{ "data": { "a": { "slug": "agents-a1-4b", "name": "Agents-A1-4B", "vendor": "InternScience (Shanghai AI Laboratory)", "vendorUrl": "https://internscience.github.io/Agents-A1/", "kind": "weights", "category": "open-weights", "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/agents-a1-4b", "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json", "dns": "agents-a1-4b.grade.anchorterminal.com", "repo": "https://huggingface.co/InternScience/Agents-A1-4B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "InternScience/Agents-A1-4B" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-FP8" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-Q8_0-GGUF" }, { "registry": "huggingface", "name": "InternScience/Agents-A1-4B-F16-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 InternScience's build repositories for the 4B are not gated (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/InternScience/Agents-A1-4B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "small", "multimodal", "reasoning", "tool-calling", "agentic", "derivative", "apache-2.0", "safetensors", "gguf", "fp8", "self-hosted" ], "lastRelease": "2026-07-14", "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": 29, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.", "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence", "strengths": [ "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB", "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model", "FP8 and three GGUF precisions from the publisher", "Apache 2.0 through the chain, with no gate", "Trained for tool calling, several calls a turn, and 262,144 tokens of context", "An evaluation framework for its agent benchmarks is published on GitHub" ], "weaknesses": [ "The base isn't declared, and the technical report doesn't cover the 4B", "No safety results, agent guidance or security contact", "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14", "Tool calling and structured output aren't documented beyond a parser name", "InternScience answered one of three discussions, and the history was squashed" ], "agentNotes": [ "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector", "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work", "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14", "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them", "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention", "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 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": 70, "reach": 90, "risk": 55, "upkeep": 43 }, "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 InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/agents-a1-4b" }, "weights": { "trainedBy": "InternScience (Shanghai AI Laboratory)", "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B", "params": "4.5B", "context": 262144, "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Agents-A1-4B-Q8_0", "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 5.2, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Includes the 0.7 GB vision projector" }, { "name": "Agents-A1-4B-Q4_K_M", "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 3.4, "memoryGB": 8, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "2.7 GB of weights and the 0.7 GB vision projector" }, { "name": "Agents-A1-4B", "url": "https://huggingface.co/InternScience/Agents-A1-4B", "who": "publisher", "precision": "bf16", "sizeGB": 9.1, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ] }, { "name": "Agents-A1-4B-FP8", "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 7.8, "memoryGB": 12, "format": "safetensors", "runsOn": [ "vLLM", "SGLang" ] }, { "name": "Agents-A1-4B-F16", "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF", "who": "publisher", "precision": "f16", "sizeGB": 9.1, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Includes the 0.7 GB vision projector" } ], "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder", "runBuild": "Agents-A1-4B", "checked": "2026-10-21", "minMemoryGB": 8 }, "family": { "lab": "InternScience (Shanghai AI Laboratory)", "family": "Agents-A1", "url": "https://www.anchorterminal.com/open-weights/#internscience", "others": [ { "name": "Agents-A1", "url": "https://huggingface.co/InternScience/Agents-A1", "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)" } ], "olderCount": 6, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template", "domain": "internscience.github.io", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.", "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.", "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)." ], "score": 47 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json", "live": { "slug": "agents-a1-4b", "securityTxt": { "url": "https://internscience.github.io/.well-known/security.txt", "state": "none", "checkedAt": "2026-11-10T16:07:46.042106985Z" }, "domain": { "domain": "github.io", "checkedAt": "2026-11-08T15:08:12.013832199Z" }, "updatedAt": "2026-11-10T16:07:46.042106985Z" } }, "answer": "Agents-A1-4B 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": "reach", "leads": "agents-a1-4b", "name": "Reach", "reasons": { "a": "Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added. Runtimes 20, the Qwen3.5 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, InternScience publishes FP8 and three GGUF precisions. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, one inference provider serves it and the line needs two.", "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": 90, "b": 25 } }, { "by": 50, "category": "interface", "leads": "agents-a1-4b", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser but the format is only in the template. Trained tool calling 20, the card says it was trained for tool use and the template renders several calls in one turn. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card's reasoning parser returns it apart from the answer. 