{ "data": { "a": { "slug": "apodex-1-1-mini", "name": "Apodex-1.1-mini", "vendor": "Apodex AI", "vendorUrl": "https://www.apodex.com", "kind": "weights", "category": "open-weights", "summary": "Apodex-1.1-mini is Apodex AI's open-weight mixture-of-experts model for long-horizon agent work with files, data, code and tools, a fine-tune of Qwen3.5-35B-A3B with about 36B parameters, about 3B active per token and a 262,144-token context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/apodex-1-1-mini", "markdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json", "dns": "apodex-1-1-mini.grade.anchorterminal.com", "repo": "https://huggingface.co/apodex/Apodex-1.1-mini", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-FP8" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-NVFP4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GPTQ-Int4" }, { "registry": "huggingface", "name": "apodex/Apodex-1.1-mini-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of Apodex's build repositories are not gated (checked 2026-10-21). Apodex's hosted API needs an account and key.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 (the repository's LICENSE file). Running it yourself costs the hardware, about 72 GB for the BF16 files or 22.6 GB for Apodex's Q4_K_M GGUF with its vision projector. Apodex's own API serves it as apodex-1.1-mini at $0.10 per million input tokens, $0.01 cached and $1.00 output, doubled for requests over 200K input tokens (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. Apodex's hosted API bills by account (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/apodex/Apodex-1.1-mini", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "nvfp4", "gptq", "self-hosted" ], "lastRelease": "2026-08-25", "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": 31, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 85, "reach": 85, "risk": 65, "upkeep": 38 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "bestFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "strengths": [ "The card documents the tool-call format, the parsers for SGLang and vLLM and a full agent loop, with parallel calls", "Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF builds, the 4-bit ones in 32 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 MoE", "Apache 2.0 through the chain, with the base's licence file carried and no gate", "Apodex's own API serves the same model", "Safetensors and GGUF only, with checksums on every weight file" ], "weaknesses": [ "No safety results, and agent guidance only from the harness", "Apodex answered none of the four discussions", "The card states no parameters, architecture, limitations or knowledge cut-off", "The template adds an Apodex identity block when no system prompt is sent and keeps earlier reasoning by default", "Structured output isn't documented" ], "agentNotes": [ "Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed", "For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file", "Pass tools through the API's tools parameter rather than in the system prompt, as the card asks", "The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768", "Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block", "The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "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": 85, "risk": 65, "upkeep": 38 }, "provenanceScore": 47, "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": "llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek -c 32768\n# or, with the card's vLLM command:\nvllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice \\\n --tool-call-parser qwen3_coder --reasoning-parser qwen3", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"apodex/Apodex-1.1-mini\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/apodex-1-1-mini" }, "weights": { "trainedBy": "Apodex AI", "builtOn": "Qwen/Qwen3.5-35B-A3B (Apache 2.0), a fine-tune, as declared in the metadata (base_model, relation finetune). Same config and the same parameter count, 35,951,822,704", "params": "35B (3B active)", "active": "3B", "context": 262144, "goodFor": "Mixture-of-experts agent model for long-horizon work with files, data, code and tools, fine-tuned from Qwen3.5-35B-A3B, with documented tool calling, parallel calls and thinking on by default.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Apodex-1.1-mini-NVFP4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 24.3, "memoryGB": 32, "format": "safetensors", "note": "NVFP4 (modelopt_mixed) needs NVIDIA Blackwell; the card serves it with SGLang" }, { "name": "Apodex-1.1-mini-GPTQ-Int4", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GPTQ-Int4", "who": "publisher", "precision": "int4", "sizeGB": 24.7, "memoryGB": 32, "format": "safetensors", "runsOn": [ "SGLang" ], "note": "The card serves it with SGLang" }, { "name": "apodex1.1-35b-Q4_K_M-MTP", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 22.6, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "21.7 GB of weights with the multi-token prediction layer and a 0.9 GB F16 vision projector; the card passes the repository's chat_template.jinja to llama-server" }, { "name": "Apodex-1.1-mini-FP8", "url": "https://huggingface.co/apodex/Apodex-1.1-mini-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 37.5, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Apodex-1.1-mini", "url": "https://huggingface.co/apodex/Apodex-1.1-mini", "who": "publisher", "precision": "bf16", "sizeGB": 71.9, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)" ] } ], "run": "vllm serve apodex/Apodex-1.1-mini --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3", "runBuild": "Apodex-1.1-mini", "checked": "2026-10-21", "minMemoryGB": 32 }, "family": { "lab": "Apodex AI (apodex)", "family": "Apodex-1", "url": "https://www.anchorterminal.com/open-weights/#apodex", "others": [ { "name": "Apodex-1.0-mini", "url": "https://huggingface.co/apodex/Apodex-1.0-mini", "params": "35B total, 3B active (MoE; 35,951,822,704 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-4B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-4B-SFT", "params": "4B (4,659,865,088 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-2B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-2B-SFT", "params": "2B (2,274,069,824 by the Hub's safetensors count)" }, { "name": "Apodex-1.0-0.8B-SFT", "url": "https://huggingface.co/apodex/Apodex-1.0-0.8B-SFT", "params": "0.8B (873,438,784 by the Hub's safetensors count)" } ], "unrestrictedCopies": 2 }, "area": "models", "provenance": { "legalEntity": "Apodex (the site's copyright line reads 2026 Apodex)", "domain": "apodex.