{ "data": { "a": { "slug": "kolibri-1", "name": "Kolibri-1", "vendor": "Aleph Alpha", "vendorUrl": "https://aleph-alpha.com", "kind": "weights", "category": "open-weights", "summary": "Kolibri-1 is Aleph Alpha's open-weight mixture-of-experts reasoning model for German and English, 78B parameters with 3.46B active per token, published on Hugging Face in FP8 and BF16 under Apache 2.0 and served with vLLM.", "url": "https://www.anchorterminal.com/tools/kolibri-1", "markdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kolibri-1.json", "dns": "kolibri-1.grade.anchorterminal.com", "repo": "https://huggingface.co/Aleph-Alpha/Kolibri-1", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Aleph-Alpha/Kolibri-1" }, { "registry": "huggingface", "name": "Aleph-Alpha/Kolibri-1-BF16" }, { "registry": "pypi", "name": "aleph-alpha-inference" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Aleph Alpha sells no per-call access to these weights. Running them costs the hardware, about 78 GB of accelerator memory for the FP8 weights per the model card (checked 2026-10-20).", "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 2026-10-20).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-20" }, "docsUrl": "https://huggingface.co/Aleph-Alpha/Kolibri-1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "reasoning", "tool-calling", "german", "apache-2.0", "safetensors", "fp8", "self-hosted", "new" ], "lastRelease": "2026-10-19", "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": 34, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 90, "reach": 65, "risk": 65, "upkeep": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "negative": 0, "verdict": "Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers.", "bestFor": "German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.", "strengths": [ "Apache 2.0 with no use policy, downloadable with no account", "Safetensors only, in Aleph Alpha's own organisation with a SHA-256 for every shard", "Tool calling documented end to end with the kolibri1 parser for vLLM and a working example", "Thinking can be set from none to high per request, with the reasoning returned separately", "262,144 tokens trained, with a full model card and a data card that gives the mix, filtering and cut-off" ], "weaknesses": [ "The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine", "No LM Studio or SGLang support in a released version, and vLLM's kolibri1 parsers come only with Aleph Alpha's plugin pinned to vLLM 0.29", "No safety evaluation results and no security contact or disclosure policy found", "Aleph Alpha answered 2 of 14 discussions, and a report of a broken tool-call parser in its container image has no reply", "No hosted API or inference provider, so an agent without the hardware can't call it", "Structured output is claimed without saying how to request it" ], "agentNotes": [ "Start it with pip install 'aleph-alpha-inference\u003e=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice", "Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags", "The plugin supports one vLLM minor version, 0.29. vLLM 0.33.0 loads the model without it, but has no kolibri1 tool or reasoning parser", "Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks", "Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128", "Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144", "With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking", "On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp or Ollama", "Validate tool results in your own code, as the card asks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 90, "reach": 65, "risk": 65, "upkeep": 70 }, "provenanceScore": 70, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install 'aleph-alpha-inference\u003e=1'\nvllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 \\\n --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Kolibri-1-GGUF:Q4_K_M --jinja", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"Aleph-Alpha/Kolibri-1\",\n \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of France?\"}],\n \"chat_template_kwargs\": {\"enable_thinking\": false}\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/kolibri-1" }, "weights": { "trainedBy": "Aleph Alpha", "params": "78.1B", "active": "3.46B", "context": 262144, "goodFor": "German and English reasoning model with tool calling and adjustable reasoning effort, text only.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "Transformers" ], "builds": [ { "name": "bartowski Kolibri-1-GGUF (Q4_K_M)", "url": "https://huggingface.co/bartowski/Kolibri-1-GGUF", "who": "bartowski", "precision": "q4_k_m", "sizeGB": 49.76, "memoryGB": 64, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Aleph Alpha's. 49.76 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "Kolibri-1", "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1", "who": "publisher", "precision": "fp8", "sizeGB": 78.8, "memoryGB": 96, "format": "safetensors" }, { "name": "bartowski Kolibri-1-GGUF (Q8_0)", "url": "https://huggingface.co/bartowski/Kolibri-1-GGUF", "who": "bartowski", "precision": "q8_0", "sizeGB": 83.14, "memoryGB": 128, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From