{ "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": "Ornith-1.5-397B scores 59 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.", "b": { "slug": "ornith-1-5-397b", "name": "Ornith-1.5-397B", "vendor": "Ornith", "vendorUrl": "https://ornith.ai", "kind": "weights", "category": "open-weights", "summary": "Ornith-1.5-397B is Ornith's flagship open-weight mixture-of-experts reasoning model for coding and agent work, about 397B parameters on Qwen3.5's architecture with a 262,144-token context, published on Hugging Face in BF16, FP8, NVFP4 and GGUF and marked MIT.", "url": "https://www.anchorterminal.com/tools/ornith-1-5-397b", "markdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.json", "dns": "ornith-1-5-397b.grade.anchorterminal.com", "repo": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-FP8" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-NVFP4" }, { "registry": "huggingface", "name": "ornith-ai/Ornith-1.5-397B-GGUF" }, { "registry": "ollama", "name": "ornith-1.5:397b" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and Ornith's FP8, NVFP4 and GGUF builds are 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 807 GB for the BF16 files, 418 GB for FP8 or 244 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": "2026-11-04" }, "docsUrl": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "coding", "mit", "safetensors", "gguf", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-08-23", "graded": true, "anchor": { "graded": true, "score": 59, "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": 77, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-04" }, "negative": 0, "verdict": "Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.", "bestFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "strengths": [ "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama from their released versions", "Ornith publishes FP8, NVFP4 and four GGUF precisions, all ungated", "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 code in the repository" ], "weaknesses": [ "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine", "No LICENSE file though the card links one, and no notice for the Apache 2.0 Qwen3.5 and Gemma 4 work it builds on", "No safety evaluation, agent-use guidance or security contact", "Every GGUF and the NVFP4 shards were replaced in place in August 2026 with no note, and the NVFP4 build now holds eight shards its index doesn't reference", "The two chat templates disagree on keeping earlier reasoning, structured output isn't documented and the card doesn't give the active parameters" ], "agentNotes": [ "Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory", "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", "When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards", "Pin a revision when you download, since Ornith has replaced files in place", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 59 } ], "editorialScores": { "interface": 85, "reach": 65, "risk": 50, "upkeep": 36 }, "provenanceScore": 19, "checked": { "categories": { "interface": "2026-11-04", "reach": "2026-11-04", "risk": "2026-11-04", "upkeep": "2026-11-04" }, "last": "2026-11-04", "oldest": "2026-11-04", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run ornith-1.5:397b\n# or, with the card's vLLM command on eight GPUs:\nvllm serve ornith-ai/Ornith-1.5-397B --served-model-name Ornith-1.5-397B --tensor-parallel-size 8 --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-1.5:397b\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/ornith-1-5-397b" }, "sameCompany": [ "ornith-1-5-122b", "ornith-1-5-35b-a3b", "ornith-1-5-9b" ], "weights": { "trainedBy": "Ornith", "builtOn": "Qwen3.5-397B-A17B, per the card's text (Ornith-1.0, which Ornith says it built on Qwen3.5 and Gemma 4, extended in 1.5); not declared in the metadata. Same qwen3_5_moe architecture and an identical parameter count to Qwen3.5-397B-A17B (Apache 2.0)", "params": "397B", "context": 262144, "goodFor": "Flagship coding and terminal-agent mixture-of-experts model with tool calling and thinking on by default, text and images in, for a multi-GPU server.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ornith-1.5-397B-FP8", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 418.4, "memoryGB": 512, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-NVFP4", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 319.5, "memoryGB": 512, "format": "safetensors", "note": "NVFP4 needs NVIDIA Blackwell. The repository's files sum to 319.5 GB, but its index uses only the 25-shard set (241.7 GB); eight further shards uploaded on 20 October 2026 aren't in the index. Either way it needs 512 GB" }, { "name": "Ornith-1.5-397B-Q6_K", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 331, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q5_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q5_K_M.gguf", "who": "publisher", "precision": "q5_k_m", "sizeGB": 286.3, "memoryGB": 512, "format": "gguf", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q4_K_M", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 244.3, "memoryGB": 512, "format": "gguf", "note": "Add mmproj-Ornith-1.5-397B-BF16.gguf (0.9 GB) for image input. Too big for the 128 GB reference machine" }, { "name": "ollama ornith-1.5:397b", "url": "https://ollama.com/library/ornith-1.5:397b", "who": "ollama", "precision": "q4_k_m", "sizeGB": 242, "memoryGB": 512, "format": "ollama", "runsOn": [ "Ollama" ], "note": "Q4_K_M (241 GB) with a BF16 vision projector (922 MB); the library has no latest tag. Size as Ollama's tags page shows it, rounded to whole GB above 10 GB. Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B", "who": "publisher", "precision": "bf16", "sizeGB": 806.8, "memoryGB": 1024, "format": "safetensors", "note": "Too big for the 128 GB reference machine" }, { "name": "Ornith-1.5-397B-Q8_0", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-GGUF/blob/main/Ornith-1.5-397B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 428.5, "memoryGB": 1024, "format": "gguf", "note": "Too big for the 128 GB reference machine" } ], "run": "ollama run ornith-1.5:397b", "runBuild": "ollama ornith-1.5:397b", "checked": "2026-10-21", "minMemoryGB": 512 }, "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", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-9B", "params": "9B (dense)", "listing": "https://www.anchorterminal.com/tools/ornith-1-5-9b" }, { "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-DFlash", "url": "https://huggingface.co/ornith-ai/Ornith-1.5-397B-DFlash", "params": "1.29B (Hub safetensors count)" } ], "unrestrictedCopies": 4 }, "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 citation reads Ornith Team, and the Hugging Face organisation ornith-ai (team plan, 10 members) 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, deletions 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." ], "score": 19 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/ornith-1-5-397b.json", "live": { "slug": "ornith-1-5-397b", "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": 65, "category": "interface", "leads": "ornith-1-5-397b", "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, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native." }, "scores": { "a": 20, "b": 85 } }, { "by": 40, "category": "reach", "leads": "ornith-1-5-397b", "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 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF 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 no inference provider serves it." }, "scores": { "a": 25, "b": 65 } }, { "by": 22, "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 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 20, Ornith uploaded new shards to the NVFP4 build on 20 October 2026. Responsiveness 0, nobody from Ornith answered either of the 2 questions, or any of the six on its GGUF and NVFP4 builds. Version policy 0, every GGUF file and the NVFP4 shards were replaced in place in August with no note. File health 0, the card links a missing LICENSE, the two chat templates differ, and the NVFP4 build carries shards its index doesn't use." }, "scores": { "a": 58, "b": 36 } }, { "by": 10, "category": "risk", "leads": "ornith-1-5-397b", "name": "Risk", "reasons": { "a": "File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.", "b": "File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, 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": "ByteDance (Seed)", "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": "None stated for the weights (the code is Apache 2.0)", "b": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "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": "2/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Ornith-1.5-397B scores 59 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 of 4 scored categories. Cola2 leads on documentation and upkeep.", "question": "Which is better for AI agents, Cola2 or Ornith-1.5-397B?" } ], "goodFor": [ { "aheadOn": [ "Documentation and upkeep, 58 against 36" ], "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, 65 against 25", "Risk, 50 against 40", "Agent interface, 85 against 20" ], "also": null, "goodFor": "A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required", "slug": "ornith-1-5-397b", "watchFor": "Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine" } ], "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": 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"Ornith-1.5-397B vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2.json", "title": "Ornith-1.5-397B vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b.json", "title": "Ornith-1.5-397B vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b.json", "title": "Ornith-1.5-397B vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b.json", "title": "Ornith-1.5-397B vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 40, "cola2": 25, "edge": "ornith-1-5-397b", "key": "reach", "name": "Reach", "ornith-1-5-397b": 65, "weight": 25 }, { "by": 10, "cola2": 40, "edge": "ornith-1-5-397b", "key": "risk", "name": "Risk", "ornith-1-5-397b": 50, "weight": 20 }, { "by": 65, "cola2": 20, "edge": "ornith-1-5-397b", "key": "interface", "name": "Agent interface", "ornith-1-5-397b": 85, "weight": 10 }, { "by": 22, "cola2": 58, "edge": "cola2", "key": "upkeep", "name": "Documentation and upkeep", "ornith-1-5-397b": 36, "weight": 10 } ], "summary": "Ornith-1.5-397B scores 59 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 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.", "ornith-1-5-397b": "Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b", "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.min.md" }, "markdown": "Ornith-1.5-397B scores 59 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 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- Ornith-1.5-397B: grade C, 59/100, rank #100 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/ornith-1-5-397b.md · JSON https://www.anchorterminal.com/api/v1/tools/ornith-1-5-397b.