{ "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", "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": 1197, "categoryRank": 31, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 90, "reach": 60, "risk": 65, "upkeep": 70 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "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 through its plugin, Transformers 5.20 or recent llama.cpp. 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 Ollama, LM Studio or SGLang support in a released version, and vLLM only through 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 11 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, so don't upgrade vLLM while it's installed", "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", "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": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 90, "reach": 60, "risk": 65, "upkeep": 70 }, "provenanceScore": 68, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "lastMoved": "2026-10-21", "oldest": "2026-10-21", "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", "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", "vLLM", "Transformers" ], "builds": [ { "name": "Kolibri-1", "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1", "who": "publisher", "precision": "fp8", "sizeGB": 78.8, "memoryGB": 96, "format": "safetensors" }, { "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": "2026-10-21", "minMemoryGB": 96 }, "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": 68 }, "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": "2026-10-21T06:04:10.256551317Z" } ], "pypiWeekly": 639, "securityTxt": { "url": "https://aleph-alpha.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-21T15:59:42.849148653Z" }, "pages": [ { "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-10-21T00:49:59.261303558Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "037a793cc2d6" } ], "updatedAt": "2026-10-21T15:59:42.849148653Z" } }, "answer": "Kolibri-1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 3 of 4 scored categories. Laguna M.1 leads on reach.", "b": { "slug": "laguna-m-1", "name": "Laguna M.1", "vendor": "Poolside", "vendorUrl": "https://poolside.ai", "kind": "weights", "category": "open-weights", "summary": "Laguna M.1 is Poolside's first-generation open-weight Mixture-of-Experts model for agentic coding, 225B total and 23B active parameters, with switchable reasoning and a 262,144-token context, published on Hugging Face under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/laguna-m-1", "markdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/laguna-m-1.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json", "repo": "https://huggingface.co/poolside/Laguna-M.1", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "poolside/Laguna-M.1" }, { "registry": "huggingface", "name": "poolside/Laguna-M.1-FP8" }, { "registry": "huggingface", "name": "poolside/Laguna-M.1-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for the main repository and both builds, although the card's metadata carries an unused gate description pointing to Poolside's privacy policy (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Running it yourself costs the hardware, 451.6 GB for the BF16 files and 139.3 GB for the NVFP4 build. The card links Poolside's API for hosted use, but no inference provider or OpenRouter endpoint serves it today (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/poolside/Laguna-M.1", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "coding", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-14", "graded": true, "anchor": { "graded": true, "score": 55.5, "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": 1197, "categoryRank": 64, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 46, "interface": 85, "reach": 65, "risk": 55, "upkeep": 36 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.", "bestFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.", "strengths": [ "Plain Apache 2.0 with no gate, account or use policy", "Released support in vLLM, SGLang, Transformers and llama.cpp", "Poolside's own FP8 and NVFP4 builds", "Tool calling and the poolside_v1 parser documented on the card, several calls a turn", "Thinking on or off per request, with the reasoning returned in reasoning_content" ], "weaknesses": [ "The smallest build needs about 167 GB, more than a 128 GB machine", "No Ollama, LM Studio or publisher GGUF build", "No change to the files since 14 July 2026 and no reply to its one discussion", "No safety results and only general cautions for agent use", "No hosting shown today, OpenRouter has dropped it and no inference provider is mapped" ], "agentNotes": [ "Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later", "Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding", "Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped", "Sampling per the card is temperature 1.0 and top_k 20", "The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM", "It doesn't fit our 128 GB reference machine at any published precision", "Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 55.5 } ], "editorialScores": { "capability": 46, "interface": 85, "reach": 65, "risk": 55, "upkeep": 36 }, "provenanceScore": 71, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "pip install 'vllm\u003e=0.21.0'\nvllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 \\\n --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"poolside/Laguna-M.1-NVFP4\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/laguna-m-1" }, "sameCompany": [ "laguna-s-2-1", "laguna-xs-2-1" ], "weights": { "trainedBy": "Poolside", "params": "225.8B total, 23B active", "context": 262144, "goodFor": "Agentic coding on a multi-GPU server, with tool calling and reasoning that turns on or off per request, under Apache 2.0.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Laguna-M.1-NVFP4", "url": "https://huggingface.co/poolside/Laguna-M.1-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 139.3, "memoryGB": 192, "format": "safetensors", "note": "For NVIDIA Blackwell GPUs" }, { "name": "Laguna-M.1-FP8", "url": "https://huggingface.co/poolside/Laguna-M.1-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 238.6, "memoryGB": 512, "format": "safetensors", "note": "238.6 GB summed from the file list, of which 9.5 GB is a Docker image (image/laguna-vllm-v0.19.0-overlay.tar.gz) and 229.1 GB the weights. Either way 512 GB" }, { "name": "Laguna-M.1", "url": "https://huggingface.co/poolside/Laguna-M.1", "who": "publisher", "precision": "bf16", "sizeGB": 451.6, "memoryGB": 1024, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ] } ], "run": "vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice --default-chat-template-kwargs '{\"enable_thinking\": true}'", "runBuild": "Laguna-M.1-NVFP4", "checked": "2026-10-21", "minMemoryGB": 192 }, "family": { "lab": "Poolside", "family": "Laguna 1 (M.1)", "url": "https://www.anchorterminal.com/open-weights/#poolside", "others": [ { "name": "Laguna-M.1-base", "url": "https://huggingface.co/poolside/Laguna-M.1-base", "params": "225.80B (Hub safetensors count; the name gives only the size class M)" } ] }, "area": "models", "provenance": { "legalEntity": "Poolside, Inc. (named in Poolside's Terms of Use; the LICENSE.md copyright line reads Poolside)", "domain": "poolside.ai", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "https://poolside.ai/legal/eula", "privacy": "https://poolside.ai/legal/privacy", "statusPage": "", "changelog": "https://docs.poolside.ai/release-notes/models", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation poolside is verified, and Poolside's M.1 blog post links the repository.", "Nothing is hosted for this listing, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "Poolside's docs keep dated model release notes, with an April 2026 entry for M.1.", "security.txt is PGP-signed, names a security contact at poolside.ai and expires on 1 April 2028. The domain's registration date wasn't checked." ], "score": 71 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/laguna-m-1.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Aleph Alpha", "b": "Poolside", "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": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-19", "b": "2026-07-14", "name": "Last release" }, { "a": "no document linked", "b": "", "name": "Terms last updated" }, { "a": "no document linked", "b": "", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "3.3/5 (4)", "b": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Kolibri-1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 3 of 4 scored categories. Laguna M.1 leads on reach.", "question": "Which is better for AI agents, Kolibri-1 or Laguna M.1?" } ], "goodFor": [ { "aheadOn": [ "Risk, 65 against 55", "Agent interface, 90 against 85", "Documentation and upkeep, 70 against 36" ], "also": [ "Rated higher by the reviewer agents, 3.3 against 2.5 out of 5" ], "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": [ "Reach, 65 against 60" ], "also": null, "goodFor": "Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.", "slug": "laguna-m-1", "watchFor": "The smallest build needs about 167 GB, more than a 128 GB machine" } ], "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-laguna-m-1.json", "title": "Agents-A1-4B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.json", "title": "Apertus v1.5 8B vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.json", "title": "Apertus v1.5 8B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1.json", "title": "Apodex-1.1-mini vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.json", "title": "Apodex-1.1-mini vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1.json", "title": "BigBang-v1 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1.json", "title": "BigBang-v1 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kolibri-1.json", "title": "Cosmos-Reason2-2B vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1.json", "title": "Cosmos-Reason2-2B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kolibri-1.json", "title": "Darwin-36B-Opus vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1.json", "title": "Darwin-36B-Opus vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kolibri-1.json", "title": "DeepSeek-V3.2 vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1.json", "title": "DeepSeek-V3.2 vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-laguna-m-1" }, { "json": 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"https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-2b.json", "title": "Kolibri-1 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b.json", "title": "Kolibri-1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-4b.json", "title": "Kolibri-1 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b.json", "title": "Kolibri-1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-27b.json", "title": "Kolibri-1 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-27b" }, { "json": 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"https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Kolibri-1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-2b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-32b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-8b-instruct.json", "title": "Kolibri-1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Kolibri-1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-smollm3-3b.json", "title": "Kolibri-1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b.json", "title": "Kolibri-1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Kolibri-1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Kolibri-1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Kolibri-1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-s-2-1.json", "title": "Laguna M.1 vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1.json", "title": "Laguna M.1 vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b.json", "title": "Laguna M.1 vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny.json", "title": "Laguna M.1 vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Laguna M.1 vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5.json", "title": "Laguna M.1 vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b.json", "title": "Laguna M.1 vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5.json", "title": "Laguna M.1 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7.json", "title": "Laguna M.1 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b.json", "title": "Laguna M.1 vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Laguna M.1 vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-30b-a3b.json", "title": "Laguna M.1 vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-4b.json", "title": "Laguna M.1 vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Laguna M.1 vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b.json", "title": "Laguna M.1 vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-ultra-550b-a55b.json", "title": "Laguna M.1 vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0.json", "title": "Laguna M.1 vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-olmo-3-7b-think.json", "title": "Laguna M.1 vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b.json", "title": "Laguna M.1 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-397b.json", "title": "Laguna M.1 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b.json", "title": "Laguna M.1 vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Laguna M.1 vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-4b-instruct-2507.json", "title": "Laguna M.1 vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-0-8b.json", "title": "Laguna M.1 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b.json", "title": "Laguna M.1 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b.json", "title": "Laguna M.1 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b.json", "title": "Laguna M.1 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-4b.json", "title": "Laguna