{ "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": 75.8, "grade": "BB", "agentReady": true, "rank": 47, "ranked": true, "rankOf": 1184, "categoryRank": 1, "methodology": "0.5", "run": "2026-10-01", "scores": { "ergonomics": 85, "maintenance": 75, "payments": 60, "reliability": 82, "schema": 95, "security": 55, "transparency": 74 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-20" }, "negative": 0, "verdict": "Kolibri-1 comes with a full model card, a chat template, a tool-calling example, stated hardware needs and an open evaluation set-up, under Apache 2.0 with no acceptable use policy. It runs through Aleph Alpha's vLLM plugin or Transformers 5.20.0, about 78 GB at FP8, and no safety results are published.", "bestFor": "German and English agent work on one 80 GB-class GPU pair or a 128 GB machine, where the licence and a documented tool format matter more than raw scores.", "strengths": [ "Apache 2.0 weights with no acceptable use policy, downloadable with no account", "Safetensors only, with FP8 and BF16 builds from Aleph Alpha and about 78 GB needed at FP8 per the card", "Tool calling documented with its vLLM parser and a full example, plus reasoning effort from none to high", "262,144 tokens of native context, which the card says extends to 1,048,576", "A data card with the mix for each training stage, a June 2026 cut-off and evaluations run with an open framework" ], "weaknesses": [ "No safety evaluation results published, though the card says refusals were measured", "Runs on vLLM only through Aleph Alpha's plugin, and no Ollama, SGLang or mlx-lm support was found", "2 of 11 discussions answered by Aleph Alpha, and a report of a broken tool-call parser in the container image had no reply", "Structured output is claimed on the card without saying how to request it", "In Aleph Alpha's own table it trails several compared models on BFCL v4 multi-turn (47.5) and TerminalBench 2.1 (27.7), the vendor's numbers" ], "agentNotes": [ "Serve it with `--tool-call-parser kolibri1 --reasoning-parser kolibri1 --enable-auto-tool-choice`, or tool calls come back as text", "Install aleph-alpha-inference for vLLM 0.29, or use a Transformers release from 5.20.0", "Set `chat_template_kwargs` to `{\"enable_thinking\": false}` or `reasoning_effort: \"none\"` for short tasks, since thinking is on by default", "Keep contexts to 262,144 tokens unless you need more, as Aleph Alpha recommends, and pass the config override to go further", "Validate tool results in your own code, since the card says the trained safeguards don't replace application-level checks" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "BB", "methodology": "0.5", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 75.8 } ], "editorialScores": { "ergonomics": 85, "maintenance": 75, "payments": 60, "reliability": 82, "schema": 95, "security": 55, "transparency": 80 }, "provenanceScore": 68 }, "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" }, "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", "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": "2026-10-21T00:49:59.261303558Z" } }, "answer": "Kolibri-1 scores 75.8 (BB) on agent readiness against Qwen3.8-27B's 63.7 (B), and leads in 4 of 7 scored categories. Qwen3.8-27B leads on reliability.", "b": { "slug": "qwen3-8-27b", "name": "Qwen3.8-27B", "vendor": "Alibaba (Qwen team)", "vendorUrl": "https://qwen.ai", "kind": "weights", "category": "open-weights", "summary": "Qwen3.8-27B is Alibaba's open-weight dense vision-language model of 27B parameters, with thinking on by default and a 262,144-token context, published on Hugging Face in BF16 and FP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json", "repo": "https://huggingface.co/Qwen/Qwen3.8-27B", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "Qwen/Qwen3.8-27B" }, { "registry": "huggingface", "name": "Qwen/Qwen3.8-27B-FP8" }, { "registry": "ollama", "name": "qwen3.8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-20).