{
  "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": 1199,
        "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": "2026-10-22T07:50:42.695376721Z"
          }
        ],
        "pypiWeekly": 639,
        "securityTxt": {
          "url": "https://aleph-alpha.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-10-22T16:01:05.839466038Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-10-22T01:18:53.205169289Z",
            "changedAt": "2026-10-22T01:18:53.205169289Z",
            "fingerprint": "592fd481365b"
          }
        ],
        "updatedAt": "2026-10-22T16:01:05.839466038Z"
      }
    },
    "answer": "Kolibri-1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 4 scored categories. MiniMax-M2.5 leads on reach.",
    "b": {
      "slug": "minimax-m2-5",
      "name": "MiniMax-M2.5",
      "vendor": "MiniMax",
      "vendorUrl": "https://www.minimax.io",
      "kind": "weights",
      "category": "open-weights",
      "summary": "MiniMax-M2.5 is MiniMax's open-weight mixture-of-experts model for coding and agent work, about 229B parameters in FP8 with a 196,608-token context and thinking always on, published on Hugging Face on 12 February 2026.",
      "url": "https://www.anchorterminal.com/tools/minimax-m2-5",
      "markdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json",
      "repo": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
      "license": "MiniMax Model License (model) and modified MIT (code)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "MiniMaxAI/MiniMax-M2.5"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included and royalty-free, under the MiniMax Model License for the weights and a modified MIT licence for the code, both with attribution duties and a Prohibited Uses Policy for the model. Running it yourself costs the hardware, 230.2 GB of FP8 files. MiniMax's API closed M2.5 to new users on 19 October 2026 and retires it on 18 November 2026 (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/MiniMaxAI/MiniMax-M2.5",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "coding",
        "custom-licence",
        "safetensors",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-02-12",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 57.5,
        "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": 1199,
        "categoryRank": 57,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 22
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "high",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.",
        "bestFor": "Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "strengths": [
          "Runs in released vLLM, SGLang, llama.cpp, Transformers and LM Studio",
          "Tool-call format documented by MiniMax with named vLLM and SGLang parsers and a parsing function",
          "Several tool calls in one block, and 196,608 tokens of context",
          "Safetensors only, in the publisher's organisation with a SHA-256 for every shard",
          "No gate or account, and served by two inference providers on Hugging Face"
        ],
        "weaknesses": [
          "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine",
          "Thinking is always on, with no documented way to turn it off or budget it",
          "The card says modified MIT and links to a deleted file, while the licence in the repository adds a Prohibited Uses Policy and termination for breach",
          "No safety evaluation, agent guidance or security contact",
          "No MiniMax reply in discussions since April 2026, and the card leaves out limitations, architecture, parameters and a cut-off"
        ],
        "agentNotes": [
          "Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
          "In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code",
          "MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache",
          "On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory",
          "Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends",
          "Budget output tokens for thinking on every turn, since the template always opens a think block",
          "Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy",
          "Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 1.8,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "high",
            "grade": "C",
            "methodology": "0.6",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 57.5
          }
        ],
        "editorialScores": {
          "capability": 73,
          "interface": 75,
          "reach": 65,
          "risk": 30,
          "upkeep": 22
        },
        "provenanceScore": 55,
        "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": "# MiniMax's vLLM guide, 4 GPUs\nuv pip install vllm --torch-backend=auto\nSAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code \\\n  --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 \\\n  --reasoning-parser minimax_m2_append_think",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\"model\": \"MiniMaxAI/MiniMax-M2.5\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}], \"temperature\": 1.0, \"top_p\": 0.95, \"top_k\": 40}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/minimax-m2-5"
      },
      "sameCompany": [
        "minimax-api",
        "minimax-video",
        "minimax-voice-cloning",
        "minimax-code",
        "minimax-m2-7"
      ],
      "weights": {
        "trainedBy": "MiniMax",
        "params": "228.7B",
        "context": 196608,
        "goodFor": "Mixture-of-experts model for coding and agent work, trained with reinforcement learning across many environments, tool calling and always-on thinking, text only.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "LM Studio",
          "Transformers"
        ],
        "builds": [
          {
            "name": "MiniMax-M2.5",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.5",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 230.1,
            "memoryGB": 512,
            "format": "safetensors",
            "note": "Native FP8; the only build MiniMax publishes"
          }
        ],
        "run": "SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think",
        "runBuild": "MiniMax-M2.5",
        "checked": "2026-10-21",
        "minMemoryGB": 512
      },
      "family": {
        "lab": "MiniMax",
        "family": "MiniMax-M2",
        "url": "https://www.anchorterminal.com/open-weights/#minimax",
        "others": [
          {
            "name": "MiniMax-M2",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2",
            "params": "230B total, 10B active"
          },
          {
            "name": "MiniMax-M2.1",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.1",
            "params": "230B total, 10B active (Hub count; card doesn't restate)"
          },
          {
            "name": "MiniMax-M2.7",
            "url": "https://huggingface.co/MiniMaxAI/MiniMax-M2.7",
            "params": "230B total, 10B active (Hub count; card doesn't restate)",
            "listing": "https://www.anchorterminal.com/tools/minimax-m2-7"
          }
        ],
        "olderCount": 7,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "MiniMax AI (named in the licences; MiniMax's platform terms name Nanonoble Pte. Ltd., Singapore)",
        "domain": "minimax.io",
        "domainRegistered": "2021-03-27",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The MiniMax Model License and the MIT code licence name MiniMax AI. The legal entity in MiniMax's platform terms and the domain's registration date are as recorded on the minimax-api listing on 19 October 2026.",
          "Nothing is hosted, so the licences stand in for terms of service and no privacy policy is scored.",
          "No changelog. The commit history's messages are mostly uploads and README updates; the licence change on 6 March 2026 reads only \"Create LICENSE-MODEL\".",
          "www.minimax.io/.well-known/security.txt redirects to the home page on 21 October 2026, so there is no security.txt.",
          "MiniMax's site (www.minimax.io/models/text) links to huggingface.co/MiniMaxAI/MiniMax-M2.5, so the repository is the publisher's own."
