{ "data": { "a": { "slug": "arete-27b", "name": "Arete-27B", "vendor": "Nous Research", "vendorUrl": "https://nousresearch.com", "kind": "weights", "category": "open-weights", "summary": "Arete-27B is Nous Research's fine-tune of Qwen3.8-27B, trained on 28,886 published examples so that its answers on politically sensitive topics stop following the Chinese state's account, with the base's tools, images and 262K context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/arete-27b", "markdownUrl": "https://www.anchorterminal.com/tools/arete-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/arete-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/arete-27b.json", "dns": "arete-27b.grade.anchorterminal.com", "repo": "https://huggingface.co/NousResearch/Arete-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "NousResearch/Arete-27B" } ], "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 and use, commercial use included, under Apache 2.0, in the card's metadata and as a LICENSE file in the repository. Running it yourself costs the hardware, 54.7 GB for the BF16 files, or 18.1 GB for bartowski's Q4_K_M GGUF with its projector (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://huggingface.co/NousResearch/Arete-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "fine-tune", "qwen", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "self-hosted" ], "lastRelease": "2026-11-06", "graded": true, "anchor": { "graded": true, "score": 55.1, "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": 92, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "bestFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "strengths": [ "The training pairs and the evaluation set are both published", "Apache 2.0, ungated, safetensors only, no remote code", "The base's architecture, so vLLM, SGLang and Transformers run it from their releases", "Fits a 128 GB machine in BF16, and 24 GB from bartowski's 4-bit GGUF", "Nous Research reports no meaningful loss against the base on its agent benchmarks" ], "weaknesses": [ "No quantised build from Nous Research, so the GGUF route depends on a third party", "The card doesn't document tool calling, structured output or how to switch thinking", "No limitations section, safety results or security contact", "None of the 13 discussions has a reply from Nous Research", "The results are Nous Research's own evaluation and nobody else has rerun it yet" ], "agentNotes": [ "Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM", "Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's", "Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off", "Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for", "Validate JSON output yourself, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.1 } ], "editorialScores": { "interface": 60, "reach": 50, "risk": 55, "upkeep": 63 }, "provenanceScore": 61, "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": "vllm serve NousResearch/Arete-27B --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\"model\": \"NousResearch/Arete-27B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/arete-27b" }, "sameCompany": [ "hermes-agent" ], "weights": { "trainedBy": "Nous Research", "builtOn": "Qwen3.8-27B, declared as base_model in the card's metadata. Supervised fine-tuning with Inkling as the teacher", "params": "27.4B", "context": 262144, "goodFor": "Qwen3.8-27B for coding and agent work, text, images and video in, with its answers on politically sensitive topics retrained away from the Chinese state's account.", "runtimes": [ "vLLM", "SGLang", "Transformers", "llama.cpp" ], "builds": [ { "name": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 18.13, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 17.20 GB of Q4_K_M weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "bartowski NousResearch_Arete-27B-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/NousResearch_Arete-27B-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 29.6, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Nous Research's. 28.67 GB of Q8_0 weights plus the 0.93 GB F16 projector, from the Hub's model information on 10 November 2026. Reach doesn't count it" }, { "name": "Arete-27B", "url": "https://huggingface.co/NousResearch/Arete-27B", "who": "publisher", "precision": "bf16", "sizeGB": 54.7, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "The only build Nous Research publishes" } ], "run": "llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja", "runBuild": "bartowski NousResearch_Arete-27B-GGUF (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Nous Research (NousResearch)", "family": "Arete", "url": "https://www.anchorterminal.com/open-weights/#nous-research" }, "area": "models", "provenance": { "legalEntity": "Nous Research, Inc. (named in the footer of nousresearch.com)", "domain": "nousresearch.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/NousResearch/Arete-27B/commits/main", "securityTxt": "none", "checked": "2026-11-10", "notes": [ "Nous Research, Inc. is the name in the site's footer, as the Hermes Agent listing records.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, eight commits on 6 November 2026, the upload and updates to the card.", "The Hermes Agent listing found no security.txt on nousresearch.com. unchecked on 10 November.", "unchecked: the domain's registration date." ], "score": 61 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/arete-27b.json" }, "answer": "Arete-27B scores 55.1 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface.", "b": { "slug": "longcat-2-5", "name": "LongCat-2.5", "vendor": "Meituan (LongCat)", "vendorUrl": "https://longcat.ai", "kind": "weights", "category": "open-weights", "summary": "LongCat-2.5 is Meituan's open-weight flagship, a multimodal mixture-of-experts model of 1.6T parameters with about 48B active per token, built for long agentic coding work. MIT licence. The weights are 3,562 GB, and the card documents deployment on NPU hardware only.", "url": "https://www.anchorterminal.com/tools/longcat-2-5", "markdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/longcat-2-5.