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": 70, "b": 20 } }, { "by": 15, "category": "risk", "leads": "agents-a1-4b", "name": "Risk", "reasons": { "a": "File safety 25, safetensors and GGUF only and nothing from the repository runs. Provenance 15, the files are in the InternScience organisation its GitHub repository links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 through the chain from Qwen3.5-4B with no conditions added. Security programme 0, no policy, contact or security.txt.", "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": 55, "b": 40 } }, { "by": 15, "category": "upkeep", "leads": "cola2", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 10, the files went up on 14 July 2026, 112 days before 3 November 2026, and the GitHub repository's last commit is 16 July. Responsiveness 10, InternScience answered one of the three discussions, unchanged on 3 November 2026. Version policy 5, a first release. File health 5, the files agree on the essentials, but the config declares a prediction layer that isn't shipped and the template carries a dated default prompt.", "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": 43, "b": 58 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "InternScience (Shanghai AI Laboratory)", "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": "2026-07-14", "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": "3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Agents-A1-4B 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, Agents-A1-4B or Cola2?" } ], "goodFor": [ { "aheadOn": [ "Reach, 90 against 25", "Risk, 55 against 40", "Agent interface, 70 against 20" ], "also": null, "goodFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence", "slug": "agents-a1-4b", "watchFor": "The base isn't declared, and the technical report doesn't cover the 4B" }, { "aheadOn": [ "Documentation and upkeep, 58 against 43" ], "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-apertus-v1-5-8b.json", "title": "Agents-A1-4B vs Apertus v1.5 8B", "url": 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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 }, { "agents-a1-4b": 90, "by": 65, "cola2": 25, "edge": "agents-a1-4b", "key": "reach", "name": "Reach", "weight": 25 }, { "agents-a1-4b": 55, "by": 15, "cola2": 40, "edge": "agents-a1-4b", "key": "risk", "name": "Risk", "weight": 20 }, { "agents-a1-4b": 70, "by": 50, "cola2": 20, "edge": "agents-a1-4b", "key": "interface", "name": "Agent interface", "weight": 10 }, { "agents-a1-4b": 43, "by": 15, "cola2": 58, "edge": "cola2", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Agents-A1-4B 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": { "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.", "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/agents-a1-4b-vs-cola2", "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.min.md" }, "markdown": "Agents-A1-4B 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- Agents-A1-4B: grade C, 61/100, rank #39 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.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### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Reach, 90 against 25\n- Risk, 55 against 40\n- Agent interface, 70 against 20\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\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 43\n\nWatch for: No licence stated for the weights\n\n\n## Why the edge\n\n### Reach: Agents-A1-4B +65\n\n- Agents-A1-4B (90): Memory 30, InternScience's Q4_K_M GGUF with its vision projector is 3.4 GB and needs about 4.1 GB with the fifth added. Runtimes 20, the Qwen3.5 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, InternScience publishes FP8 and three GGUF precisions. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, one inference provider serves it and the line needs two.\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### Agent interface: Agents-A1-4B +50\n\n- Agents-A1-4B (70): Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card names the qwen3_coder parser but the format is only in the template. Trained tool calling 20, the card says it was trained for tool use and the template renders several calls in one turn. Structured output 0. Reasoning control 10, the template turns thinking off per request and the card's reasoning parser returns it apart from the answer. 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### Risk: Agents-A1-4B +15\n\n- Agents-A1-4B (55): File safety 25, safetensors and GGUF only and nothing from the repository runs. Provenance 15, the files are in the InternScience organisation its GitHub repository links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published. Licence risk 15, Apache 2.0 through the chain from Qwen3.5-4B with no conditions added. Security programme 0, no policy, contact or security.txt.\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 +15\n\n- Agents-A1-4B (43): Model card 8, it covers intended use, parameters and context but not the architecture, limitations, languages or a knowledge cut-off. Training data 5, a general description with no cut-off. Last change 10, the files went up on 14 July 2026, 112 days before 3 November 2026, and the GitHub repository's last commit is 16 July. Responsiveness 10, InternScience answered one of the three discussions, unchanged on 3 November 2026. Version policy 5, a first release. File health 5, the files agree on the essentials, but the config declares a prediction layer that isn't shipped and the template carries a dated default prompt.\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 | Agents-A1-4B | Cola2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 25 | Agents-A1-4B +65 |\n| Risk | 20% (20 this run) | 55 | 40 | Agents-A1-4B +15 |\n| Agent interface | 10% (10 this run) | 70 | 20 | Agents-A1-4B +50 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 58 | Cola2 +15 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **33.9 · F** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | Cola2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | 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 | 2026-07-14 | 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 | 3/5 (4) | none |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\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### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. 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, Agents-A1-4B or Cola2?\n\nAgents-A1-4B 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/agents-a1-4b-vs-cola2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-cola2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"cola2\"}`. 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"meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-11-11", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.7", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Agents-A1-4B vs Cola2", "url": "" } ], "description": "Agents-A1-4B scores 61 (C) to Cola2's 33.9 (F) for open-weight models you run yourself. 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