{
  "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": 1211,
        "categoryRank": 32,
        "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": "2026-10-26T10:03:52.72702903Z"
          }
        ],
        "pypiWeekly": 404,
        "securityTxt": {
          "url": "https://aleph-alpha.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-10-26T15:29:46.633360604Z"
        },
        "domain": {
          "domain": "aleph-alpha.com",
          "registered": "2018-08-11",
          "source": "https://rdap.verisign.com/com/v1/domain/aleph-alpha.com",
          "checkedAt": "2026-10-25T14:04:57.6425087Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-26T03:17:52.588355822Z",
            "changedAt": "2026-10-26T03:17:52.588355822Z",
            "fingerprint": "f3c3784222a8"
          }
        ],
        "updatedAt": "2026-10-26T15:29:46.633360604Z"
      }
    },
    "answer": "Kolibri-1 and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 61 (C).",
    "b": {
      "slug": "nanbeige-4-3-33b",
      "name": "Nanbeige4.3-33B",
      "vendor": "Nanbeige LLM Lab (BOSS Zhipin)",
      "vendorUrl": "https://huggingface.co/Nanbeige",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Nanbeige4.3-33B is the Nanbeige LLM Lab's open-weight dense model for agentic coding and office work, 33B parameters on the Llama architecture with a 512K-token context, thinking that switches per request and tool calls in an XML format.",
      "url": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b",
      "markdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json",
      "repo": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
      "license": "Apache-2.0 (Hub metadata only; the repository has no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Nanbeige/Nanbeige4.3-33B"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for Nanbeige/Nanbeige4.3-33B and Nanbeige/Nanbeige4.3-33B-Base (checked 2026-10-25).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 as the card's metadata names it. Neither repository holds a LICENSE file. Running it costs the hardware, 66.26 GB of BF16 files and no quantised build from the publisher. No hosted API or inference provider serves it (checked 2026-10-25).",
      "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 2026-10-25).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-25"
      },
      "docsUrl": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "llama",
        "reasoning",
        "tool-calling",
        "coding",
        "agentic",
        "long-context",
        "safetensors",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-24",
      "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": 1211,
        "categoryRank": 48,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 55,
          "risk": 60,
          "upkeep": 56
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-25"
        },
        "negative": 0,
        "verdict": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.",
        "bestFor": "Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.",
        "strengths": [
          "A plain LlamaForCausalLM with no repository code, so released vLLM, SGLang, Transformers and llama.cpp load it",
          "Apache 2.0 by its metadata, ungated, safetensors only",
          "524,288 tokens of context set in config.json with no rope scaling",
          "Thinking switches per request, and preserve_thinking keeps earlier reasoning for agent loops",
          "Tool calls in an XML format the qwen3_coder parser reads, several per turn",
          "A Nanbeige member answered four of the first ten discussions on release day"
        ],
        "weaknesses": [
          "No independent benchmark result yet; every agentic number on the card is Nanbeige's own run",
          "BF16 only from Nanbeige, about 80 GB to load, and no Ollama or LM Studio build",
          "No LICENSE file in either repository; Apache 2.0 rests on the metadata tag",
          "No safety evaluation, agent-use guidance, training data description or knowledge cut-off",
          "No hosted API or inference provider serves it"
        ],
        "agentNotes": [
          "Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers",
          "Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes",
          "Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends",
          "Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card",
          "Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed",