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json", "dns": "longcat-2-5.grade.anchorterminal.com", "repo": "https://huggingface.co/meituan-longcat/LongCat-2.5", "license": "MIT", "transports": [], "packages": [ { "registry": "huggingface", "name": "meituan-longcat/LongCat-2.5" }, { "registry": "huggingface", "name": "meituan-longcat/LongCat-2.5-W4A4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under the MIT licence. Running it yourself is a data-centre job: 3,562 GB in BF16, or 1,336 GB for Meituan's 4-bit build, which the card says is for INT4 computation on NPU accelerators (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://huggingface.co/meituan-longcat/LongCat-2.5", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "reasoning", "tool-calling", "agentic-coding", "mit", "safetensors", "int4", "npu", "self-hosted" ], "lastRelease": "2026-11-10", "graded": true, "anchor": { "graded": true, "score": 50.5, "grade": "D", "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": 107, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 75, "reach": 35, "risk": 60, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.", "bestFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do", "strengths": [ "MIT licence, ungated", "Tool calling documented with a worked example", "Reasoning can be switched per request and comes back as reasoning_content", "Meituan publishes its own 4-bit build", "Updated on the day we checked" ], "weaknesses": [ "3,562 GB in BF16 and 1,336 GB at 4 bits", "No released runtime has its architecture", "The only documented deployment is on NPU hardware through Meituan's fork", "config.json names model code that isn't in the repository", "No stated context length, languages, safety results or data sources", "No inference provider serves it" ], "agentNotes": [ "You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet", "If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format", "Pass `chat_template_kwargs` with `enable_thinking` true or false", "With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends", "The card's results use temperature 1.0 and top_p 0.95", "Its benchmark table is Meituan's own. The unmarked scores are in-house" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "D", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 50.5 } ], "editorialScores": { "interface": 75, "reach": 35, "risk": 60, "upkeep": 46 }, "provenanceScore": 49, "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": "# No released runtime loads it. Meituan's NPU instructions are in its SGLang fork:\n# github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu, npu_test/ReadMe.md", "http": "# The card's example calls an OpenAI-compatible chat completions API with tools, and passes\n# extra_body={\"chat_template_kwargs\": {\"enable_thinking\": true, \"save_history_reasoning_content\": true}}" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/longcat-2-5" }, "weights": { "trainedBy": "Meituan (LongCat team)", "builtOn": "LongCat-2.0 and LongCat-Next, per the card", "params": "1.6T", "active": "48B", "context": 524288, "goodFor": "Long agentic coding and research runs with images, for an operator with NPU clusters. Nobody else can run it yet.", "runtimes": [], "builds": [ { "name": "LongCat-2.5-W4A4", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4", "who": "publisher", "precision": "int4", "sizeGB": 1336, "memoryGB": 2048, "format": "safetensors", "note": "Meituan's 4-bit build of the experts' weights and activations, for INT4 computation on NPU accelerators" }, { "name": "LongCat-2.5 (BF16 safetensors)", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5", "who": "publisher", "precision": "bf16", "sizeGB": 3562, "memoryGB": 8192, "format": "safetensors", "note": "210 files" } ], "run": "See github.com/meituan-longcat/SGLang-FluentLLM, branch longcat-2.5-npu", "runBuild": "LongCat-2.5-W4A4", "checked": "2026-11-10", "minMemoryGB": 2048 }, "family": { "lab": "Meituan (LongCat)", "family": "LongCat-2", "url": "https://www.anchorterminal.com/open-weights/#meituan-longcat", "others": [ { "name": "LongCat-2.5-W4A4", "url": "https://huggingface.co/meituan-longcat/LongCat-2.5-W4A4" }, { "name": "LongCat-2.0", "url": "https://huggingface.co/meituan-longcat/LongCat-2.0" } ] }, "area": "models", "provenance": { "legalEntity": "Meituan, named in the card's licence section. The legal entity wasn't looked up", "domain": "longcat.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://huggingface.co/meituan-longcat/LongCat-2.5/blob/main/LICENSE", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/meituan-longcat/LongCat-2.5/commits/main", "securityTxt": "unknown", "checked": "2026-11-10", "notes": [ "longcat.ai has no robots.txt (the request returns a not-found message) and its pages are JavaScript applications that gave no text on 10 November 2026, so the site's own statements weren't read.", "The domain's registration date wasn't looked up.", "Nothing is hosted under this listing, so the MIT licence stands in for terms of service and no privacy policy is scored.", "The changelog is the commit history, 50 commits or more since 4 November 2026." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/longcat-2-5.json" }, "edges": [ { "by": 17, "category": "upkeep", "leads": "arete-27b", "name": "Documentation and upkeep", "reasons": { "a": "Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.", "b": "Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46." }, "scores": { "a": 63, "b": 46 } }, { "by": 15, "category": "reach", "leads": "arete-27b", "name": "Reach", "reasons": { "a": "Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.", "b": "Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories 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. 0 + 0 + 10 + 15 + 10 + 0 = 35." }, "scores": { "a": 50, "b": 35 } }, { "by": 15, "category": "interface", "leads": "longcat-2-5", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.", "b": "Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75." }, "scores": { "a": 60, "b": 75 } }, { "by": 5, "category": "risk", "leads": "longcat-2-5", "name": "Risk", "reasons": { "a": "File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.", "b": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60." }, "scores": { "a": 55, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Nous Research", "b": "Meituan (LongCat)", "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": "MIT", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-06", "b": "2026-11-10", "name": "Last release" }, { "a": "no document linked", "b": "not read yet", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" } ], "faq": [ { "answer": "Arete-27B scores 55.1 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface.", "question": "Which is better for AI agents, Arete-27B or LongCat-2.5?" } ], "goodFor": [ { "aheadOn": [ "Reach, 50 against 35", "Documentation and upkeep, 63 against 46" ], "also": null, "goodFor": "Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine", "slug": "arete-27b", "watchFor": "No quantised build from Nous Research, so the GGUF route depends on a third party" }, { "aheadOn": [ "Risk, 60 against 55", "Agent interface, 75 against 60" ], "also": null, "goodFor": "Operators with NPU clusters, and anyone tracking what the open flagships can do", "slug": "longcat-2-5", "watchFor": "3,562 GB in BF16 and 1,336 GB at 4 bits" } ], "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-arete-27b.json", "title": "Agents-A1-4B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-arete-27b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.json", "title": "Agents-A1-4B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b.json", "title": "Apertus v1.5 8B vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-arete-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.json", "title": "Apertus v1.5 8B vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b.json", "title": "Apodex-1.1-mini vs Arete-27B", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-arete-27b" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.json", "title": "Apodex-1.1-mini vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1.json", "title": "Arete-27B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x.json", "title": "Arete-27B vs Carbon-X", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-carbon-x" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2.json", "title": "Arete-27B vs Cola2", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cola2" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b.json", "title": "Arete-27B vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-cosmos-reason2-2b" }, { "json": 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"https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it.json", "title": "Arete-27B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it.json", "title": "Arete-27B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it.json", "title": "Arete-27B vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air.json", "title": "Arete-27B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash.json", "title": "Arete-27B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3.json", "title": "Arete-27B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash.json", "title": "Arete-27B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b.json", "title": "Arete-27B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b.json", "title": "Arete-27B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b.json", "title": "Arete-27B vs Granite 4.2 30B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-30b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b.json", "title": "Arete-27B vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b.json", "title": "Arete-27B vs Granite 4.2 8B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-granite-4-2-8b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b.json", "title": "Arete-27B vs Hy-Memory-30B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy-memory-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-hy3.json", "title": "Arete-27B vs Hy3", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling.json", "title": "Arete-27B vs Inkling", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling-small.json", "title": "Arete-27B vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Arete-27B vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3.json", "title": "Arete-27B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro.json", "title": "Arete-27B vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kimi-k3-1-pro" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1.json", "title": "Arete-27B vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos.json", "title": "Arete-27B vs Kronos", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1.json", "title": "Arete-27B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1.json", "title": "Arete-27B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1.json", "title": "Arete-27B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b.json", "title": "Arete-27B