{
  "data": {
    "a": {
      "slug": "kolibri-1",
      "name": "Kolibri-1",
      "vendor": "Aleph Alpha",
      "vendorUrl": "https://aleph-alpha.com",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Kolibri-1 is Aleph Alpha's open-weight mixture-of-experts reasoning model for German and English, 78B parameters with 3.46B active per token, published on Hugging Face in FP8 and BF16 under Apache 2.0 and served with vLLM.",
      "url": "https://www.anchorterminal.com/tools/kolibri-1",
      "markdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/kolibri-1.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/kolibri-1.json",
      "dns": "kolibri-1.grade.anchorterminal.com",
      "repo": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "Aleph-Alpha/Kolibri-1"
        },
        {
          "registry": "huggingface",
          "name": "Aleph-Alpha/Kolibri-1-BF16"
        },
        {
          "registry": "pypi",
          "name": "aleph-alpha-inference"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-20).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0. Aleph Alpha sells no per-call access to these weights. Running them costs the hardware, about 78 GB of accelerator memory for the FP8 weights per the model card (checked 2026-10-20).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-20).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-10-20"
      },
      "docsUrl": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "reasoning",
        "tool-calling",
        "german",
        "apache-2.0",
        "safetensors",
        "fp8",
        "self-hosted",
        "new"
      ],
      "lastRelease": "2026-10-19",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 61,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 34,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 90,
          "reach": 65,
          "risk": 65,
          "upkeep": 70
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-03"
        },
        "negative": 0,
        "verdict": "Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers.",
        "bestFor": "German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.",
        "strengths": [
          "Apache 2.0 with no use policy, downloadable with no account",
          "Safetensors only, in Aleph Alpha's own organisation with a SHA-256 for every shard",
          "Tool calling documented end to end with the kolibri1 parser for vLLM and a working example",
          "Thinking can be set from none to high per request, with the reasoning returned separately",
          "262,144 tokens trained, with a full model card and a data card that gives the mix, filtering and cut-off"
        ],
        "weaknesses": [
          "The smallest publisher build is FP8 at 78.8 GB, so it needs about 95 GB and won't fit a 64 GB machine",
          "No LM Studio or SGLang support in a released version, and vLLM's kolibri1 parsers come only with Aleph Alpha's plugin pinned to vLLM 0.29",
          "No safety evaluation results and no security contact or disclosure policy found",
          "Aleph Alpha answered 2 of 14 discussions, and a report of a broken tool-call parser in its container image has no reply",
          "No hosted API or inference provider, so an agent without the hardware can't call it",
          "Structured output is claimed without saying how to request it"
        ],
        "agentNotes": [
          "Start it with pip install 'aleph-alpha-inference\u003e=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice",
          "Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags",
          "The plugin supports one vLLM minor version, 0.29. vLLM 0.33.0 loads the model without it, but has no kolibri1 tool or reasoning parser",
          "Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks",
          "Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128",
          "Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144",
          "With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking",
          "On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp or Ollama",
          "Validate tool results in your own code, as the card asks"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 4,
        "avgRating": 3.3,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 61
          }
        ],
        "editorialScores": {
          "interface": 90,
          "reach": 65,
          "risk": 65,
          "upkeep": 70
        },
        "provenanceScore": 70,
        "checked": {
          "categories": {
            "interface": "2026-11-03",
            "reach": "2026-11-03",
            "risk": "2026-11-03",
            "upkeep": "2026-11-03"
          },
          "last": "2026-11-03",
          "lastMoved": "2026-11-03",
          "oldest": "2026-11-03",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install 'aleph-alpha-inference\u003e=1'\nvllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 \\\n  --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice\n# or, with llama.cpp and bartowski's GGUF, a third party's conversion:\nllama-server -hf bartowski/Kolibri-1-GGUF:Q4_K_M --jinja",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H \"Content-Type: application/json\" \\\n  -d '{\n    \"model\": \"Aleph-Alpha/Kolibri-1\",\n    \"messages\": [{\"role\": \"user\", \"content\": \"What is the capital of France?\"}],\n    \"chat_template_kwargs\": {\"enable_thinking\": false}\n  }'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/kolibri-1"
      },
      "weights": {
        "trainedBy": "Aleph Alpha",