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 36\n\nWatch for: No licence stated for the weights\n\n### Ornith-1.5-397B (C)\n\nGood for: A self-hosted frontier-scale coding agent on an 8-GPU server, where a clear licence file and an answering publisher aren't required\n\nAhead on:\n- Reach, 65 against 25\n- Risk, 50 against 40\n- Agent interface, 85 against 20\n\nWatch for: Needs about 290 GB of memory at 4 bits and about 970 GB in BF16, so it doesn't fit the reference machine\n\n\n## Why the edge\n\n### Agent interface: Ornith-1.5-397B +65\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- Ornith-1.5-397B (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card shows how tools go in and how calls come back and names the parser for vLLM and SGLang. Trained tool calling 20, Ornith trains it on agent tasks and the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, Ornith's template turns thinking off per request with enable_thinking set to false and the card says the reasoning comes back in reasoning_content, though the card doesn't name the switch. Context 10, 262,144 tokens native.\n\n### Reach: Ornith-1.5-397B +40\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- Ornith-1.5-397B (65): Memory 0, since the smallest 4-bit build, Ollama's at 242 GB, needs about 290 GB with the fifth added, above the 256 GB band, and Ornith's own Q4_K_M GGUF and NVFP4 builds need about the same. Runtimes 25, the most the line gives, from vLLM 0.19.1, SGLang 0.5.9, Transformers 5.8.1, llama.cpp's qwen35moe architecture and Ollama's library, while LM Studio's entry is the base's. Builds 15, Ornith publishes FP8, NVFP4 and GGUF 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 no inference provider serves it.\n\n### Documentation and upkeep: Cola2 +22\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- Ornith-1.5-397B (36): 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 20, Ornith uploaded new shards to the NVFP4 build on 20 October 2026. Responsiveness 0, nobody from Ornith answered either of the 2 questions, or any of the six on its GGUF and NVFP4 builds. Version policy 0, every GGUF file and the NVFP4 shards were replaced in place in August with no note. File health 0, the card links a missing LICENSE, the two chat templates differ, and the NVFP4 build carries shards its index doesn't use.\n\n### Risk: Ornith-1.5-397B +10\n\n- Cola2 (40): File safety 15, safetensors only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the ByteDance-Seed organisation, which the ByteDance-Seed GitHub repository for the model links to (10). Safety evaluation 0, no safety results. Agent guidance 5, General cautions in the GitHub model card: not instruction-tuned or safety-aligned, not for use as an assistant, for facts or advice, or for high-impact decisions about people. Licence risk 0, With no licence for the weights there is nothing to hold ByteDance to. Security programme 5, the GitHub repository's SECURITY.md sends reports to ByteDance's security centre and says how they're assessed (5). 15 + 15 + 0 + 5 + 0 + 5 = 40.\n- Ornith-1.5-397B (50): File safety 25, safetensors and GGUF only and no Python in any of Ornith's repositories for this size, 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 | Cola2 | Ornith-1.5-397B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 25 | 65 | Ornith-1.5-397B +40 |\n| Risk | 20% (20 this run) | 40 | 50 | Ornith-1.5-397B +10 |\n| Agent interface | 10% (10 this run) | 20 | 85 | Ornith-1.5-397B +65 |\n| Documentation and upkeep | 10% (10 this run) | 58 | 36 | Cola2 +22 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **33.9 · F** | **59 · C** | |\n\n## Facts side by side\n\n| Fact | Cola2 | Ornith-1.5-397B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | ByteDance (Seed) | 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 | None stated for the weights (the code is Apache 2.0) | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 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 | 2/5 (4) |\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**Ornith-1.5-397B.** Ornith's largest coding and terminal-agent model, with XML tool calls that vLLM and SGLang parse and 262,144 tokens of context. It needs about 290 GB even at 4 bits, so a multi-GPU server, not the 128 GB reference machine. The main risks are a missing licence file, no safety results, no publisher replies and files replaced without a note.\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### Ornith-1.5-397B\n\n1. Start it with the card's vLLM command at `--tensor-parallel-size 8`, or `ollama run ornith-1.5:397b` on a machine with about 300 GB of memory\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. When downloading the NVFP4 build, fetch only the files its model.safetensors.index.json names (the 25-shard set, 241.7 GB), not the eight extra shards\n6. Pin a revision when you download, since Ornith has replaced files in place\n7. Validate JSON output yourself, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Cola2 or Ornith-1.5-397B?\n\nOrnith-1.5-397B scores 59 (C) on agent readiness against Cola2's 33.9 (F), and leads in 3 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-ornith-1-5-397b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"cola2\", \"b\": \"ornith-1-5-397b\"}`. 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