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "apodex.com links to the apodex organisation on Hugging Face, and the card links back to apodex.com, apodex.ai, platform.apodex.ai and github.com/ApodexAI/FrontierAgent. Every commit is by one account, Eric-LRL-130, an apodex member.", "Nothing is graded as hosted, so the licence stands in for terms of service and no privacy policy is scored. Apodex's API has its own terms, not read here.", "No changelog for the weights. The FrontierAgent harness keeps a CHANGELOG.", "apodex.com and apodex.ai return 404 for /.well-known/security.txt. FrontierAgent's SECURITY.md gives a reporting address and timeline for the harness.", "unchecked: the domain's registration date." ], "score": 47 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/apodex-1-1-mini.json", "live": { "slug": "apodex-1-1-mini", "securityTxt": { "url": "https://apodex.com/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:09:39.653671365Z" }, "domain": { "domain": "apodex.com", "registered": "2018-10-10", "source": "https://rdap.verisign.com/com/v1/domain/apodex.com", "checkedAt": "2026-11-08T14:48:24.412328563Z" }, "updatedAt": "2026-11-10T16:09:39.653671365Z" } }, "answer": "Apodex-1.1-mini scores 61 (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": "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" }, "edges": [ { "by": 65, "category": "interface", "leads": "apodex-1-1-mini", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.", "b": "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." }, "scores": { "a": 85, "b": 20 } }, { "by": 60, "category": "reach", "leads": "apodex-1-1-mini", "name": "Reach", "reasons": { "a": "Memory 20, Apodex's Q4_K_M GGUF with its vision projector is 22.6 GB and needs about 27.1 GB with the fifth added, so 32 GB, as do its NVFP4 and GPTQ-Int4 builds. Runtimes 20, the Qwen3.5 MoE architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 5, Apodex's own API serves it.", "b": "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." }, "scores": { "a": 85, "b": 25 } }, { "by": 25, "category": "risk", "leads": "apodex-1-1-mini", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.", "b": "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." }, "scores": { "a": 65, "b": 40 } }, { "by": 20, "category": "upkeep", "leads": "cola2", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.", "b": "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." }, "scores": { "a": 38, "b": 58 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Apodex AI", "b": "ByteDance (Seed)", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "None stated for the weights (the code is Apache 2.0)", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-08-25", "b": "2026-11-06", "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": "3.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Apodex-1.1-mini scores 61 (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, Apodex-1.1-mini or Cola2?" } ], "goodFor": [ { "aheadOn": [ "Reach, 85 against 25", "Risk, 65 against 40", "Agent interface, 85 against 20" ], "also": null, "goodFor": "Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence", "slug": "apodex-1-1-mini", "watchFor": "No safety results, and agent guidance only from the harness" }, { "aheadOn": [ "Documentation and upkeep, 58 against 38" ], "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" } ], "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-apodex-1-1-mini.json", "title": "Agents-A1-4B vs Apodex-1.1-mini", "url": 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"https://www.anchorterminal.com/compare/cola2-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Cola2 vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/cola2-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3.json", "title": "Cola2 vs Kimi K3", "url": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro.json", "title": "Cola2 vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/cola2-vs-kimi-k3-1-pro" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-kolibri-1.json", "title": "Cola2 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/cola2-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-kronos.json", "title": "Cola2 vs Kronos", "url": "https://www.anchorterminal.com/compare/cola2-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-m-1.json", "title": "Cola2 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1.json", "title": "Cola2 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-laguna-xs-2-1.json", "title": "Cola2 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/cola2-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-lbs-27b.json", "title": "Cola2 vs LBS-27B", "url": "https://www.anchorterminal.com/compare/cola2-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm2-5-2-6b.json", "title": "Cola2 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b.json", "title": "Cola2 vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-1-7b" }, { "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-lfm3-omni-2b.json", "title": "Cola2 vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b.json", "title": "Cola2 vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/cola2-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-0-tiny.json", "title": "Cola2 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019.json", "title": "Cola2 vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/cola2-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5.json", "title": "Cola2 vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/cola2-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Cola2 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl.json", "title": "Cola2 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/cola2-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-o-4-5.json", "title": "Cola2 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b.json", "title": "Cola2 vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b.json", "title": "Cola2 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minicpm5-2b.json", "title": "Cola2 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/cola2-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-5.json", "title": "Cola2 