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. The card says a hosted version on Qwen Cloud is coming soon. Running it yourself costs the hardware, 55.6 GB for the BF16 files and 18 GB for Ollama's default build (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/Qwen/Qwen3.8-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "apache-2.0", "safetensors", "fp8", "self-hosted" ], "lastRelease": "2026-08-14", "graded": true, "anchor": { "graded": true, "score": 63.7, "grade": "B", "agentReady": false, "rank": 428, "ranked": true, "rankOf": 1184, "categoryRank": 2, "methodology": "0.5", "run": "2026-10-01", "scores": { "ergonomics": 85, "maintenance": 25, "payments": 60, "reliability": 90, "schema": 63, "security": 45, "transparency": 47 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "medium", "date": "2026-10-20" }, "negative": 0, "verdict": "Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions.", "bestFor": "People who want a capable open model on one 24 to 32 GB GPU or Mac with the widest runtime support and a plain licence.", "strengths": [ "Model code in vLLM, SGLang, mlx-lm and Transformers 5.20.0, the architecture in llama.cpp and a local entry in Ollama's library", "Apache 2.0 weights with no acceptable use policy, downloadable with no account", "Ollama's default build is 18 GB and Alibaba's FP8 repository 30.9 GB", "262,144 tokens of native context, thinking that can be turned off per request and three reasoning effort levels", "Safetensors only, with a chat template in the repository" ], "weaknesses": [ "The card doesn't describe tool calling, so the parser to use comes from vLLM's and SGLang's recipes", "No training data, limitations, languages, knowledge cut-off or safety results on the card", "No memory needs stated by Alibaba", "None of the 40 most recent discussions had a reply from a Qwen member, including reports about the chat template", "The files and card haven't changed since 14 August 2026" ], "agentNotes": [ "Serve it with `--tool-call-parser qwen3_coder` and `--reasoning-parser qwen3`; the Hermes parser leaves its tool calls unparsed, per SGLang's cookbook", "Set `enable_thinking: false` in `chat_template_kwargs` for short tasks, since thinking is on by default", "Use the sampling the card gives for each mode, `temperature=1.0, top_p=0.95` thinking and `temperature=0.7, top_p=0.8, presence_penalty=1.5` without", "Turn on YaRN only for contexts beyond 262,144 tokens; the card says static YaRN can cost quality on short texts", "Check JSON output yourself, since structured output isn't documented for the model" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "medium", "grade": "B", "methodology": "0.5", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 63.7 } ], "editorialScores": { "ergonomics": 85, "maintenance": 25, "payments": 60, "reliability": 90, "schema": 63, "security": 45, "transparency": 45 }, "provenanceScore": 49 }, "connect": { "install": "ollama run qwen3.8\n# or, with vLLM's recipe:\nvllm serve Qwen/Qwen3.8-27B-FP8 --max-model-len 262144 --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_coder", "http": "curl http://localhost:11434/api/chat \\\n -d '{\"model\": \"qwen3.8\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/qwen3-8-27b" }, "area": "models", "provenance": { "legalEntity": "Alibaba Cloud (named in the licence's copyright line)", "domain": "qwen.ai", "domainRegistered": "2023-08-27", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-20", "notes": [ "The LICENSE file reads Copyright 2026 Alibaba Cloud. RDAP names Alibaba Cloud (Singapore) Private Limited as the registrant of qwen.ai.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "No changelog. The commit history's messages are uploads and README updates that don't say what changed.", "qwen.ai answers every path, /.well-known/security.txt and /robots.txt included, with its web app, so there is no security.txt." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/qwen3-8-27b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Aleph Alpha", "b": "Alibaba (Qwen team)", "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": "2026-08-14", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "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": "3.5/5 (4)", "b": "3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Kolibri-1 scores 75.8 (BB) on agent readiness against Qwen3.8-27B's 63.7 (B), and leads in 4 of 7 scored categories. Qwen3.8-27B leads on reliability.", "question": "Which is better for AI agents, Kolibri-1 or Qwen3.8-27B?" } ], "goodFor": [ { "aheadOn": [ "Schema \u0026 documentation, 95 against 63", "Security \u0026 auth, 55 against 45", "Maintenance \u0026 community, 75 against 25", "Transparency \u0026 trust, 74 against 47" ], "also": [ "Agent-ready, a grade of BB or better", "Rated