        "params": "78.1B",
        "active": "3.46B",
        "context": 262144,
        "goodFor": "German and English reasoning model with tool calling and adjustable reasoning effort, text only.",
        "runtimes": [
          "llama.cpp",
          "Ollama",
          "vLLM",
          "Transformers"
        ],
        "builds": [
          {
            "name": "bartowski Kolibri-1-GGUF (Q4_K_M)",
            "url": "https://huggingface.co/bartowski/Kolibri-1-GGUF",
            "who": "bartowski",
            "precision": "q4_k_m",
            "sizeGB": 49.76,
            "memoryGB": 64,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Aleph Alpha's. 49.76 GB of Q4_K_M weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "Kolibri-1",
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 78.8,
            "memoryGB": 96,
            "format": "safetensors"
          },
          {
            "name": "bartowski Kolibri-1-GGUF (Q8_0)",
            "url": "https://huggingface.co/bartowski/Kolibri-1-GGUF",
            "who": "bartowski",
            "precision": "q8_0",
            "sizeGB": 83.14,
            "memoryGB": 128,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "From bartowski, a third party's conversion and not Aleph Alpha's. 83.14 GB of Q8_0 weights, from the Hub's model information on 10 November 2026. Recorded after the last grading, so Reach doesn't count it"
          },
          {
            "name": "Kolibri-1-BF16",
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1-BF16",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 156.2,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)"
            ]
          }
        ],
        "run": "pip install 'aleph-alpha-inference\u003e=1' \u0026\u0026 vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice",
        "runBuild": "Kolibri-1",
        "checked": "2026-11-03",
        "minMemoryGB": 64
      },
      "family": {
        "lab": "Aleph Alpha",
        "family": "Kolibri",
        "url": "https://www.anchorterminal.com/open-weights/#aleph-alpha",
        "olderCount": 23,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Aleph Alpha GmbH",
        "domain": "aleph-alpha.com",
        "domainRegistered": "2018-08-11",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main",
        "securityTxt": "unknown",
        "checked": "2026-10-20",
        "notes": [
          "The model card names Aleph Alpha GmbH as the provider and Aleph Alpha Research GmbH as the developer. The imprint names Aleph Alpha GmbH, Heidelberg, commercial register Mannheim HRB 732804.",
          "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.",
          "The changelog is the repository's commit history, whose messages say what changed (Release 2026-10-02, a quantisation config fix on 13 October, transformers_version added on 19 October).",
          "aleph-alpha.com/.well-known/security.txt answered 403 to our reader on 20 October 2026, so it is counted as unchecked rather than absent."
        ],
        "score": 70
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/kolibri-1.json",
      "live": {
        "slug": "kolibri-1",
        "versions": [
          {
            "registry": "pypi",
            "name": "aleph-alpha-inference",
            "version": "1.0.0",
            "released": "2026-10-03",
            "seenAt": "2026-11-10T03:51:07.580354462Z"
          }
        ],
        "pypiWeekly": 115,
        "securityTxt": {
          "url": "https://aleph-alpha.com/.well-known/security.txt",
          "state": "unknown",
          "checkedAt": "2026-11-10T16:08:53.984135769Z"
        },
        "domain": {
          "domain": "aleph-alpha.com",
          "registered": "2018-08-11",
          "source": "https://rdap.verisign.com/com/v1/domain/aleph-alpha.com",
          "checkedAt": "2026-11-08T14:59:31.895475432Z"
        },
        "pages": [
          {
            "url": "https://huggingface.co/Aleph-Alpha/Kolibri-1/commits/main",
            "kind": "changelog",
            "status": 200,
            "checkedAt": "2026-11-10T16:22:08.257556045Z",
            "changedAt": "2026-11-10T16:22:08.257556045Z",
            "fingerprint": "847be72adc2c"
          }
        ],
        "updatedAt": "2026-11-10T16:22:08.257556045Z"
      }
    },
    "answer": "Kolibri-1 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on reach.",
    "b": {
      "slug": "step-3-7-flash",
      "name": "Step 3.7 Flash",
      "vendor": "StepFun",
      "vendorUrl": "https://platform.stepfun.ai",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Step 3.7 Flash is StepFun's open-weight vision-language model, 198B parameters with about 11B active, with tool calling and a 256K context. Apache 2.0. Its 4-bit GGUF is 105 GB, just over what a 128 GB machine holds.",
      "url": "https://www.anchorterminal.com/tools/step-3-7-flash",
      "markdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json",
      "dns": "step-3-7-flash.grade.anchorterminal.com",
      "repo": "https://huggingface.co/stepfun-ai/Step-3.7-Flash",
      "license": "Apache-2.0 (metadata and card; no LICENSE file)",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "stepfun-ai/Step-3.7-Flash"
        },
        {
          "registry": "huggingface",
          "name": "stepfun-ai/Step-3.7-Flash-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "stepfun-ai/Step-3.7-Flash-FP8"
        },
        {
          "registry": "huggingface",
          "name": "stepfun-ai/Step-3.7-Flash-NVFP4"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. None of the four repositories is gated (checked 2026-11-10).",