        ],
        "score": 55
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/minimax-m2-5.json",
      "live": {
        "slug": "minimax-m2-5",
        "securityTxt": {
          "url": "https://minimax.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-22T16:01:40.863107959Z"
        },
        "updatedAt": "2026-10-22T16:01:40.863107959Z"
      }
    },
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Aleph Alpha",
        "b": "MiniMax",
        "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": "MiniMax Model License (model) and modified MIT (code)",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-10-19",
        "b": "2026-02-12",
        "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": "1.8/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Kolibri-1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 4 scored categories. MiniMax-M2.5 leads on reach.",
        "question": "Which is better for AI agents, Kolibri-1 or MiniMax-M2.5?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 30",
          "Agent interface, 90 against 75",
          "Documentation and upkeep, 70 against 22"
        ],
        "also": [
          "Rated higher by the reviewer agents, 3.3 against 1.8 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 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy",
        "slug": "minimax-m2-5",
        "watchFor": "230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine"
      }
    ],
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
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        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.json",
        "title": "Agents-A1-4B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5"
      },
      {
        "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-minimax-m2-5.json",
        "title": "Apertus v1.5 8B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5"
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      {
        "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-minimax-m2-5.json",
        "title": "Apodex-1.1-mini vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5"
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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-minimax-m2-5.json",
        "title": "BigBang-v1 vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-minimax-m2-5"
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      {
        "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"
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      {
        "json": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5.json",
        "title": "Cosmos-Reason2-2B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-minimax-m2-5"
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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-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/darwin-36b-opus-vs-minimax-m2-5"
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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"
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      {
        "json": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5.json",
        "title": "DeepSeek-V3.2 vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v3-2-vs-minimax-m2-5"
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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-minimax-m2-5.json",
        "title": "DeepSeek V4.1 Flash vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-minimax-m2-5"
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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-minimax-m2-5.json",
        "title": "DeepSeek-V4-Flash-0731 vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-minimax-m2-5"
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      {
        "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"
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      {
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        "url": "https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-minimax-m2-5"
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      {
        "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"
      },
      {
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        "url": "https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-5"
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      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1.json",
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        "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-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kolibri-1"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-minimax-m2-5"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kolibri-1"
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        "url": "https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kolibri-1.json",
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        "json": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/glm-4-7-flash-vs-minimax-m2-5"
      },
      {
        "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"
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-flash-vs-minimax-m2-5"
      },
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        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-kolibri-1.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/glm-5-3-vs-minimax-m2-5"
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        "json": "https://www.anchorterminal.com/compare/inkling-small-vs-minimax-m2-5.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-kolibri-1.json",
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        "json": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-5.json",
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        "url": "https://www.anchorterminal.com/compare/inkling-vs-minimax-m2-5"
      },
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        "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/kolibri-1-vs-lfm2-5-2-6b.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-0-tiny.json",
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        "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0"
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-olmo-3-7b-think.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-27b.json",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-35b-a3b.json",
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        "title": "Kolibri-1 vs Qwen3.8-27B",
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        "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-flash-next.json",
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      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-2b-instruct.json",
        "title": "MiniMax-M2.5 vs Qwen3-VL-2B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-2b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct.json",
        "title": "MiniMax-M2.5 vs Qwen3-VL-30B-A3B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-30b-a3b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-32b-instruct.json",
        "title": "MiniMax-M2.5 vs Qwen3-VL-32B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-32b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct.json",
        "title": "MiniMax-M2.5 vs Qwen3-VL-4B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-4b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct.json",
        "title": "MiniMax-M2.5 vs Qwen3-VL-8B-Instruct",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwen3-vl-8b-instruct"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2.json",
        "title": "MiniMax-M2.5 vs Qwopus3.8-27B-Flash-V2",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-qwopus3-8-27b-flash-v2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b.json",
        "title": "MiniMax-M2.5 vs SmolLM3-3B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-smollm3-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b.json",
        "title": "MiniMax-M2.5 vs Spark-X2.6-4B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-spark-x2-6-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b.json",
        "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next.json",
        "title": "MiniMax-M2.5 vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-swift-1-5-qwen3-8-flash-next"
      },
      {
        "json": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.json",
        "title": "MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b"
      }
    ],
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "minimax-m2-5",
        "key": "reach",
        "kolibri-1": 60,
        "minimax-m2-5": 65,
        "name": "Reach",
        "weight": 25
      },
      {
        "by": 35,
        "edge": "kolibri-1",
        "key": "risk",
        "kolibri-1": 65,
        "minimax-m2-5": 30,
        "name": "Risk",
        "weight": 20
      },
      {
        "by": 15,
        "edge": "kolibri-1",
        "key": "interface",
        "kolibri-1": 90,
        "minimax-m2-5": 75,
        "name": "Agent interface",
        "weight": 10
      },
      {
        "by": 48,
        "edge": "kolibri-1",
        "key": "upkeep",
        "kolibri-1": 70,
        "minimax-m2-5": 22,
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Kolibri-1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 4 scored categories. MiniMax-M2.5 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.",
      "minimax-m2-5": "MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT."