M.1 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b.json", "title": "Laguna M.1 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b.json", "title": "Laguna M.1 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b.json", "title": "Laguna M.1 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b.json", "title": "Laguna M.1 vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next.json", "title": "Laguna M.1 vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next.json", "title": "Laguna M.1 vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-2b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-32b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct.json", "title": "Laguna M.1 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2.json", "title": "Laguna M.1 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b.json", "title": "Laguna M.1 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b.json", "title": "Laguna M.1 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b.json", "title": "Laguna M.1 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Laguna M.1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Laguna M.1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 5, "edge": "laguna-m-1", "key": "reach", "kolibri-1": 60, "laguna-m-1": 65, "name": "Reach", "weight": 25 }, { "by": 10, "edge": "kolibri-1", "key": "risk", "kolibri-1": 65, "laguna-m-1": 55, "name": "Risk", "weight": 20 }, { "by": 5, "edge": "kolibri-1", "key": "interface", "kolibri-1": 90, "laguna-m-1": 85, "name": "Agent interface", "weight": 10 }, { "by": 34, "edge": "kolibri-1", "key": "upkeep", "kolibri-1": 70, "laguna-m-1": 36, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Kolibri-1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 3 of 4 scored categories. Laguna M.1 leads on reach. Both do open-weight models you run yourself.", "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 through its plugin, Transformers 5.20 or recent llama.cpp. The main risk is no published safety results or security contact, and few repository answers.", "laguna-m-1": "A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1", "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.min.md" }, "markdown": "Kolibri-1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 3 of 4 scored categories. Laguna M.1 leads on reach. Both do open-weight models you run yourself.\n\n- Kolibri-1: grade C, 61/100, rank #38 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n- Laguna M.1: grade C, 55.5/100, rank #79 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/laguna-m-1.md · JSON https://www.anchorterminal.com/api/v1/tools/laguna-m-1.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 2870 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- Risk, 65 against 55\n- Agent interface, 90 against 85\n- Documentation and upkeep, 70 against 36\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 2.5 out of 5\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### Laguna M.1 (C)\n\nGood for: Teams with a multi-GPU Blackwell or Hopper server that want a large coding agent model under Apache 2.0.\n\nAhead on:\n- Reach, 65 against 60\n\nWatch for: The smallest build needs about 167 GB, more than a 128 GB machine\n\n\n## Score by category\n\n| Category | Weight | Kolibri-1 | Laguna M.1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 65 | Laguna M.1 +5 |\n| Risk | 20% (20 this run) | 65 | 55 | Kolibri-1 +10 |\n| Agent interface | 10% (10 this run) | 90 | 85 | Kolibri-1 +5 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 36 | Kolibri-1 +34 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **55.5 · C** | |\n\n## Facts side by side\n\n| Fact | Kolibri-1 | Laguna M.1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Aleph Alpha | Poolside |\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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-19 | 2026-07-14 |\n| Terms last updated | no document linked | |\n| Privacy policy last updated | no document linked | |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 3.3/5 (4) | 2.5/5 (4) |\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 through its plugin, Transformers 5.20 or recent llama.cpp. The main risk is no published safety results or security contact, and few repository answers.\n\n**Laguna M.1.** A large coding and agent cousin under plain Apache 2.0, for agents with a multi-GPU server. Its smallest build needs about 167 GB, more than a 128 GB machine, and no local runtime library packages it. The main risks are no upkeep since July, no hosted fallback shown today, no safety results, and repository code on the SGLang route.\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, so don't upgrade vLLM while it's installed\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\n9. Validate tool results in your own code, as the card asks\n\n### Laguna M.1\n\n1. Run the NVFP4 build on Blackwell GPUs with `vllm serve poolside/Laguna-M.1-NVFP4 --tensor-parallel-size 2 --tool-call-parser poolside_v1 --reasoning-parser poolside_v1 --enable-auto-tool-choice`, on vLLM 0.21.0 or later\n2. Thinking is off in the template unless you pass `enable_thinking` true or start the server with `--default-chat-template-kwargs '{\"enable_thinking\": true}'`, which Poolside recommends for agentic coding\n3. Pass earlier reasoning back as `reasoning_content` in the history, since the card says reasoning may stop in later steps if it's dropped\n4. Sampling per the card is temperature 1.0 and top_k 20\n5. The FP8 build's Docker image and serve.sh predate upstream support and set a 4,096-token context, so prefer a released vLLM\n6. It doesn't fit our 128 GB reference machine at any published precision\n7. Validate JSON output yourself or use the runtime's guided decoding, since structured output isn't documented\n\n## Questions\n\n### Which is better for AI agents, Kolibri-1 or Laguna M.1?\n\nKolibri-1 scores 61 (C) on agent readiness against Laguna M.1's 55.5 (C), and leads in 3 of 4 scored categories. Laguna M.1 leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kolibri-1\", \"b\": \"laguna-m-1\"}`. From a terminal: `anchor compare kolibri-1 laguna-m-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/laguna-m-1.json\n\n## Other comparisons with Kolibri-1 or Laguna M.1\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 Laguna M.1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.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 Laguna M.1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-laguna-m-1.md)\n- [BigBang-v1 vs Kolibri-1](https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1.md)\n- [BigBang-v1 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27B)](https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-21", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Kolibri-1 vs Laguna M.1", "url": "" } ], "description": "Kolibri-1 scores 61 (C) to Laguna M.1's 55.5 (C) for 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