      "pricing": "free",
      "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 109 GB at IQ4_XS with its projector, 129 GB in NVFP4 or 403 GB in BF16. StepFun's API serves it at $0.20 per million input tokens and $1.15 per million output, per the card, and DeepInfra and Featherless serve it too (checked 2026-11-10).",
      "priceSummary": "Free",
      "where": "local",
      "x402": {
        "level": "no",
        "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).",
        "endpoints": []
      },
      "toolCount": null,
      "popularity": {
        "githubStars": null,
        "npmWeekly": null,
        "pypiWeekly": null,
        "asOf": "2026-11-10"
      },
      "docsUrl": "https://huggingface.co/stepfun-ai/Step-3.7-Flash",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "moe",
        "multimodal",
        "vision",
        "reasoning",
        "tool-calling",
        "agentic",
        "long-context",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "nvfp4",
        "custom-code",
        "self-hosted"
      ],
      "lastRelease": "2026-06-03",
      "graded": true,
      "anchor": {
        "graded": true,
        "score": 59.8,
        "grade": "C",
        "agentReady": false,
        "rank": 0,
        "ranked": false,
        "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools",
        "rankOf": 1518,
        "categoryRank": 73,
        "methodology": "0.7",
        "run": "2026-10-01",
        "scores": {
          "interface": 85,
          "reach": 70,
          "risk": 45,
          "upkeep": 39
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-11-10"
        },
        "negative": 0,
        "verdict": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.",
        "bestFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision",
        "strengths": [
          "Apache 2.0, ungated",
          "GGUF, FP8 and NVFP4 builds from StepFun",
          "Four released runtimes carry it, with Step parsers in vLLM and SGLang",
          "A 256K context with images in",
          "Hosted by StepFun and two providers"
        ],
        "weaknesses": [
          "About 131 GB to run at 4 bits with images, over a 128 GB machine's share",
          "Every serve command on the card runs the repository's code",
          "No reply from StepFun on the repository's discussions",
          "Not changed since 3 June 2026",
          "No limitations, safety results or training data on the card"
        ],
        "agentNotes": [
          "On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server",
          "On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`",
          "The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code",
          "For llama.cpp load the mmproj file from StepFun's GGUF repository for image input",
          "StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`",
          "The same model is on our StepFun API listing, with prices and terms"
        ],
        "metrics": {
          "kind": "local",
          "measured": false
        },
        "reviewCount": 0,
        "avgRating": 0,
        "history": [
          {
            "basis": "public evidence",
            "confidence": "low",
            "grade": "C",
            "methodology": "0.7",
            "pending": [
              "performance",
              "tasks"
            ],
            "run": "2026-10-01",
            "runLabel": "October 2026 research run",
            "score": 59.8
          }
        ],
        "editorialScores": {
          "interface": 85,
          "reach": 70,
          "risk": 45,
          "upkeep": 39
        },
        "provenanceScore": 58,
        "checked": {
          "categories": {
            "interface": "2026-11-10",
            "reach": "2026-11-10",
            "risk": "2026-11-10",
            "upkeep": "2026-11-10"
          },
          "last": "2026-11-10",
          "oldest": "2026-11-10",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "pip install \"sglang\u003e=0.5.23\"\nsglang serve --model-path stepfun-ai/Step-3.7-Flash-FP8 --tp 8 --ep 4 --reasoning-parser step3p5 --tool-call-parser step3p5 --enable-multimodal --trust-remote-code",
        "http": "# SGLang and vLLM serve an OpenAI-compatible API. StepFun's own API is at https://api.stepfun.ai/v1\n# (https://api.stepfun.com/v1 for keys issued in China)"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/step-3-7-flash"
      },
      "sameModel": [
        "stepfun-api"
      ],
      "weights": {
        "trainedBy": "StepFun",
        "params": "198B",
        "active": "11B",
        "context": 262144,
        "goodFor": "Agent work that mixes images, search and code, on a GPU server or through StepFun's API. It's a few GB too big for a 128 GB machine at 4 bits.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Step-3.7-Flash-GGUF IQ4_XS",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF",
            "who": "publisher",
            "precision": "iq4_xs",
            "sizeGB": 108.96,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "104.99 GB with the 3.97 GB F16 projector"
          },
          {
            "name": "Step-3.7-Flash-GGUF Q4_K_S",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF",
            "who": "publisher",
            "precision": "q4_k_s",
            "sizeGB": 115.47,
            "memoryGB": 192,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "111.50 GB with the 3.97 GB F16 projector"
          },
          {
            "name": "Step-3.7-Flash-NVFP4",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-NVFP4",