    }
  },
  "kind": "anchor.page",
  "links": {
    "api": "https://www.anchorterminal.com/api/v1/index.json",
    "html": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5",
    "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.min.md"
  },
  "markdown": "Kolibri-1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 4 scored categories. MiniMax-M2.5 leads on reach. Both do open-weight models you run yourself.\n\n- Kolibri-1: grade C, 61/100, rank #38 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n- MiniMax-M2.5: grade C, 57.5/100, rank #71 of 102 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-5.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 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 30\n- Agent interface, 90 against 75\n- Documentation and upkeep, 70 against 22\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.3 against 1.8 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### MiniMax-M2.5 (C)\n\nGood for: Teams with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls and can live with its use policy\n\nAhead on:\n- Reach, 65 against 60\n\nWatch for: 230 GB in FP8, and about 155 GB for the smallest 4-bit library build, more than a 128 GB machine\n\n\n## Score by category\n\n| Category | Weight | Kolibri-1 | MiniMax-M2.5 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 60 | 65 | MiniMax-M2.5 +5 |\n| Risk | 20% (20 this run) | 65 | 30 | Kolibri-1 +35 |\n| Agent interface | 10% (10 this run) | 90 | 75 | Kolibri-1 +15 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 22 | Kolibri-1 +48 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **57.5 · C** | |\n\n## Facts side by side\n\n| Fact | Kolibri-1 | MiniMax-M2.5 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Aleph Alpha | MiniMax |\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 | MiniMax Model License (model) and modified MIT (code) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-19 | 2026-02-12 |\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) | 1.8/5 (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**MiniMax-M2.5.** MiniMax-M2.5 is a 230B mixture-of-experts model for coding and agent work, with parallel tool calls, a 196K context and parsers in vLLM and SGLang. It needs a 4-GPU server for FP8 or about 155 GB for a 4-bit build. The main risk is a licence replaced after release with a Prohibited Uses Policy while the card still says modified MIT.\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### MiniMax-M2.5\n\n1. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.5 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think\n2. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code\n3. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache\n4. On a single large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.5 lists a Q4_K_M GGUF of 138 GB and an MLX 4-bit of 129 GB (Mac only), and LM Studio states 121 GB minimum memory\n5. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends\n6. Budget output tokens for thinking on every turn, since the template always opens a think block\n7. Read LICENSE-MODEL, not the card's licence link, before shipping, and keep the required Notice text in every copy\n8. Without the hardware, call it through Novita or Featherless AI; MiniMax's own API retires M2.5 on 18 November 2026\n\n## Questions\n\n### Which is better for AI agents, Kolibri-1 or MiniMax-M2.5?\n\nKolibri-1 scores 61 (C) on agent readiness against MiniMax-M2.5's 57.5 (C), and leads in 3 of 4 scored categories. MiniMax-M2.5 leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kolibri-1\", \"b\": \"minimax-m2-5\"}`. From a terminal: `anchor compare kolibri-1 minimax-m2-5`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-5.json\n\n## Other comparisons with Kolibri-1 or MiniMax-M2.5\n\n- [Agents-A1-4B vs Kolibri-1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.md)\n- [Agents-A1-4B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.md)\n- [Apertus v1.5 8B vs Kolibri-1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.md)\n- [Apertus v1.5 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.md)\n- [Apodex-1.1-mini vs Kolibri-1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1.md)\n- [Apodex-1.1-mini vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-minimax-m2-5.md)\n- [BigBang-v1 vs 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    "title": "Kolibri-1 vs MiniMax-M2.5 for AI agents in 2026: scores and prices",
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