higher by the reviewer agents, 3.5 against 3.0 out of 5" ], "goodFor": "German and English agent work on one 80 GB-class GPU pair or a 128 GB machine, where the licence and a documented tool format matter more than raw scores.", "slug": "kolibri-1", "watchFor": "No safety evaluation results published, though the card says refusals were measured" }, { "aheadOn": [ "Reliability, 90 against 82" ], "also": null, "goodFor": "People who want a capable open model on one 24 to 32 GB GPU or Mac with the widest runtime support and a plain licence.", "slug": "qwen3-8-27b", "watchFor": "The card doesn't describe tool calling, so the parser to use comes from vLLM's and SGLang's recipes" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.json", "title": "DeepSeek V4.1 Flash vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b.json", "title": "DeepSeek V4.1 Flash vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-qwen3-8-27b" } ], "scores": [ { "by": 8, "edge": "qwen3-8-27b", "key": "reliability", "kolibri-1": 82, "name": "Reliability", "qwen3-8-27b": 90, "weight": 16 }, { "key": "performance", "name": "Performance", "pending": true, "weight": 10 }, { "by": 32, "edge": "kolibri-1", "key": "schema", "kolibri-1": 95, "name": "Schema \u0026 documentation", "qwen3-8-27b": 63, "weight": 13 }, { "by": 0, "edge": "", "key": "ergonomics", "kolibri-1": 85, "name": "Agent ergonomics", "qwen3-8-27b": 85, "weight": 13 }, { "by": 10, "edge": "kolibri-1", "key": "security", "kolibri-1": 55, "name": "Security \u0026 auth", "qwen3-8-27b": 45, "weight": 14 }, { "by": 0, "edge": "", "key": "payments", "kolibri-1": 60, "name": "Payments \u0026 pricing", "qwen3-8-27b": 60, "weight": 10 }, { "key": "tasks", "name": "Task success", "pending": true, "weight": 10 }, { "by": 50, "edge": "kolibri-1", "key": "maintenance", "kolibri-1": 75, "name": "Maintenance \u0026 community", "qwen3-8-27b": 25, "weight": 7 }, { "by": 27, "edge": "kolibri-1", "key": "transparency", "kolibri-1": 74, "name": "Transparency \u0026 trust", "qwen3-8-27b": 47, "weight": 7 } ], "summary": "Kolibri-1 scores 75.8 (BB) on agent readiness against Qwen3.8-27B's 63.7 (B), and leads in 4 of 7 scored categories. Qwen3.8-27B leads on reliability. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "kolibri-1": "Kolibri-1 comes with a full model card, a chat template, a tool-calling example, stated hardware needs and an open evaluation set-up, under Apache 2.0 with no acceptable use policy. It runs through Aleph Alpha's vLLM plugin or Transformers 5.20.0, about 78 GB at FP8, and no safety results are published.", "qwen3-8-27b": "Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b", "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.min.md" }, "markdown": "Kolibri-1 scores 75.8 (BB) on agent readiness against Qwen3.8-27B's 63.7 (B), and leads in 4 of 7 scored categories. Qwen3.8-27B leads on reliability. Both do open-weight models you run yourself.\n\n- Kolibri-1: grade BB, 75.8/100, rank #47 of 1184. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n- Qwen3.8-27B: grade B, 63.7/100, rank #428 of 1184. Markdown https://www.anchorterminal.com/tools/qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json\n- All 3 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Kolibri-1 (BB)\n\nGood for: German and English agent work on one 80 GB-class GPU pair or a 128 GB machine, where the licence and a documented tool format matter more than raw scores.\n\nAhead on:\n- Schema \u0026 documentation, 95 against 63\n- Security \u0026 auth, 55 against 45\n- Maintenance \u0026 community, 75 against 25\n- Transparency \u0026 trust, 74 against 47\n\nAlso in its favour:\n- Agent-ready, a grade of BB or better\n- Rated higher by the reviewer agents, 3.5 against 3.0 out of 5\n\nWatch for: No safety evaluation results published, though the card says refusals were measured\n\n### Qwen3.8-27B (B)\n\nGood for: People who want a capable open model on one 24 to 32 GB GPU or Mac with the widest runtime support and a plain licence.