            "who": "publisher",
            "precision": "nvfp4",
            "sizeGB": 129.25,
            "memoryGB": 192,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "StepFun's 4-bit build for NVIDIA GPUs"
          },
          {
            "name": "Step-3.7-Flash-FP8",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 212.53,
            "memoryGB": 256,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ],
            "note": "StepFun's FP8 build"
          },
          {
            "name": "Step-3.7-Flash-GGUF Q8_0",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 213.39,
            "memoryGB": 512,
            "format": "gguf",
            "runsOn": [
              "llama.cpp"
            ],
            "note": "209.42 GB with the 3.97 GB F16 projector"
          },
          {
            "name": "Step-3.7-Flash (BF16 safetensors)",
            "url": "https://huggingface.co/stepfun-ai/Step-3.7-Flash",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 402.73,
            "memoryGB": 512,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang",
              "Transformers"
            ],
            "note": "26 files"
          }
        ],
        "run": "llama-server -hf stepfun-ai/Step-3.7-Flash-GGUF:IQ4_XS --jinja",
        "runBuild": "Step-3.7-Flash-GGUF IQ4_XS",
        "checked": "2026-11-10",
        "minMemoryGB": 192
      },
      "family": {
        "lab": "StepFun",
        "family": "Step 3",
        "url": "https://www.anchorterminal.com/open-weights/#stepfun",
        "others": [
          {
            "name": "Step-3.5-Flash",
            "url": "https://huggingface.co/stepfun-ai/Step-3.5-Flash"
          },
          {
            "name": "Step3-VL-10B",
            "url": "https://huggingface.co/stepfun-ai/Step3-VL-10B"
          }
        ]
      },
      "area": "models",
      "provenance": {
        "legalEntity": "STEPFUN TECHNOLOGY (HK) LIMITED",
        "domain": "stepfun.ai",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "https://huggingface.co/stepfun-ai/Step-3.7-Flash/commits/main",
        "securityTxt": "none",
        "checked": "2026-11-10",
        "notes": [
          "StepFun's legal entity, domain and security.txt are carried from our StepFun API listing.",
          "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The StepFun API that serves this model has its own listing.",
          "The changelog is the commit history, 23 commits from 23 May to 3 June 2026."
        ],
        "score": 58
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/step-3-7-flash.json"
    },
    "edges": [
      {
        "by": 31,
        "category": "upkeep",
        "leads": "kolibri-1",
        "name": "Documentation and upkeep",
        "reasons": {
          "a": "The model card earns 20 with all seven items. Training data earns 15 for a data card with the mix, the filtering and the cut-off, short of a dataset list with sizes. The last change earns 20, for template and card commits on 20 October 2026. Responsiveness earns 5, since Aleph Alpha replied in 2 of 14 discussions across both repositories and none since 13 October. Version policy earns 5 for a first release. File health earns 5, because the files agree but a report of a broken tool-call parser in the publisher's container image has waited since 7 October without a reply.",
          "b": "Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39."
        },
        "scores": {
          "a": 70,
          "b": 39
        }
      },
      {
        "by": 20,
        "category": "risk",
        "leads": "kolibri-1",
        "name": "Risk",
        "reasons": {
          "a": "File safety earns 25 for safetensors only, with no repository code run on load. Provenance earns 15, for the Aleph-Alpha organisation the launch post links and a SHA-256 for every weight shard. Safety evaluation earns 0, because the card says refusals were measured and neither the card nor the technical report publishes results. Agent guidance earns 10, since the card says what the trained refusals cover and which safeguards to add around the model, without prompt-injection guidance. Licence risk earns the full 15 under unchanged Apache 2.0. The security programme earns 0, as no contact, policy or advisories were found and security.txt answered 403 on 3 November, so it is scored as absent and left open.",
          "b": "File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45."
        },
        "scores": {
          "a": 65,
          "b": 45
        }
      },
      {
        "by": 5,
        "category": "reach",
        "leads": "step-3-7-flash",
        "name": "Reach",
        "reasons": {
          "a": "Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only the reference machine's band. There is no publisher 4-bit or GGUF build and no Ollama library entry to bring it lower. Runtimes earn 20, for released Transformers 5.20.0, released llama.cpp builds from b11709, Ollama 0.40.14, which builds llama.cpp b11963 with kolibri1, and vLLM, through Aleph Alpha's plugin for 0.29 and natively in 0.33.0 through its Transformers backend. LM Studio's latest runtime predates kolibri1 and SGLang 0.5.23 has no model file. Builds earn 10 for the one FP8 quantisation beside BF16. Terms earn 15 for Apache 2.0 with no use policy. Download earns 10 because neither repository is gated. Hosted earns 0, since no inference provider is listed on the Hub and Aleph Alpha names no API serving it.",
          "b": "Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70."