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7.json", "title": "Cola2 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m3-1-flash.json", "title": "Cola2 vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/cola2-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4.json", "title": "Cola2 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/cola2-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-muse-glimmer-30b.json", "title": "Cola2 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/cola2-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b.json", "title": "Cola2 vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-3-33b.json", "title": "Cola2 vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Cola2 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Cola2 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-30b-a3b.json", "title": "Cola2 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-4b.json", "title": "Cola2 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Cola2 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-super-120b-a12b.json", "title": "Cola2 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-ultra-550b-a55b.json", "title": "Cola2 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/cola2-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0.json", "title": "Cola2 vs North Mini Code 1.0", "url": "https://www.anchorterminal.com/compare/cola2-vs-north-mini-code-1-0" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-occamy-1-0.json", "title": "Cola2 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/cola2-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-olmo-3-7b-think.json", "title": "Cola2 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/cola2-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b.json", "title": "Cola2 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-35b-a3b.json", "title": "Cola2 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b.json", "title": "Cola2 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/cola2-vs-ornith-1-5-397b" }, { "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/cola2-vs-otel-2-0-llm-31b-it.json", "title": "Cola2 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/cola2-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b.json", "title": "Cola2 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Cola2 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-4b-instruct-2507.json", "title": "Cola2 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-0-8b.json", "title": "Cola2 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b.json", "title": "Cola2 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-2b.json", "title": "Cola2 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-397b-a17b.json", "title": "Cola2 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-4b.json", "title": "Cola2 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-9b.json", "title": "Cola2 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-6-27b.json", "title": "Cola2 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-6-35b-a3b.json", "title": "Cola2 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-8-27b.json", "title": "Cola2 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-8-flash-next.json", "title": "Cola2 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Cola2 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-coder-next.json", "title": "Cola2 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Cola2 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-2b-instruct.json", "title": "Cola2 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Cola2 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-32b-instruct.json", "title": "Cola2 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-4b-instruct.json", "title": "Cola2 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-8b-instruct.json", "title": "Cola2 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-qwopus3-8-27b-flash-v2.json", "title": "Cola2 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/cola2-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-recursive-flint.json", "title": "Cola2 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/cola2-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-ripple-65b-preview.json", "title": "Cola2 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/cola2-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-sensenova-loop-9b-preview.json", "title": "Cola2 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/cola2-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-smollm3-3b.json", "title": "Cola2 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/cola2-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-spark-x2-6-4b.json", "title": "Cola2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/cola2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash.json", "title": "Cola2 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-swift-1-5-qwen3-8-27b.json", "title": "Cola2 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/cola2-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Cola2 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/cola2-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-thinkingcap-qwen3-8-27b.json", "title": "Cola2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/cola2-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-unlimited-ocr-2.json", "title": "Cola2 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/cola2-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b.json", "title": "Cola2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b.json", "title": "Cola2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/cola2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/cola2-vs-zaya1-8b.json", "title": "Cola2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/cola2-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apodex-1-1-mini": 85, "by": 60, "cola2": 25, "edge": "apodex-1-1-mini", "key": "reach", "name": "Reach", "weight": 25 }, { "apodex-1-1-mini": 65, "by": 25, "cola2": 40, "edge": "apodex-1-1-mini", "key": "risk", "name": "Risk", "weight": 20 }, { "apodex-1-1-mini": 85, "by": 65, "cola2": 20, "edge": "apodex-1-1-mini", "key": "interface", "name": "Agent interface", "weight": 10 }, { "apodex-1-1-mini": 38, "by": 20, "cola2": 58, "edge": "cola2", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Apodex-1.1-mini