vs LBS-27B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lbs-27b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b.json", "title": "Arete-27B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b.json", "title": "Arete-27B vs LFM3-1.7B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-1-7b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b.json", "title": "Arete-27B vs LFM3-Audio-2B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-audio-2b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-omni-2b.json", "title": "Arete-27B vs LFM3-Omni-2B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-omni-2b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-lfm3-vl-2b.json", "title": "Arete-27B vs LFM3-VL-2B", "url": 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"https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7.json", "title": "Arete-27B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m3-1-flash.json", "title": "Arete-27B vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4.json", "title": "Arete-27B vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b.json", "title": "Arete-27B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-nanbeige-4-2-3b.json", "title": 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"title": "Arete-27B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b.json", "title": "Arete-27B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b.json", "title": "Arete-27B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it.json", "title": "Arete-27B vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b.json", "title": "Arete-27B vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/arete-27b-vs-qwen-agentworld-35b-a3b" }, { "json": 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"https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b.json", "title": "LongCat-2.5 vs MiniCPM-V 4.7 35B-A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-v-4-7-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm5-2b.json", "title": "LongCat-2.5 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5.json", "title": "LongCat-2.5 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-7.json", "title": "LongCat-2.5 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m3-1-flash.json", "title": "LongCat-2.5 vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mistral-large-4.json", "title": "LongCat-2.5 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-muse-glimmer-30b.json", "title": "LongCat-2.5 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-2-3b.json", "title": "LongCat-2.5 vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-3-33b.json", "title": "LongCat-2.5 vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "LongCat-2.5 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "LongCat-2.5 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-30b-a3b.json", "title": "LongCat-2.5 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b.json", "title": "LongCat-2.5 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-nemotron-3-nano-4b" }, { "json": 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"https://www.anchorterminal.com/compare/longcat-2-5-vs-occamy-1-0.json", "title": "LongCat-2.5 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-olmo-3-7b-think.json", "title": "LongCat-2.5 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b.json", "title": "LongCat-2.5 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-35b-a3b.json", "title": "LongCat-2.5 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-397b.json", "title": "LongCat-2.5 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-9b.json", "title": "LongCat-2.5 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it.json", "title": "LongCat-2.5 vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b.json", "title": "LongCat-2.5 vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-30b-a3b-instruct-2507.json", "title": "LongCat-2.5 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-4b-instruct-2507.json", "title": "LongCat-2.5 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-0-8b.json", "title": "LongCat-2.5 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b.json", "title": "LongCat-2.5 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-2b.json", "title": "LongCat-2.5 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-397b-a17b.json", "title": "LongCat-2.5 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-4b.json", "title": "LongCat-2.5 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b.json", "title": "LongCat-2.5 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-27b.json", "title": "LongCat-2.5 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b.json", "title": "LongCat-2.5 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b.json", "title": "LongCat-2.5 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next.json", "title": "LongCat-2.5 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-30b-a3b-instruct.json", "title": "LongCat-2.5 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-next.json", "title": "LongCat-2.5 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-omni-30b-a3b-instruct.json", "title": "LongCat-2.5 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct.json", "title": "LongCat-2.5 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct.json", "title": "LongCat-2.5 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct.json", "title": "LongCat-2.5 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct.json", "title": "LongCat-2.5 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct.json", "title": "LongCat-2.5 