        },
        "scores": {
          "a": 65,
          "b": 70
        }
      },
      {
        "by": 5,
        "category": "interface",
        "leads": "kolibri-1",
        "name": "Agent interface",
        "reasons": {
          "a": "The chat template earns 20 as Jinja in tokenizer_config.json. The tool-calling format earns 25, because the card documents how tools are declared and how calls come back and names the kolibri1 parser for vLLM with a full example. Trained tool calling earns 20, from training on tool environments and a template built for several calls in one turn. Structured output earns 5, since the card claims it without saying how to ask. Reasoning control earns 10 for effort levels and an off switch per request, with the reasoning returned in a separate field. Context earns 10 for 262,144 tokens trained.",
          "b": "Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85."
        },
        "scores": {
          "a": 90,
          "b": 85
        }
      }
    ],
    "facts": [
      {
        "a": "Open-weight model",
        "b": "Open-weight model",
        "name": "Kind"
      },
      {
        "a": "Aleph Alpha",
        "b": "StepFun",
        "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 (metadata and card; 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": "2026-06-03",
        "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": "3.3/5 (4)",
        "b": "none",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Kolibri-1 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on reach.",
        "question": "Which is better for AI agents, Kolibri-1 or Step 3.7 Flash?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Risk, 65 against 45",
          "Agent interface, 90 against 85",
          "Documentation and upkeep, 70 against 39"
        ],
        "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": [
          "Reach, 70 against 65"
        ],
        "also": null,
        "goodFor": "Teams with a GPU server or a provider account that want an open, long-context agent model with vision",
        "slug": "step-3-7-flash",
        "watchFor": "About 131 GB to run at 4 bits with images, over a 128 GB machine's share"
      }
    ],
    "history": {
      "kolibri-1": [
        {
          "causes": [
            "product"
          ],
          "date": "2026-11-03",
          "delta": 0,
          "from": 61,
          "fromGrade": "C",
          "to": 61,
          "toGrade": "C"
        },
        {
          "causes": [
            "method"
          ],
          "date": "2026-10-21",
          "delta": -14.8,
          "from": 75.8,
          "fromGrade": "BB",
          "to": 61,
          "toGrade": "C"
        }
      ],
      "step-3-7-flash": null
    },
    "job": {
      "capability": "model.open-weights",
      "name": "Open-weight models you run yourself"
    },
    "others": [
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        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash.json",
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        "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash"
      },
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        "title": "Arete-27B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash"
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      {
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1"
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        "url": "https://www.anchorterminal.com/compare/bigbang-v1-vs-step-3-7-flash"
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        "title": "Carbon-X vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash.json",
        "title": "Carbon-X vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash"
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        "url": "https://www.anchorterminal.com/compare/cola2-vs-kolibri-1"
      },
      {
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        "title": "Cola2 vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash"
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-kolibri-1"
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      {
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        "url": "https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-step-3-7-flash"
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        "title": "SmolLM3-3B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-step-3-7-flash"
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      {
        "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash.json",
        "title": "Spark-X2.6-4B vs Step 3.7 Flash",
        "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-step-3-7-flash"
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      {
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        "title": "Step 3.7 Flash vs Swift 1.5 Qwen3.8-27B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-27b"
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        "title": "Step 3.7 Flash vs Swift 1.5 Qwen3.8-Flash-Next",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-swift-1-5-qwen3-8-flash-next"
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      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b.json",
        "title": "Step 3.7 Flash vs ThinkingCap (Qwen3.8 27B)",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-thinkingcap-qwen3-8-27b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2.json",
        "title": "Step 3.7 Flash vs Baidu Unlimited-OCR-2",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-unlimited-ocr-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b.json",
        "title": "Step 3.7 Flash vs WeVisOne-VL-2B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b.json",
        "title": "Step 3.7 Flash vs WeVisOne-VL-4B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-wevisone-vl-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b.json",
        "title": "Step 3.7 Flash vs ZAYA1-8B",
        "url": "https://www.anchorterminal.com/compare/step-3-7-flash-vs-zaya1-8b"
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    ],
    "panel": null,
    "scores": [
      {
        "key": "capability",
        "name": "Capability",
        "pending": true,
        "weight": 35
      },
      {
        "by": 5,
        "edge": "step-3-7-flash",
        "key": "reach",
        "kolibri-1": 65,
        "name": "Reach",
        "step-3-7-flash": 70,
        "weight": 25
      },
      {
        "by": 20,
        "edge": "kolibri-1",
        "key": "risk",
        "kolibri-1": 65,
        "name": "Risk",
        "step-3-7-flash": 45,
        "weight": 20
      },
      {
        "by": 5,
        "edge": "kolibri-1",
        "key": "interface",
        "kolibri-1": 90,
        "name": "Agent interface",
        "step-3-7-flash": 85,
        "weight": 10
      },
      {
        "by": 31,
        "edge": "kolibri-1",
        "key": "upkeep",
        "kolibri-1": 70,
        "name": "Documentation and upkeep",
        "step-3-7-flash": 39,
        "weight": 10
      }
    ],
    "summary": "Kolibri-1 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on reach. Both do open-weight models you run yourself.",
    "tasks": null,
    "tutorials": [],
    "verdicts": {
      "kolibri-1": "Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers.",
      "step-3-7-flash": "A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description."