          "Send one system message at the start only, as the template raises an error for a later one",
          "In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "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": 85,
          "reach": 55,
          "risk": 60,
          "upkeep": 56
        },
        "provenanceScore": 53,
        "checked": {
          "categories": {
            "interface": "2026-10-25",
            "reach": "2026-10-25",
            "risk": "2026-10-25",
            "upkeep": "2026-10-25"
          },
          "last": "2026-10-25",
          "oldest": "2026-10-25",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "# vLLM 0.32.0 or SGLang 0.5.22, as the card's commands (the card uses four GPUs with tensor parallelism)\nhf download Nanbeige/Nanbeige4.3-33B --local-dir Nanbeige4.3-33B\nvllm serve ./Nanbeige4.3-33B --served-model-name nanbeige --tensor-parallel-size 4 \\\n  --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder\n# or\npython -m sglang.launch_server --model-path ./Nanbeige4.3-33B --tp-size 4 \\\n  --reasoning-parser qwen3 --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"nanbeige\", \"messages\": [{\"role\": \"user\", \"content\": \"Which number is bigger, 9.11 or 9.8?\"}],\n       \"temperature\": 0.6, \"top_p\": 0.95, \"chat_template_kwargs\": {\"enable_thinking\": true}}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/nanbeige-4-3-33b"
      },
      "weights": {
        "trainedBy": "Nanbeige LLM Lab (BOSS Zhipin)",
        "builtOn": "Nanbeige/Nanbeige4.3-33B-Base (fine-tune, declared in the card's metadata; Apache 2.0 by its tag, no LICENSE file)",
        "params": "33B",
        "context": 524288,
        "goodFor": "A cousin for long agentic coding and office tasks on a machine with about 96 GB of memory, if Nanbeige's own results hold up, with a 512K context and per-request thinking.",
        "runtimes": [
          "vLLM",
          "SGLang",
          "Transformers",
          "llama.cpp"
        ],
        "builds": [
          {
            "name": "Nanbeige4.3-33B (BF16)",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 66.26,
            "memoryGB": 96,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "Whole repository, 66.24 GB of weights in 14 shards. 66.26 GB × 1.2 = 79.5 GB. The only build from Nanbeige; no Ollama, LM Studio or ggml-org build exists, and llama.cpp needs a GGUF converted with its own script or taken from someone else"
          }
        ],
        "run": "vllm serve Nanbeige/Nanbeige4.3-33B --tensor-parallel-size 4 --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder",
        "runBuild": "Nanbeige4.3-33B (BF16)",
        "checked": "2026-10-25",
        "minMemoryGB": 96
      },
      "family": {
        "lab": "Nanbeige LLM Lab (BOSS Zhipin)",
        "family": "Nanbeige4.3",
        "url": "https://www.anchorterminal.com/open-weights/#nanbeige",
        "others": [
          {
            "name": "Nanbeige4.3-33B-Base",
            "url": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B-Base",
            "params": "33B"
          }
        ],
        "olderCount": 7
      },
      "area": "models",
      "provenance": {
        "legalEntity": "BOSS Zhipin (Kanzhun Limited), unconfirmed",
        "domain": "zhipin.com",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Nanbeige/Nanbeige4.3-33B/commits/main",
        "securityTxt": "none",
        "checked": "2026-10-25",
        "notes": [
          "The card names the Nanbeige Team and nothing else. BOSS Zhipin is identified as the maker by the lab's Nanbeige4.2-3B report on arXiv (Nanbeige LLM Lab, Boss Zhipin), which links huggingface.co/Nanbeige, and by the chat template's default system prompt naming BOSS Zhipin as developer and trainer. The card's contact address is on kanzhun.com. Kanzhun Limited as BOSS Zhipin's operator wasn't confirmed first-hand, since ir.zhipin.com refused our reader. The domain's registration date wasn't checked.",
          "The Hugging Face organisation Nanbeige (Nanbeige LLM Lab) isn't verified by the Hub. The arXiv report's link to it is the publisher's own documentation pointing there.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history.",
          "www.zhipin.com/.well-known/security.txt answers 404 and kanzhun.com's redirects to the home page. BOSS Zhipin runs a security response centre at security.zhipin.com for reporting flaws in its products; nothing links it to the model."