bartowski, a third party's conversion and not Aleph Alpha's. 83.14 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it" }, { "name": "Kolibri-1-BF16", "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1-BF16", "who": "publisher", "precision": "bf16", "sizeGB": 156.2, "memoryGB": 192, "format": "safetensors", "runsOn": [ "vLLM (ROCm)" ] } ], "run": "pip install 'aleph-alpha-inference\u003e=1' \u0026\u0026 vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice", "runBuild": "Kolibri-1", "checked": "2026-11-03", "minMemoryGB": 64 }, "family": { "lab": "Aleph Alpha", "family": "Kolibri", "url": "https://www.anchorterminal.com/open-weights/#aleph-alpha", "olderCount": 23, "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "Aleph Alpha GmbH", "domain": "aleph-alpha.com", "domainRegistered": "2018-08-11", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main", "securityTxt": "unknown", "checked": "2026-10-20", "notes": [ "The model card names Aleph Alpha GmbH as the provider and Aleph Alpha Research GmbH as the developer. The imprint names Aleph Alpha GmbH, Heidelberg, commercial register Mannheim HRB 732804.", "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, whose messages say what changed (Release 2026-10-02, a quantisation config fix on 13 October, transformers_version added on 19 October).", "aleph-alpha.com/.well-known/security.txt answered 403 to our reader on 20 October 2026, so it is counted as unchecked rather than absent." ], "score": 70 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/kolibri-1.json", "live": { "slug": "kolibri-1", "versions": [ { "registry": "pypi", "name": "aleph-alpha-inference", "version": "1.0.0", "released": "2026-10-03", "seenAt": "2026-11-10T03:51:07.580354462Z" } ], "pypiWeekly": 115, "securityTxt": { "url": "https://aleph-alpha.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-10T16:08:53.984135769Z" }, "domain": { "domain": "aleph-alpha.com", "registered": "2018-08-11", "source": "https://rdap.verisign.com/com/v1/domain/aleph-alpha.com", "checkedAt": "2026-11-08T14:59:31.895475432Z" }, "pages": [ { "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-10T16:22:08.257556045Z", "changedAt": "2026-11-10T16:22:08.257556045Z", "fingerprint": "847be72adc2c" } ], "updatedAt": "2026-11-10T16:22:08.257556045Z" } }, "answer": "Kolibri-1 scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category.", "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": 30, "category": "reach", "leads": "kolibri-1", "name": "Reach", "reasons": { "a": "Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only the reference machine's band. There is no publisher 4-bit or GGUF build and no Ollama library entry to bring it lower. Runtimes earn 20, for released Transformers 5.20.0, released llama.cpp builds from b11709, Ollama 0.40.14, which builds llama.cpp b11963 with kolibri1, and vLLM, through Aleph Alpha's plugin for 0.29 and natively in 0.33.0 through its Transformers backend. LM Studio's latest runtime predates kolibri1 and SGLang 0.5.23 has no model file. Builds earn 10 for the one FP8 quantisation beside BF16. Terms earn 15 for Apache 2.0 with no use policy. Download earns 10 because neither repository is gated. Hosted earns 0, since no inference provider is listed on the Hub and Aleph Alpha names no API serving it.", "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": 65, "b": 35 } }, { "by": 24, "category": "upkeep", "leads": "kolibri-1", "name": "Documentation and upkeep", "reasons": { "a": "The model card earns 20 with all seven items. Training data earns 15 for a data card with the mix, the filtering and the cut-off, short of a dataset list with sizes. The last change earns 20, for template and card commits on 20 October 2026. Responsiveness earns 5, since Aleph Alpha replied in 2 of 14 discussions across both repositories and none since 13 October. Version policy earns 5 for a first release. File health earns 5, because the files agree but a report of a broken tool-call parser in the publisher's container image has waited since 7 October without a reply.", "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": 70, "b": 46 } }, { "by": 15, "category": "interface", "leads": "kolibri-1", "name": "Agent interface", "reasons": { "a": "The chat template earns 20 as Jinja in tokenizer_config.json. The tool-calling format earns 25, because the card documents how tools are declared and how calls come back and names the kolibri1 parser for vLLM with a full example. Trained tool calling earns 20, from training on tool environments and a template built for several calls in one turn. Structured output earns 5, since the card claims it without saying how to ask. Reasoning control earns 10 for effort levels and an off switch per request, with the reasoning returned in a separate field. Context earns 10 for 262,144 tokens trained.", "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": 90, "b": 75 } }, { "by": 5, "category": "risk", "leads": "kolibri-1", "name": "Risk", "reasons": { "a": "File safety earns 25 for safetensors only, with no repository code run on load. Provenance earns 15, for the Aleph-Alpha organisation the launch