scores 61 (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": { "apodex-1-1-mini": "An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2", "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.min.md" }, "markdown": "Apodex-1.1-mini scores 61 (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- Apodex-1.1-mini: grade C, 61/100, rank #41 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/apodex-1-1-mini.md · JSON https://www.anchorterminal.com/api/v1/tools/apodex-1-1-mini.json\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- 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### Apodex-1.1-mini (C)\n\nGood for: Long-running agent tasks with tools on a 32 GB GPU or Mac, or through Apodex's API, under a plain licence\n\nAhead on:\n- Reach, 85 against 25\n- Risk, 65 against 40\n- Agent interface, 85 against 20\n\nWatch for: No safety results, and agent guidance only from the harness\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 38\n\nWatch for: No licence stated for the weights\n\n\n## Why the edge\n\n### Agent interface: Apodex-1.1-mini +65\n\n- Apodex-1.1-mini (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, the card says how tools go in and come back and names the parser for SGLang and vLLM, with a full example. Trained tool calling 20, trained for native function calling with parallel calls. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card says the reasoning comes back in reasoning_content. Context 10, 262,144 tokens.\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\n### Reach: Apodex-1.1-mini +60\n\n- Apodex-1.1-mini (85): Memory 20, Apodex's Q4_K_M GGUF with its vision projector is 22.6 GB and needs about 27.1 GB with the fifth added, so 32 GB, as do its NVFP4 and GPTQ-Int4 builds. Runtimes 20, the Qwen3.5 MoE architecture runs in released vLLM, SGLang, Transformers and llama.cpp, while Ollama's and LM Studio's entries are the base's, not this model's. Builds 15, Apodex publishes FP8, NVFP4, GPTQ-Int4 and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 5, Apodex's own API serves it.\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\n### Risk: Apodex-1.1-mini +25\n\n- Apodex-1.1-mini (65): File safety 25, safetensors and GGUF only and nothing from the repositories runs. Provenance 15, the files are in the apodex organisation its site links to, with a SHA-256 for every weight file and a SHA256SUMS file for the GGUF. Safety evaluation 0, none published. Agent guidance 5, the recommended harness documents approval gates and a sandbox boundary, but nothing on prompt injection or refusals for the model. Licence risk 15, Apache 2.0 from Qwen3.5-35B-A3B through to this model with no conditions added. Security programme 5, the harness has a disclosure policy with a private address, with no route shown for the weights.\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\n### Documentation and upkeep: Cola2 +20\n\n- Apodex-1.1-mini (38): Model card 8, it covers intended use, context and languages but not the architecture, parameters, limitations or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 15, the GGUF build went up on 10 September 2026, 54 days before 3 November. Responsiveness 0, Apodex answered none of the four discussions, unchanged on 3 November 2026. Version policy 5, 1.0-mini stays in its own repository without a note. File health 5, the files agree, but the card's agent example is broken and reports wait without a reply.\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\n## Score by category\n\n| Category | Weight | Apodex-1.1-mini | Cola2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 85 | 25 | Apodex-1.1-mini +60 |\n| Risk | 20% (20 this run) | 65 | 40 | Apodex-1.1-mini +25 |\n| Agent interface | 10% (10 this run) | 85 | 20 | Apodex-1.1-mini +65 |\n| Documentation and upkeep | 10% (10 this run) | 38 | 58 | Cola2 +20 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **33.9 · F** | |\n\n## Facts side by side\n\n| Fact | Apodex-1.1-mini | Cola2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Apodex AI | ByteDance (Seed) |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | None stated for the weights (the code is Apache 2.0) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-08-25 | 2026-11-06 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.3/5 (4) | none |\n\n## Verdicts\n\n**Apodex-1.1-mini.** An agent cousin fine-tuned from Qwen3.5-35B-A3B for long tasks with files, code and tools, with parallel tool calling and about 3B active parameters. It needs 32 GB for Apodex's GGUF, NVFP4 or GPTQ-Int4 builds or about 86 GB in BF16, and Apodex's API serves it too. The main risks are no safety results and no replies from the publisher.\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## Before you call either\n\n### Apodex-1.1-mini\n\n1. Serve it with `vllm serve apodex/Apodex-1.1-mini --enable-auto-tool-choice --tool-call-parser qwen3_coder --reasoning-parser qwen3`, or the same parsers in SGLang, so tool calls and reasoning come back parsed\n2. For llama.cpp, `llama-server -hf apodex/Apodex-1.1-mini-GGUF:Q4_K_M --jinja --reasoning-format deepseek`, and the card passes the repository's chat_template.jinja with --chat-template-file\n3. Pass tools through the API's tools parameter rather than in the system prompt, as the card asks\n4. The card recommends temperature 1.0, top_p 0.95, repetition_penalty 1.05 and max_tokens up to 32,768\n5. Send your own system prompt, or set identity_mode, since the template otherwise adds an Apodex identity block\n6. The template keeps all earlier reasoning in the context unless `preserve_thinking` is false, which grows long agent sessions\n7. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n8. The card's Python example has a broken line where it runs the tool (available_toolsname), so fix it to `available_tools[name](**args)` before running\n9. Validate JSON output yourself, since structured output isn't documented\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## Questions\n\n### Which is better for AI agents, Apodex-1.1-mini or Cola2?\n\nApodex-1.1-mini scores 61 (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/apodex-1-1-mini-vs-cola2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-cola2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apodex-1-1-mini\", \"b\": \"cola2\"}`. 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