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2.json", "title": "LongCat-2.5 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint.json", "title": "LongCat-2.5 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview.json", "title": "LongCat-2.5 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview.json", "title": "LongCat-2.5 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b.json", "title": "LongCat-2.5 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b.json", "title": "LongCat-2.5 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash.json", "title": "LongCat-2.5 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b.json", "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next.json", "title": "LongCat-2.5 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b.json", "title": "LongCat-2.5 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2.json", "title": "LongCat-2.5 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b.json", "title": "LongCat-2.5 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b.json", "title": "LongCat-2.5 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b.json", "title": "LongCat-2.5 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "arete-27b": 50, "by": 15, "edge": "arete-27b", "key": "reach", "longcat-2-5": 35, "name": "Reach", "weight": 25 }, { "arete-27b": 55, "by": 5, "edge": "longcat-2-5", "key": "risk", "longcat-2-5": 60, "name": "Risk", "weight": 20 }, { "arete-27b": 60, "by": 15, "edge": "longcat-2-5", "key": "interface", "longcat-2-5": 75, "name": "Agent interface", "weight": 10 }, { "arete-27b": 63, "by": 17, "edge": "arete-27b", "key": "upkeep", "longcat-2-5": 46, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Arete-27B scores 55.1 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "arete-27b": "Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.", "longcat-2-5": "Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5", "json": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.min.md" }, "markdown": "Arete-27B scores 55.1 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface. Both do open-weight models you run yourself.\n\n- Arete-27B: grade C, 55.1/100, rank #123 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/arete-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/arete-27b.json\n- LongCat-2.5: grade D, 50.5/100, rank #145 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/longcat-2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/longcat-2-5.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### Arete-27B (C)\n\nGood for: Agents that want Qwen3.8-27B's coding and tool use without its handling of politically sensitive topics, on a 24 GB card or a 128 GB machine\n\nAhead on:\n- Reach, 50 against 35\n- Documentation and upkeep, 63 against 46\n\nWatch for: No quantised build from Nous Research, so the GGUF route depends on a third party\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nAhead on:\n- Risk, 60 against 55\n- Agent interface, 75 against 60\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n\n## Why the edge\n\n### Documentation and upkeep: Arete-27B +17\n\n- Arete-27B (63): Model card 8, no limitations, context length, languages or cut-off. Training data 20, the training pairs and the evaluation set are published with their counts. Last change 20, a commit on 6 November 2026. Responsiveness 0, none of 13 discussions has a reply from Nous Research. Version policy 5, a first release. File health 10, the files agree. 8 + 20 + 20 + 0 + 5 + 10 = 63.\n- LongCat-2.5 (46): Model card 11, Intended use, limitations (the usage considerations), the architecture and the parameter counts are on the card. Training data 5, A general description of the training stages and where the reinforcement learning tasks came from, with no sources, sizes or cut-off. Last change 20, the last commit, on 10 November 2026, updated the card, on the day of this check. Responsiveness 0, Neither of the 2 discussions that aren't pull requests (9 and 10 November 2026) has a reply from a meituan-longcat member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, LongCat-2.0 stays in its own repositories and 2.5 came out in new ones, with no dated note on the older ones that we read. File health 5, the template is in both places and agrees, but config.json's auto_map names configuration_longcat_oe and modeling_longcat_oe, which aren't in the repository, and the tokenizer config's 131,072-token length is a quarter of the config's 524,288 positions. 11 + 5 + 20 + 0 + 5 + 5 = 46.\n\n### Reach: Arete-27B +15\n\n- Arete-27B (50): Memory 10, the only build from Nous Research is the 54.7 GB BF16 release, which needs about 66 GB and fits the reference machine. Runtimes 15, vLLM, SGLang and Transformers run the base's architecture from those files, and llama.cpp isn't credited because Nous Research publishes no GGUF. Builds 0, no quantised build of its own. Terms 15, Apache 2.0. Download 10, not gated. Hosted 0, no provider. 10 + 15 + 0 + 15 + 10 + 0 = 50.\n- LongCat-2.5 (35): Memory 0, the smallest build is Meituan's W4A4 repository, 1,336 GB, and 1,336 GB × 1.2 = 1,603 GB, far above 256 GB. The BF16 files are 3,562 GB. Runtimes 0, none of the six runs it in a released version: vLLM 0.33.0, SGLang 0.5.23, Transformers 5.22.2 and llama.cpp b12242 have no LongcatCausalLM or pro_v code, and the model code config.json names isn't in the repository. Builds 10, one quantised build from Meituan, the W4A4 repository (4-bit experts, 1,336 GB). Terms 15, MIT in the metadata, the LICENSE file and the card, with nothing added. Download 10, the Hub API reports gated false for both repositories 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. 0 + 0 + 10 + 15 + 10 + 0 = 35.