    }
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  "markdown": "Kolibri-1 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on reach. Both do open-weight models you run yourself.\n\n- Kolibri-1: grade C, 61/100, rank #44 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n- Step 3.7 Flash: grade C, 59.8/100, rank #93 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/step-3-7-flash.md · JSON https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Kolibri-1 (C)\n\nGood for: German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.\n\nAhead on:\n- Risk, 65 against 45\n- Agent interface, 90 against 85\n- Documentation and upkeep, 70 against 39\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### Step 3.7 Flash (C)\n\nGood for: Teams with a GPU server or a provider account that want an open, long-context agent model with vision\n\nAhead on:\n- Reach, 70 against 65\n\nWatch for: About 131 GB to run at 4 bits with images, over a 128 GB machine's share\n\n\n## Why the edge\n\n### Documentation and upkeep: Kolibri-1 +31\n\n- Kolibri-1 (70): The model card earns 20 with all seven items. Training data earns 15 for a data card with the mix, the filtering and the cut-off, short of a dataset list with sizes. The last change earns 20, for template and card commits on 20 October 2026. Responsiveness earns 5, since Aleph Alpha replied in 2 of 14 discussions across both repositories and none since 13 October. Version policy earns 5 for a first release. File health earns 5, because the files agree but a report of a broken tool-call parser in the publisher's container image has waited since 7 October without a reply.\n- Step 3.7 Flash (39): Model card 14, Intended use, the architecture, the parameters, the context and the language (in the metadata) are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 3 June 2026, fixed the modelling code, 160 days before this check and within 180. Responsiveness 0, none of the 11 most recent discussions that aren't pull requests (29 May to 24 June 2026) has a reply from a stepfun-ai member, counted on 10 November by author flags and dates with a script, text not read. Version policy 5, Step-3.5-Flash stays in its own repositories and 3.7 came out in new ones, with no dated note on the older cards that we read. File health 10, Config, template and card agree on the architecture and the context, and June commits fixed the modelling code. 14 + 0 + 10 + 0 + 5 + 10 = 39.\n\n### Risk: Kolibri-1 +20\n\n- Kolibri-1 (65): File safety earns 25 for safetensors only, with no repository code run on load. Provenance earns 15, for the Aleph-Alpha organisation the launch post links and a SHA-256 for every weight shard. Safety evaluation earns 0, because the card says refusals were measured and neither the card nor the technical report publishes results. Agent guidance earns 10, since the card says what the trained refusals cover and which safeguards to add around the model, without prompt-injection guidance. Licence risk earns the full 15 under unchanged Apache 2.0. The security programme earns 0, as no contact, policy or advisories were found and security.txt answered 403 on 3 November, so it is scored as absent and left open.\n- Step 3.7 Flash (45): File safety 15, safetensors and GGUF only, no .bin, .pt or .pth files (25). Provenance 15, the files sit in the stepfun-ai organisation, which the card's StepFun platform and model page belong to (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations section and gives no guidance on prompt injection, instruction priority, refusals or safeguards. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 0, no way to report a flaw on the card, and our StepFun API listing found no security.txt, bug bounty or disclosure policy. 15 + 15 + 0 + 0 + 15 + 0 = 45.\n\n### Reach: Step 3.7 Flash +5\n\n- Kolibri-1 (65): Memory earns 10 because the smallest qualifying build is Aleph Alpha's own FP8 one at 78.8 GB, which comes to 94.6 GB with the fifth added and so fits only the reference machine's band. There is no publisher 4-bit or GGUF build and no Ollama library entry to bring it lower. Runtimes earn 20, for released Transformers 5.20.0, released llama.cpp builds from b11709, Ollama 0.40.14, which builds llama.cpp b11963 with kolibri1, and vLLM, through Aleph Alpha's plugin for 0.29 and natively in 0.33.0 through its Transformers backend. LM Studio's latest runtime predates kolibri1 and SGLang 0.5.23 has no model file. Builds earn 10 for the one FP8 quantisation beside BF16. Terms earn 15 for Apache 2.0 with no use policy. Download earns 10 because neither repository is gated. Hosted earns 0, since no inference provider is listed on the Hub and Aleph Alpha names no API serving it.