        ],
        "score": 53
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/nanbeige-4-3-33b.json"
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Aleph Alpha",
        "b": "Nanbeige LLM Lab (BOSS Zhipin)",
        "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 (Hub metadata only; the repository has no LICENSE file)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-19",
        "b": "2026-10-24",
        "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": "3.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Kolibri-1 and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 61 (C).",
        "question": "Which is better for AI agents, Kolibri-1 or Nanbeige4.3-33B?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Reach, 60 against 55",
          "Risk, 65 against 60",
          "Agent interface, 90 against 85",
          "Documentation and upkeep, 70 against 56"
        ],
        "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": "Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.",
        "slug": "nanbeige-4-3-33b",
        "watchFor": "No independent benchmark result yet; every agentic number on the card is Nanbeige's own run"
      }
    ],
    "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-nanbeige-4-3-33b.json",
        "title": "Agents-A1-4B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b"
      },
      {
        "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-nanbeige-4-3-33b.json",
        "title": "Apertus v1.5 8B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b"
      },
      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b.json",
        "title": "Apodex-1.1-mini vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-3-33b.json",
        "title": "BigBang-v1 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-kolibri-1.json",
        "title": "Carbon-X vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b.json",
        "title": "Carbon-X vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b"
      },
      {
        "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-nanbeige-4-3-33b.json",
        "title": "Cosmos-Reason2-2B vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b.json",
        "title": "Darwin-36B-Opus vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b"
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      {
        "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-nanbeige-4-3-33b.json",
        "title": "DeepSeek-V3.2 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-nanbeige-4-3-33b"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-3-33b.json",
        "title": "DeepSeek V4.1 Flash vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kolibri-1.json",
        "title": "DeepSeek-V4-Flash-0731 vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nanbeige-4-3-33b.json",
        "title": "DeepSeek-V4-Flash-0731 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kolibri-1.json",
        "title": "DiffusionGemma 26B A4B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nanbeige-4-3-33b.json",
        "title": "DiffusionGemma 26B A4B IT vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kolibri-1.json",
        "title": "Gemma 4 12B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1.json",
        "title": "Gemma 4 26B A4B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.json",
        "title": "Gemma 4 31B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nanbeige-4-3-33b.json",
        "title": "Gemma 4 31B IT vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kolibri-1.json",
        "title": "Gemma 4 E2B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nanbeige-4-3-33b.json",
        "title": "Gemma 4 E2B IT vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kolibri-1.json",
        "title": "Gemma 4 E4B IT vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b.json",
        "title": "Gemma 4 E4B IT vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1.json",
        "title": "GLM-4.5-Air vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-5-air-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kolibri-1.json",
        "title": "GLM-4.7-Flash vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1.json",
        "title": "GLM-5.3-Flash vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nanbeige-4-3-33b.json",
        "title": "GLM-5.3-Flash vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-kolibri-1.json",
        "title": "GLM-5.3 vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-nanbeige-4-3-33b.json",
        "title": "GLM-5.3 vs Nanbeige4.3-33B",
        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kolibri-1"
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      {
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kolibri-1"
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      {
        "json": "https://www.anchorterminal.com/compare/gpt-oss-20b-vs-nanbeige-4-3-33b.json",
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      {
        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kolibri-1"
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        "json": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/granite-4-2-3b-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-kolibri-1.json",
        "title": "Hy3 vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/hy3-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/hy3-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/hy3-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-kolibri-1.json",
        "title": "Inkling-Small vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-small-vs-nanbeige-4-3-33b"
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      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-nanbeige-4-3-33b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kolibri-1.json",
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        "json": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-3-33b.json",
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        "url": "https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-nanbeige-4-3-33b"
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      {
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-kronos"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-7.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-7"
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      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-0-8b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-0-8b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-122B-A10B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-122b-a10b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-2b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-2B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-397B-A17B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-397b-a17b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-4b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.5-9B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.6-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-35b-a3b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.6-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-6-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next.json",
        "title": "Nanbeige4.3-33B vs Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-30b-a3b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-Coder-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-next.json",