\n\n### Agent interface: LongCat-2.5 +15\n\n- Arete-27B (60): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the template says it. Trained tool calling 20, several calls a turn and agentic work as an intended use. Structured output 0 and reasoning control 0, neither is documented on the card. Context 10, 262,144 tokens native. 20 + 10 + 20 + 0 + 0 + 10 = 60.\n- LongCat-2.5 (75): Chat template 20, chat_template.jinja in the repository (11,823 characters), with a copy in tokenizer_config.json. Tool-calling format 25, the card documents how tools are declared (the OpenAI function format) and how calls come back (tool_calls with an id, a name and JSON arguments, answered by a tool message), in a worked example against an OpenAI-compatible server. Trained tool calling 10, the card describes reinforcement learning on long agentic tasks and shows one tool call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output. Reasoning control 10, enable_thinking on or off per request through chat_template_kwargs, with the reasoning returned as reasoning_content, both shown on the card. Context 10, the card states no context length, but config.json gives 524,288 positions and the card's own evaluations ran at 512K and 1M sequence lengths after what it calls long-context training, so 128K or more is taken as trained. 20 + 25 + 10 + 0 + 10 + 10 = 75.\n\n### Risk: LongCat-2.5 +5\n\n- Arete-27B (55): File safety 25, safetensors only and no remote code. Provenance 15, Nous Research's own organisation, linked from its blog, with a hash on every file. Safety evaluation 0 and agent guidance 0, nothing published on either. Licence risk 15, plain Apache 2.0. Security programme 0, no way to report a flaw found. 25 + 15 + 0 + 0 + 15 + 0 = 55.\n- LongCat-2.5 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, the files sit in the meituan-longcat organisation, the name of Meituan's GitHub organisation that the card links for deployment, and the card's licence names Meituan (10). Safety evaluation 0, no safety results on the card. Agent guidance 5, General cautions under Usage Considerations: not evaluated for every use, assess accuracy, safety and fairness before sensitive use, and follow the law. Licence risk 15, MIT with no use policy, no threshold and no right to change the terms. Security programme 0, no way to report a flaw on the card beyond a general contact, and Meituan's own security pages weren't looked up, so this scores as absent and is an open question. 25 + 15 + 0 + 5 + 15 + 0 = 60.\n\n## Score by category\n\n| Category | Weight | Arete-27B | LongCat-2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 50 | 35 | Arete-27B +15 |\n| Risk | 20% (20 this run) | 55 | 60 | LongCat-2.5 +5 |\n| Agent interface | 10% (10 this run) | 60 | 75 | LongCat-2.5 +15 |\n| Documentation and upkeep | 10% (10 this run) | 63 | 46 | Arete-27B +17 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **55.1 · C** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | Arete-27B | LongCat-2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Nous Research | Meituan (LongCat) |\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 | MIT |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-06 | 2026-11-10 |\n| Terms last updated | no document linked | not read yet |\n| Privacy policy last updated | no document linked | no document linked |\n\n## Verdicts\n\n**Arete-27B.** Qwen3.8-27B with its answers on politically sensitive topics retrained, four days old when read. The training data is published, which is rare. Nous Research ships BF16 only, so a small machine runs it from bartowski's GGUF, and the card documents none of the tool or thinking controls it inherits.\n\n**LongCat-2.5.** Meituan's flagship is MIT-licensed and documented for tool calling and switchable reasoning, with strong numbers of its own on agentic coding. Almost nobody can run it: 3,562 GB of weights, a 1,336 GB 4-bit build for NPU accelerators, and no released runtime with its architecture. A model to watch until a runtime or a provider picks it up.\n\n## Before you call either\n\n### Arete-27B\n\n1. Start it with `llama-server -hf bartowski/NousResearch_Arete-27B-GGUF:Q4_K_M --jinja`, a third party's conversion, or serve the BF16 files with vLLM\n2. Use the base's parsers in vLLM, `--reasoning-parser qwen3 --tool-call-parser qwen3_coder`, and check tool calls first, since the template differs slightly from Qwen3.8-27B's\n3. Thinking switches with `enable_thinking` in the template. The card's own evaluation ran with it off\n4. Expect answers on politically sensitive topics to differ from Qwen3.8-27B's. That is what the fine-tune is for\n5. Validate JSON output yourself, since structured output isn't documented\n\n### LongCat-2.5\n\n1. You can't run this on a workstation or a single GPU server. Plan for a provider, and none serves it yet\n2. If an operator serves it, call it as an OpenAI-compatible chat API with tools in the function format\n3. Pass `chat_template_kwargs` with `enable_thinking` true or false\n4. With thinking on, keep earlier reasoning in the history (`save_history_reasoning_content` true), as the card recommends\n5. The card's results use temperature 1.0 and top_p 0.95\n6. Its benchmark table is Meituan's own. The unmarked scores are in-house\n\n## Questions\n\n### Which is better for AI agents, Arete-27B or LongCat-2.5?\n\nArete-27B scores 55.1 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in 2 of 4 scored categories. LongCat-2.5 leads on risk and agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/arete-27b-vs-longcat-2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"arete-27b\", \"b\": \"longcat-2-5\"}`. 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