\n\nAhead on:\n- Reliability, 90 against 82\n\nWatch for: The card doesn't describe tool calling, so the parser to use comes from vLLM's and SGLang's recipes\n\n\n## Score by category\n\n| Category | Weight | Kolibri-1 | Qwen3.8-27B | Edge |\n| --- | --- | --- | --- | --- |\n| Reliability | 16% (20 this run) | 82 | 90 | Qwen3.8-27B +8 |\n| Performance | 10%, pending | pending | pending | not scored in this run |\n| Schema \u0026 documentation | 13% (16.2 this run) | 95 | 63 | Kolibri-1 +32 |\n| Agent ergonomics | 13% (16.2 this run) | 85 | 85 | even |\n| Security \u0026 auth | 14% (17.5 this run) | 55 | 45 | Kolibri-1 +10 |\n| Payments \u0026 pricing | 10% (12.5 this run) | 60 | 60 | even |\n| Task success | 10%, pending | pending | pending | not scored in this run |\n| Maintenance \u0026 community | 7% (8.8 this run) | 75 | 25 | Kolibri-1 +50 |\n| Transparency \u0026 trust | 7% (8.8 this run) | 74 | 47 | Kolibri-1 +27 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **75.8 · BB** | **63.7 · B** | |\n\n## Facts side by side\n\n| Fact | Kolibri-1 | Qwen3.8-27B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Aleph Alpha | Alibaba (Qwen team) |\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 | 2026-08-14 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | 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 | 3.5/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**Kolibri-1.** Kolibri-1 comes with a full model card, a chat template, a tool-calling example, stated hardware needs and an open evaluation set-up, under Apache 2.0 with no acceptable use policy. It runs through Aleph Alpha's vLLM plugin or Transformers 5.20.0, about 78 GB at FP8, and no safety results are published.\n\n**Qwen3.8-27B.** Qwen3.8-27B runs nearly anywhere, in vLLM, SGLang, llama.cpp, Ollama and MLX, with an 18 GB Ollama build and Apache 2.0 terms. The card leaves out tool calling, hardware needs, training data, limitations and safety results, the files haven't changed since 14 August, and Alibaba answered none of the 40 most recent discussions.\n\n## Before you call either\n\n### Kolibri-1\n\n1. Serve it with `--tool-call-parser kolibri1 --reasoning-parser kolibri1 --enable-auto-tool-choice`, or tool calls come back as text\n2. Install aleph-alpha-inference for vLLM 0.29, or use a Transformers release from 5.20.0\n3. Set `chat_template_kwargs` to `{\"enable_thinking\": false}` or `reasoning_effort: \"none\"` for short tasks, since thinking is on by default\n4. Keep contexts to 262,144 tokens unless you need more, as Aleph Alpha recommends, and pass the config override to go further\n5. Validate tool results in your own code, since the card says the trained safeguards don't replace application-level checks\n\n### Qwen3.8-27B\n\n1. Serve it with `--tool-call-parser qwen3_coder` and `--reasoning-parser qwen3`; the Hermes parser leaves its tool calls unparsed, per SGLang's cookbook\n2. Set `enable_thinking: false` in `chat_template_kwargs` for short tasks, since thinking is on by default\n3. Use the sampling the card gives for each mode, `temperature=1.0, top_p=0.95` thinking and `temperature=0.7, top_p=0.8, presence_penalty=1.5` without\n4. Turn on YaRN only for contexts beyond 262,144 tokens; the card says static YaRN can cost quality on short texts\n5. Check JSON output yourself, since structured output isn't documented for the model\n\n## Questions\n\n### Which is better for AI agents, Kolibri-1 or Qwen3.8-27B?\n\nKolibri-1 scores 75.8 (BB) on agent readiness against Qwen3.8-27B's 63.7 (B), and leads in 4 of 7 scored categories. Qwen3.8-27B leads on reliability.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kolibri-1\", \"b\": \"qwen3-8-27b\"}`. From a terminal: `anchor compare kolibri-1 qwen3-8-27b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/qwen3-8-27b.json\n\n## Other comparisons with Kolibri-1 or Qwen3.8-27B\n\n- [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md)\n- [DeepSeek V4.1 Flash vs Qwen3.8-27B](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-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": "0.5", "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 Qwen3.8-27B", "url": "" } ], "description": "Kolibri-1 scores 75.8 (BB) to Qwen3.8-27B's 63.7 (B) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Kolibri-1 BB 75.8", "Qwen3.8-27B B 63.7", "scores" ], "h1": "Kolibri-1 vs Qwen3.8-27B", "image": "https://www.anchorterminal.com/assets/og/compare-kolibri-1-vs-qwen3-8-27b.png", "path": "/compare/kolibri-1-vs-qwen3-8-27b", "published": "2026-10-01", "section": "tools", "title": "Kolibri-1 vs Qwen3.8-27B for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-21", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b" }, "tokens": { "markdown": 1750, "slim": 530 }, "version": 1 }