        "title": "Nanbeige4.3-33B vs Qwen3-Coder-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-coder-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-omni-30b-a3b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-Omni-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-omni-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-2b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-32b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct.json",
        "title": "Nanbeige4.3-33B vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2.json",
        "title": "Nanbeige4.3-33B vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint.json",
        "title": "Nanbeige4.3-33B vs Recursive Flint",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-recursive-flint"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b.json",
        "title": "Nanbeige4.3-33B vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b.json",
        "title": "Nanbeige4.3-33B vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "Nanbeige4.3-33B vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Nanbeige4.3-33B vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/nanbeige-4-3-33b-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "kolibri-1",
        "key": "reach",
        "kolibri-1": 60,
        "name": "Reach",
        "nanbeige-4-3-33b": 55,
        "weight": 25
      },
      {
        "by": 5,
        "edge": "kolibri-1",
        "key": "risk",
        "kolibri-1": 65,
        "name": "Risk",
        "nanbeige-4-3-33b": 60,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "kolibri-1",
        "key": "interface",
        "kolibri-1": 90,
        "name": "Agent interface",
        "nanbeige-4-3-33b": 85,
        "weight": 10
      },
      {
        "by": 14,
        "edge": "kolibri-1",
        "key": "upkeep",
        "kolibri-1": 70,
        "name": "Documentation and upkeep",
        "nanbeige-4-3-33b": 56,
        "weight": 10
      }
    ],
    "summary": "Kolibri-1 and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 61 (C). 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.",
      "nanbeige-4-3-33b": "A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b",
    "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.min.md"
  },
  "markdown": "Kolibri-1 and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 61 (C). Both do open-weight models you run yourself.\n\n- Kolibri-1: grade C, 61/100, rank #40 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n- Nanbeige4.3-33B: grade C, 61/100, rank #56 of 111 open-weight models. Markdown https://www.anchorterminal.com/tools/nanbeige-4-3-33b.md · JSON https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.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 3341 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, 60 against 55\n- Risk, 65 against 60\n- Agent interface, 90 against 85\n- Documentation and upkeep, 70 against 56\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### Nanbeige4.3-33B (C)\n\nGood for: Agents with about 96 GB of GPU memory, or the 128 GB reference machine, that want an Apache-tagged 33B model for long coding and tool loops and can wait for independent results.\n\nWatch for: No independent benchmark result yet; every agentic number on the card is Nanbeige's own run\n\n\n## Score by category\n\n| Category | Weight | Kolibri-1 | Nanbeige4.3-33B | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 55 | Kolibri-1 +5 |\n| Risk | 20% (20 this run) | 65 | 60 | Kolibri-1 +5 |\n| Agent interface | 10% (10 this run) | 90 | 85 | Kolibri-1 +5 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 56 | Kolibri-1 +14 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Kolibri-1 | Nanbeige4.3-33B |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Aleph Alpha | Nanbeige LLM Lab (BOSS Zhipin) |\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 (Hub metadata only; the repository has no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-19 | 2026-10-24 |\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 | 3.3/5 (4) | none |\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**Nanbeige4.3-33B.** A dense 33B Llama-architecture cousin for long agentic coding and office tasks, with per-request thinking, XML tool calls that stock vLLM and SGLang parse, and 512K of context. It needs about 96 GB at BF16, as Nanbeige ships no quantised build. Its agentic results so far are Nanbeige's own, with no safety results, training data notes or licence file.\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### Nanbeige4.3-33B\n\n1. Serve with `vllm serve Nanbeige/Nanbeige4.3-33B --enable-auto-tool-choice --reasoning-parser qwen3 --tool-call-parser qwen3_coder`, as the card says, or SGLang with the same two parsers\n2. Keep `tool_call_format` at its default `xml`, since qwen3_coder parses that format; the `json` option writes Hermes-style JSON calls, which need a different parser such as vLLM's hermes\n3. Pass `chat_template_kwargs` `{\"preserve_thinking\": true}` for multi-turn tool use and coding, and `{\"enable_thinking\": false}` to skip reasoning, as the card recommends\n4. Use temperature 1.0, top_p 0.95 and up to 65,536 new tokens for agent work, and temperature 0.6 for reasoning and chat, per the card\n5. Send your own system prompt, since without one the template injects a Chinese identity line, or a Chinese tool-expert prompt when tools are passed\n6. Send one system message at the start only, as the template raises an error for a later one\n7. In plain Transformers pass `use_cache=True` to generate, since config.json and generation_config.json set it false; trust_remote_code isn't needed\n\n## Questions\n\n### Which is better for AI agents, Kolibri-1 or Nanbeige4.3-33B?\n\nKolibri-1 and Nanbeige4.3-33B score within a point of each other on agent readiness, 61 (C) and 61 (C).\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kolibri-1\", \"b\": \"nanbeige-4-3-33b\"}`. From a terminal: `anchor compare kolibri-1 nanbeige-4-3-33b`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/nanbeige-4-3-33b.json\n\n## Other comparisons with Kolibri-1 or Nanbeige4.3-33B\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 Nanbeige4.3-33B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-nanbeige-4-3-33b.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 Nanbeige4.3-33B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nanbeige-4-3-33b.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 Nanbeige4.3-33B](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-nanbeige-4-3-33b.md)\n- [BigBang-v1 vs Kolibri-1](https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1.md)\n- [BigBang-v1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/bigbang-v1-vs-nanbeige-4-3-33b.md)\n- [Carbon-X vs Kolibri-1](https://www.anchorterminal.com/compare/carbon-x-vs-kolibri-1.md)\n- [Carbon-X vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/carbon-x-vs-nanbeige-4-3-33b.md)\n- [Cosmos-Reason2-2B vs Kolibri-1](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kolibri-1.md)\n- [Cosmos-Reason2-2B vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-nanbeige-4-3-33b.md)\n- [Darwin-36B-Opus vs Kolibri-1](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-kolibri-1.md)\n- [Darwin-36B-Opus vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-nanbeige-4-3-33b.md)\n- [DeepSeek-V3.2 vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-kolibri-1.md)\n- [DeepSeek-V3.2 vs 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