yourself from the XML it returns", "It doesn't reason, so don't pass enable_thinking", "Validate JSON output yourself" ], "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": 70, "reach": 80, "risk": 40, "upkeep": 49 }, "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-Omni-2B-GGUF:Q4_K_M --jinja\n# or\nollama run lfm3-omni", "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-omni-2b" }, "sameCompany": [ "liquid-d1", "lfm2-5-2-6b", "lfm3-1-7b", "lfm3-audio-2b", "lfm3-vl-2b" ], "weights": { "trainedBy": "Liquid AI", "builtOn": "The LFM3 backbone (Liquid's own; the metadata names no base model)", "params": "2.17B", "context": 32000, "goodFor": "One small on-device model in place of separate speech recognition, vision, language and speech models, for voice chat, questions about video with its soundtrack and tool calls from spoken requests. It doesn't reason.", "runtimes": [ "llama.cpp", "Ollama" ], "builds": [ { "name": "LFM3-Omni-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B", "who": "publisher", "precision": "bf16", "sizeGB": 4.49, "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-Omni-2B-GGUF Q8_0", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 2.36, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Omni-2B-Q8_0.gguf (1.85 GB) plus mmproj-LFM3-Omni-2B-Q8_0.gguf (0.51 GB)" }, { "name": "LFM3-Omni-2B-GGUF Q4_K_M", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 1.39, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "LFM3-Omni-2B-Q4_K_M.gguf (1.10 GB) plus Liquid's projector file mmproj-LFM3-Omni-2B-Q4_K_M.gguf (0.29 GB), from the Hub's file list. The repository also holds Q4_0, Q8_0, F16 and BF16 files" }, { "name": "LFM3-Omni-2B-MLX-4bit", "url": "https://huggingface.co/LiquidAI/LFM3-Omni-2B-MLX-4bit", "who": "publisher", "precision": "4bit", "sizeGB": 2.11, "memoryGB": 8, "format": "mlx", "runsOn": [ "MLX-VLM" ], "note": "Liquid also publishes 8-bit and BF16 MLX builds" }, { "name": "ollama lfm3-omni", "url": "https://ollama.com/library/lfm3-omni", "who": "ollama", "precision": "q4_k_m", "sizeGB": 2, "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-Omni-2B-GGUF:Q4_K_M --jinja", "runBuild": "LFM3-Omni-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-Audio-2B", "url": "https://huggingface.co/LiquidAI/LFM3-Audio-2B", "params": "2.00B", "listing": "https://www.anchorterminal.com/tools/lfm3-audio-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-Omni-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-omni-2b.json" }, "edges": [ { "by": 55, "category": "reach", "leads": "lfm3-omni-2b", "name": "Reach", "reasons": { "a": "Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.", "b": "Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni 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-omni-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80." }, "scores": { "a": 25, "b": 80 } }, { "by": 50, "category": "interface", "leads": "lfm3-omni-2b", "name": "Agent interface", "reasons": { "a": "Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.", "b": "Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70." }, "scores": { "a": 20, "b": 70 } }, { "by": 9, "category": "upkeep", "leads": "cola2", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.", "b": "Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. 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 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 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. 14 + 5 + 20 + 0 + 5 + 5 = 49." }, "scores": { "a": 58, "b": 49 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "ByteDance (Seed)", "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": "None stated for the weights (the code is 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": "2026-11-06", "b": "2026-11-03", "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" } ], "faq": [ { "answer": "LFM3-Omni-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 2 of 4 scored categories. Cola2 leads on documentation and upkeep.", "question": "Which is better for AI agents, Cola2 or LFM3-Omni-2B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 58 against 49" ], "also": null, "goodFor": "Researchers working on latent or diffusion language models who want a checkpoint to study", "slug": "cola2", "watchFor": "No licence stated for the weights" }, { "aheadOn": [ "Reach, 80 against 25", "Agent interface, 70 against 20" ], "also": null, "goodFor": "Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools", "slug": "lfm3-omni-2b", "watchFor": "Only llama.cpp and Ollama run it in a release" } ], "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-cola2.json", "title": "Agents-A1-4B vs Cola2", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2" }, { 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{ "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-o-4-5.json", "title": "LFM3-Omni-2B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-1b.json", "title": "LFM3-Omni-2B vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-35b-a3b.json", "title": "LFM3-Omni-2B vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm5-2b.json", "title": "LFM3-Omni-2B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-minimax-m2-5.json", "title": "LFM3-Omni-2B vs MiniMax-M2.5", "url": 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"https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nanbeige-4-2-3b.json", "title": "LFM3-Omni-2B vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nanbeige-4-3-33b.json", "title": "LFM3-Omni-2B vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "LFM3-Omni-2B vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LFM3-Omni-2B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-30b-a3b.json", "title": "LFM3-Omni-2B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-4b.json", "title": "LFM3-Omni-2B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "LFM3-Omni-2B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-super-120b-a12b.json", "title": "LFM3-Omni-2B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-ultra-550b-a55b.json", "title": "LFM3-Omni-2B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0.json", "title": "LFM3-Omni-2B vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-occamy-1-0.json", "title": "LFM3-Omni-2B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-olmo-3-7b-think.json", "title": "LFM3-Omni-2B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ornith-1-5-122b.json", "title": "LFM3-Omni-2B vs 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"https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b.json", "title": "LFM3-Omni-2B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "LFM3-Omni-2B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-4b-instruct-2507.json", "title": "LFM3-Omni-2B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-0-8b.json", "title": "LFM3-Omni-2B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-122b-a10b.json", "title": "LFM3-Omni-2B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-2b.json", "title": "LFM3-Omni-2B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-397b-a17b.json", "title": "LFM3-Omni-2B vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-4b.json", "title": "LFM3-Omni-2B vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-9b.json", "title": "LFM3-Omni-2B vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b.json", "title": "LFM3-Omni-2B vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-35b-a3b.json", "title": "LFM3-Omni-2B vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-27b.json", "title": "LFM3-Omni-2B vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-flash-next.json", "title": "LFM3-Omni-2B vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-next.json", "title": "LFM3-Omni-2B vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-2b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-32b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-4b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct.json", "title": "LFM3-Omni-2B vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwopus3-8-27b-flash-v2.json", "title": "LFM3-Omni-2B vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-recursive-flint.json", "title": "LFM3-Omni-2B vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ripple-65b-preview.json", "title": "LFM3-Omni-2B vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-sensenova-loop-9b-preview.json", "title": "LFM3-Omni-2B vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-smollm3-3b.json", "title": "LFM3-Omni-2B vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-spark-x2-6-4b.json", "title": "LFM3-Omni-2B vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash.json", "title": "LFM3-Omni-2B vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-27b.json", "title": "LFM3-Omni-2B vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LFM3-Omni-2B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "LFM3-Omni-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2.json", "title": "LFM3-Omni-2B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b.json", "title": "LFM3-Omni-2B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b.json", "title": "LFM3-Omni-2B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-zaya1-8b.json", "title": "LFM3-Omni-2B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/lfm3-omni-2b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 55, "cola2": 25, "edge": "lfm3-omni-2b", "key": "reach", "lfm3-omni-2b": 80, "name": "Reach", "weight": 25 }, { "by": 0, "cola2": 40, "edge": "", "key": "risk", "lfm3-omni-2b": 40, "name": "Risk", "weight": 20 }, { "by": 50, "cola2": 20, "edge": "lfm3-omni-2b", "key": "interface", "lfm3-omni-2b": 70, "name": "Agent interface", "weight": 10 }, { "by": 9, "cola2": 58, "edge": "cola2", "key": "upkeep", "lfm3-omni-2b": 49, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "LFM3-Omni-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 2 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "cola2": "A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.", "lfm3-omni-2b": "One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b", "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b.min.md" }, "markdown": "LFM3-Omni-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 2 of 4 scored categories. Cola2 leads on documentation and upkeep. Both do open-weight models you run yourself.\n\n- Cola2: grade F, 33.9/100, rank #160 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/cola2.md · JSON https://www.anchorterminal.com/api/v1/tools/cola2.json\n- LFM3-Omni-2B: grade C, 61/100, rank #71 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/lfm3-omni-2b.md · JSON https://www.anchorterminal.com/api/v1/tools/lfm3-omni-2b.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### Cola2 (F)\n\nGood for: Researchers working on latent or diffusion language models who want a checkpoint to study\n\nAhead on:\n- Documentation and upkeep, 58 against 49\n\nWatch for: No licence stated for the weights\n\n### LFM3-Omni-2B (C)\n\nGood for: Voice and camera agents on a laptop or phone that want one small model for hearing, seeing, speaking and calling tools\n\nAhead on:\n- Reach, 80 against 25\n- Agent interface, 70 against 20\n\nWatch for: Only llama.cpp and Ollama run it in a release\n\n\n## Why the edge\n\n### Reach: LFM3-Omni-2B +55\n\n- Cola2 (25): Memory 15, the only build is the F32 safetensors set, 35.07 GB, and 35.07 GB × 1.2 = 42.1 GB, within 64 GB. Loaded as BF16, as the card's example does, it needs about half, but the line reads the file size. Runtimes 0, none of the six runs it in a released version. Builds 0, one set of weights and no quantised build. Terms 0, no licence for the weights. Download 10, the Hub API reports gated false on 10 November 2026, so the files download with no account and nothing to accept. Hosted 0, Hugging Face maps no inference provider to it, and nothing names an API that serves it. 15 + 0 + 0 + 0 + 10 + 0 = 25.