post links and a SHA-256 for every weight shard. Safety evaluation earns 0, because the card says refusals were measured and neither the card nor the technical report publishes results. Agent guidance earns 10, since the card says what the trained refusals cover and which safeguards to add around the model, without prompt-injection guidance. Licence risk earns the full 15 under unchanged Apache 2.0. The security programme earns 0, as no contact, policy or advisories were found and security.txt answered 403 on 3 November, so it is scored as absent and left open.", "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": 65, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Aleph Alpha", "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": "2026-10-19", "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" }, { "a": "3.3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Kolibri-1 scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category.", "question": "Which is better for AI agents, Kolibri-1 or LongCat-2.5?" } ], "goodFor": [ { "aheadOn": [ "Reach, 65 against 35", "Risk, 65 against 60", "Agent interface, 90 against 75", "Documentation and upkeep, 70 against 46" ], "also": null, "goodFor": "German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.", "slug": "kolibri-1", "watchFor": "The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine" }, { "aheadOn": null, "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": { "kolibri-1": [ { "causes": [ "product" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" }, { "causes": [ "method" ], "date": "2026-10-21", "delta": -14.8, "from": 75.8, "fromGrade": "BB", "to": 61, "toGrade": "C" } ], "longcat-2-5": null }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.json", "title": "Agents-A1-4B vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1" }, { "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-kolibri-1.json", "title": "Apertus v1.5 8B vs Kolibri-1", "url": 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"title": "Ling-3.0-tiny vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-longcat-2-5.json", "title": "Ling-3.1-flash-1019 vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/ling-3-1-flash-1019-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-distill-qwen-9b.json", "title": "LongCat-2.5 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl.json", "title": "LongCat-2.5 vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/longcat-2-5-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/longcat-2-5-vs-minicpm-o-4-5.json", "title": "LongCat-2.5 vs MiniCPM-o 4.5", "url": 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"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": 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"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 }, { "by": 30, "edge": "kolibri-1", "key": "reach", "kolibri-1": 65, "longcat-2-5": 35, "name": "Reach", "weight": 25 }, { "by": 5, "edge": "kolibri-1", "key": "risk", "kolibri-1": 65, "longcat-2-5": 60, "name": "Risk", "weight": 20 }, { "by": 15, "edge": "kolibri-1", "key": "interface", "kolibri-1": 90, "longcat-2-5": 75, "name": "Agent interface", "weight": 10 }, { "by": 24, "edge": "kolibri-1", "key": "upkeep", "kolibri-1": 70, "longcat-2-5": 46, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Kolibri-1 scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "kolibri-1": "Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers.", "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/kolibri-1-vs-longcat-2-5", "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5.min.md" }, "markdown": "Kolibri-1 scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category. Both do open-weight models you run yourself.\n\n- Kolibri-1: grade C, 61/100, rank #44 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.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### Kolibri-1 (C)\n\nGood for: German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.\n\nAhead on:\n- Reach, 65 against 35\n- Risk, 65 against 60\n- Agent interface, 90 against 75\n- Documentation and upkeep, 70 against 46\n\nWatch for: The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine\n\n### LongCat-2.5 (D)\n\nGood for: Operators with NPU clusters, and anyone tracking what the open flagships can do\n\nWatch for: 3,562 GB in BF16 and 1,336 GB at 4 bits\n\n\n## Why the edge\n\n### Reach: Kolibri-1 +30\n\n- Kolibri-1 (65): Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only the reference machine's band. There is no publisher 4-bit or GGUF build and no Ollama library entry to bring it lower. Runtimes earn 20, for released Transformers 5.20.0, released llama.cpp builds from b11709, Ollama 0.40.14, which builds llama.cpp b11963 with kolibri1, and vLLM, through Aleph Alpha's plugin for 0.29 and natively in 0.33.0 through its Transformers backend. LM Studio's latest runtime predates kolibri1 and SGLang 0.5.23 has no model file. Builds earn 10 for the one FP8 quantisation beside BF16. Terms earn 15 for Apache 2.0 with no use policy. Download earns 10 because neither repository is gated. Hosted earns 0, since no inference provider is listed on the Hub and Aleph Alpha names no API serving it.