\n- Step 3.7 Flash (70): Memory 5, the smallest build at 4 bits is StepFun's IQ4_XS GGUF, 104.99 GB with the 3.97 GB projector, and 108.96 GB × 1.2 = 130.8 GB, over what the reference machine can give a model and within 256 GB. The IQ3 and Q3 builds are below 4 bits and aren't read. Runtimes 20, Released vLLM 0.33.0, SGLang 0.5.23 and Transformers 5.22.2 have the step3p7 model, and llama.cpp b12242 has the architecture, with StepFun's own GGUF. Four at 5 each, and the card documents all four. Builds 15, StepFun publishes GGUF at five quantised precisions, an FP8 build and an NVFP4 build. Terms 15, Apache 2.0 in the card's metadata and its licence section, an OSI licence with nothing added. Download 10, the Hub API reports gated false for all four repositories on 10 November 2026, so the files download with no account and nothing to accept. Hosted 5, StepFun's own API serves it, per the card and our StepFun API listing, and Featherless and DeepInfra are live for it on Hugging Face. 5 + 20 + 15 + 15 + 10 + 5 = 70.\n\n### Agent interface: Kolibri-1 +5\n\n- Kolibri-1 (90): The chat template earns 20 as Jinja in tokenizer_config.json. The tool-calling format earns 25, because the card documents how tools are declared and how calls come back and names the kolibri1 parser for vLLM with a full example. Trained tool calling earns 20, from training on tool environments and a template built for several calls in one turn. Structured output earns 5, since the card claims it without saying how to ask. Reasoning control earns 10 for effort levels and an off switch per request, with the reasoning returned in a separate field. Context earns 10 for 262,144 tokens trained.\n- Step 3.7 Flash (85): Chat template 20, chat_template.jinja in the repository (5,723 characters). Tool-calling format 25, the card names the parser to use in two runtimes, step3p5 in vLLM (with auto tool choice) and in SGLang, and both ship Step parsers in a release. Trained tool calling 20, the card presents tool orchestration as a main use, with multi-turn results, and the template renders every call in a turn. Structured output 0, Nothing on the card about JSON or schema-constrained output for the weights. Reasoning control 10, the template has a reasoning_effort setting and keeps reasoning_content, and the card names the step3p5 reasoning parser for vLLM and SGLang, which returns the reasoning apart from the answer. Context 10, 256K tokens per the card, and 262,144 positions in config.json. 20 + 25 + 20 + 0 + 10 + 10 = 85.\n\n## Score by category\n\n| Category | Weight | Kolibri-1 | Step 3.7 Flash | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 65 | 70 | Step 3.7 Flash +5 |\n| Risk | 20% (20 this run) | 65 | 45 | Kolibri-1 +20 |\n| Agent interface | 10% (10 this run) | 90 | 85 | Kolibri-1 +5 |\n| Documentation and upkeep | 10% (10 this run) | 70 | 39 | Kolibri-1 +31 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **59.8 · C** | |\n\n## Facts side by side\n\n| Fact | Kolibri-1 | Step 3.7 Flash |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Aleph Alpha | StepFun |\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 (metadata and card; no LICENSE file) |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-19 | 2026-06-03 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3.3/5 (4) | none |\n\n## How the scores have moved\n\n### Kolibri-1\n\n- no change on 3 November 2026: product\n- -14.8 on 21 October 2026 (BB to C): checklist\n\n### Step 3.7 Flash\n\nNo score change since it was listed.\n\n## Verdicts\n\n**Kolibri-1.** Kolibri-1 is a German and English reasoning model with a documented tool-call format, switchable thinking and a 262,144-token context, under Apache 2.0. Aleph Alpha's FP8 build needs about 95 GB and runs on vLLM and Transformers 5.20; llama.cpp and Ollama 0.40.14 need a GGUF. The main risk is no published safety results or security contact, and few repository answers.\n\n**Step 3.7 Flash.** A capable open agent model with a clean licence, every kind of build and four released runtimes. Two things hold it back: it's a little too big for a 128 GB machine at 4 bits, whatever the card says, and StepFun has gone quiet on the repository since June. No safety results, limitations or data description.