\n- LFM3-Omni-2B (80): Memory 30, Liquid's Q4_K_M GGUF is 1.10 GB with its 0.29 GB projector file, and 1.39 GB × 1.2 = 1.7 GB, which fits 8 GB, well inside the 16 GB line. Runtimes 10, Released llama.cpp has the lfm3 architecture with the LFM3 vision and audio projectors and speech output (src/models/lfm3.cpp and tools/mtmd at b12242, with a realtime client example), and Ollama's library has an lfm3-omni 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-omni-2b among its models, so the publisher hosts it. 30 + 10 + 15 + 10 + 10 + 5 = 80.\n\n### Agent interface: LFM3-Omni-2B +50\n\n- Cola2 (20): Chat template 20, A chat template in tokenizer_config.json (4,116 characters), which came with the Qwen3 tokenizer. Tool-calling format 0, none. Trained tool calling 0, not trained for tool calling. Structured output 0, Nothing about JSON or schema-constrained output. Reasoning control 0, no reasoning mode. Context 0, no context length is stated. 20 + 0 + 0 + 0 + 0 + 0 = 20.\n- LFM3-Omni-2B (70): Chat template 20, chat_template.jinja in the repository (13,513 characters), with roles, images, video and audio, tools, deferred tools and tool results. Tool-calling format 25, the card and Liquid's tool-use page document tools passed through `tools=` in apply_chat_template, calls returned as XML and results sent back as tool messages. Trained tool calling 20, the template loops over every entry in an assistant turn's tool_calls, so several calls fit in one turn, and the card documents tool calling from spoken or written requests. Structured output 0, the card doesn't mention JSON or structured output for this model. Reasoning control 0, the card says this is a non-reasoning model and that `enable_thinking=True` is not supported, so there is no thinking to turn off or budget. Context 5, 32K tokens of input and output per the card. 20 + 25 + 20 + 0 + 0 + 5 = 70.\n\n### Documentation and upkeep: Cola2 +9\n\n- Cola2 (58): Model card 8, Intended use and limitations (the out-of-scope list, and the unconverged latent model) and the architecture are on the card and the GitHub model card it links. Training data 15, the GitHub model card names the source (OLMo 2 pretraining text, retokenised) and the mix by stage with token counts, 346.03B in all. Last change 20, All six commits are from 6 November 2026, four days before this check, the last one updating a checksum in the README. Responsiveness 0, no discussions on the repository, so nobody has asked anything yet and the line reads as none answered, as settled in calibration. Version policy 5, A first release. File health 10, Config, files and card agree: F32 weights, a float32 config and a card that says to load them as bfloat16, with SHA256SUMS to check the download. 8 + 15 + 20 + 0 + 5 + 10 = 58.\n- LFM3-Omni-2B (49): Model card 14, Intended use (one on-device model in place of separate speech recognition, vision, language and speech models), architecture, parameters (2.17B), context (32K) and the text and speech languages. 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 28 October, documented video frame rate and frame limits. Responsiveness 0, none of the 12 discussions that aren't pull requests (27 October to 9 November 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. 14 + 5 + 20 + 0 + 5 + 5 = 49.\n\n## Score by category\n\n| Category | Weight | Cola2 | LFM3-Omni-2B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 80 | LFM3-Omni-2B +55 |\n| Risk | 20% (20 this run) | 40 | 40 | even |\n| Agent interface | 10% (10 this run) | 20 | 70 | LFM3-Omni-2B +50 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 49 | Cola2 +9 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **33.9 · F** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Cola2 | LFM3-Omni-2B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | ByteDance (Seed) | 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 | None stated for the weights (the code is 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 | 2026-11-06 | 2026-11-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Cola2.** A research release, and an honest one: ByteDance Seed says it's a base model with an unconverged latent stage, scoring below Llama-3-8B on its own table. It's here for people studying continuous-latent generation. Agents can't use it as a model: 32-token completions, no chat or tools, the repository's own code to load it, and no licence on the weights.\n\n**LFM3-Omni-2B.** One 2.17B model that sees, listens and talks on an 8 GB machine and turns spoken requests into tool calls. Only llama.cpp and Ollama run it in a release. The context is 32K, it doesn't reason, Liquid hasn't answered a question on the repository, and there are no safety results or data sources.\n\n## Before you call either\n\n### Cola2\n\n1. Don't plan to use this as your model. It completes short prompts and wasn't tuned to follow instructions\n2. Don't use it commercially on the strength of this listing. The weights have no stated licence\n3. If you study it, read modeling_cola2.py first: trust_remote_code runs it\n4. Pin Transformers below 5, as the card requires\n5. Pass a seeded torch.Generator to generate(). Output varies between runs even with greedy decoding\n6. Check the download with `sha256sum -c SHA256SUMS`\n\n### LFM3-Omni-2B\n\n1. Start it with `llama-server -hf LiquidAI/LFM3-Omni-2B-GGUF:Q4_K_M --jinja` or `ollama run lfm3-omni`\n2. Greedy decoding for text. For speech use audio_temperature 0.4 and audio_repetition_penalty 1.2, per the card\n3. The context is 32K, and llama.cpp samples video at 2 frames a second up to 384 frames\n4. Parse tool calls yourself from the XML it returns\n5. It doesn't reason, so don't pass enable_thinking\n6. Validate JSON output yourself\n\n## Questions\n\n### Which is better for AI agents, Cola2 or LFM3-Omni-2B?\n\nLFM3-Omni-2B scores 61 (C) on agent readiness against Cola2's 33.9 (F), and leads in 2 of 4 scored categories. Cola2 leads on documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cola2\", \"b\": \"lfm3-omni-2b\"}`. 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