\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### Documentation and upkeep: Kolibri-1 +24\n\n- Kolibri-1 (70): The model card earns 20 with all seven items. Training data earns 15 for a data card with the mix, the filtering and the cut-off, short of a dataset list with sizes. The last change earns 20, for template and card commits on 20 October 2026. Responsiveness earns 5, since Aleph Alpha replied in 2 of 14 discussions across both repositories and none since 13 October. Version policy earns 5 for a first release. File health earns 5, because the files agree but a report of a broken tool-call parser in the publisher's container image has waited since 7 October without a reply.\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### Agent interface: Kolibri-1 +15\n\n- Kolibri-1 (90): The chat template earns 20 as Jinja in tokenizer_config.json. The tool-calling format earns 25, because the card documents how tools are declared and how calls come back and names the kolibri1 parser for vLLM with a full example. Trained tool calling earns 20, from training on tool environments and a template built for several calls in one turn. Structured output earns 5, since the card claims it without saying how to ask. Reasoning control earns 10 for effort levels and an off switch per request, with the reasoning returned in a separate field. Context earns 10 for 262,144 tokens trained.\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: Kolibri-1 +5\n\n- Kolibri-1 (65): File safety earns 25 for safetensors only, with no repository code run on load. Provenance earns 15, for the Aleph-Alpha organisation the launch post links and a SHA-256 for every weight shard. Safety evaluation earns 0, because the card says refusals were measured and neither the card nor the technical report publishes results. Agent guidance earns 10, since the card says what the trained refusals cover and which safeguards to add around the model, without prompt-injection guidance. Licence risk earns the full 15 under unchanged Apache 2.0. The security programme earns 0, as no contact, policy or advisories were found and security.txt answered 403 on 3 November, so it is scored as absent and left open.\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 | Kolibri-1 | LongCat-2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 35 | Kolibri-1 +30 |\n| Risk | 20% (20 this run) | 65 | 60 | Kolibri-1 +5 |\n| Agent interface | 10% (10 this run) | 90 | 75 | Kolibri-1 +15 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 46 | Kolibri-1 +24 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **50.5 · D** | |\n\n## Facts side by side\n\n| Fact | Kolibri-1 | LongCat-2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Aleph Alpha | 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 | 2026-10-19 | 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| Agent reviews | 3.3/5 (4) | none |\n\n## How the scores have moved\n\n### Kolibri-1\n\n- no change on 3 November 2026: product\n- -14.8 on 21 October 2026 (BB to C): checklist\n\n### LongCat-2.5\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Kolibri-1.** Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers.\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### Kolibri-1\n\n1. Start it with pip install 'aleph-alpha-inference\u003e=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice\n2. Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags\n3. The plugin supports one vLLM minor version, 0.29. vLLM 0.33.0 loads the model without it, but has no kolibri1 tool or reasoning parser\n4. Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks\n5. Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128\n6. Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144\n7. With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking\n8. On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp or Ollama\n9. Validate tool results in your own code, as the card asks\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, Kolibri-1 or LongCat-2.5?\n\nKolibri-1 scores 61 (C) on agent readiness against LongCat-2.5's 50.5 (D), and leads in every scored category.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kolibri-1\", \"b\": \"longcat-2-5\"}`. From a terminal: `anchor compare kolibri-1 longcat-2-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/longcat-2-5.json\n\n## Other comparisons with Kolibri-1 or LongCat-2.5\n\n- [Agents-A1-4B vs Kolibri-1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.md)\n- [Agents-A1-4B vs LongCat-2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-longcat-2-5.md)\n- [Apertus v1.5 8B vs Kolibri-1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.md)\n- [Apertus v1.5 8B vs LongCat-2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-longcat-2-5.md)\n- [Apodex-1.1-mini vs Kolibri-1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1.md)\n- [Apodex-1.1-mini vs LongCat-2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-longcat-2-5.md)\n- [Arete-27B vs Kolibri-1](https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1.md)\n- 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