\n\n## Before you call either\n\n### Kolibri-1\n\n1. Start it with pip install 'aleph-alpha-inference\u003e=1' then vllm serve Aleph-Alpha/Kolibri-1 --kv-cache-dtype fp8 --reasoning-parser kolibri1 --tool-call-parser kolibri1 --enable-auto-tool-choice\n2. Without the kolibri1 tool-call parser and enable-auto-tool-choice, tool calls come back as text in tool_call tags\n3. The plugin supports one vLLM minor version, 0.29. vLLM 0.33.0 loads the model without it, but has no kolibri1 tool or reasoning parser\n4. Thinking is on by default at high effort. Pass reasoning_effort none or enable_thinking false in chat_template_kwargs for short tasks\n5. Recommended sampling is temperature 1.0, top_p 0.97 and top_k 128\n6. Past 262,144 tokens, add --max-model-len 1048576 and the max_position_embeddings override in --hf-overrides, though Aleph Alpha recommends staying at or below 262,144\n7. With Transformers 5.20 or later, tokenizer.parse_response returns the answer in content and the reasoning in thinking\n8. On the reference machine the FP8 build fits by size, but no document confirms vLLM runs FP8 on gfx1151, and Aleph Alpha ships no GGUF for llama.cpp or Ollama\n9. Validate tool results in your own code, as the card asks\n\n### Step 3.7 Flash\n\n1. On a 128 GB machine the 4-bit GGUF won't fit with a working context. Use StepFun's API or a provider, or a server\n2. On vLLM or SGLang pass `--tool-call-parser step3p5 --reasoning-parser step3p5`\n3. The card's commands pass trust-remote-code. Released Transformers 5.22.2 has the model built in if you'd rather not run the repository's code\n4. For llama.cpp load the mmproj file from StepFun's GGUF repository for image input\n5. StepFun's API has two hosts. Use the one where your key was issued, `api.stepfun.ai` or `api.stepfun.com`\n6. The same model is on our StepFun API listing, with prices and terms\n\n## Questions\n\n### Which is better for AI agents, Kolibri-1 or Step 3.7 Flash?\n\nKolibri-1 scores 61 (C) on agent readiness against Step 3.7 Flash's 59.8 (C), and leads in 3 of 4 scored categories. Step 3.7 Flash leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash.json, and with the fewest tokens: https://www.anchorterminal.com/compare/kolibri-1-vs-step-3-7-flash.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"kolibri-1\", \"b\": \"step-3-7-flash\"}`. From a terminal: `anchor compare kolibri-1 step-3-7-flash`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/kolibri-1.json and https://www.anchorterminal.com/api/v1/tools/step-3-7-flash.json\n\n## Other comparisons with Kolibri-1 or Step 3.7 Flash\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 Step 3.7 Flash](https://www.anchorterminal.com/compare/agents-a1-4b-vs-step-3-7-flash.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 Step 3.7 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-step-3-7-flash.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 Step 3.7 Flash](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-step-3-7-flash.md)\n- [Arete-27B vs Kolibri-1](https://www.anchorterminal.com/compare/arete-27b-vs-kolibri-1.md)\n- [Arete-27B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/arete-27b-vs-step-3-7-flash.md)\n- [BigBang-v1 vs Kolibri-1](https://www.anchorterminal.com/compare/bigbang-v1-vs-kolibri-1.md)\n- [BigBang-v1 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/bigbang-v1-vs-step-3-7-flash.md)\n- [Carbon-X vs Kolibri-1](https://www.anchorterminal.com/compare/carbon-x-vs-kolibri-1.md)\n- [Carbon-X vs Step 3.7 Flash](https://www.anchorterminal.com/compare/carbon-x-vs-step-3-7-flash.md)\n- [Cola2 vs Kolibri-1](https://www.anchorterminal.com/compare/cola2-vs-kolibri-1.md)\n- [Cola2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/cola2-vs-step-3-7-flash.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 Step 3.7 Flash](https://www.anchorterminal.com/compare/cosmos-reason2-2b-vs-step-3-7-flash.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 Step 3.7 Flash](https://www.anchorterminal.com/compare/darwin-36b-opus-vs-step-3-7-flash.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 Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v3-2-vs-step-3-7-flash.md)\n- [DeepSeek V4.1 Flash vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-kolibri-1.md)\n- [DeepSeek V4.1 Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v4-1-flash-vs-step-3-7-flash.md)\n- [DeepSeek-V4-Flash-0731 vs Kolibri-1](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-kolibri-1.md)\n- [DeepSeek-V4-Flash-0731 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/deepseek-v4-flash-0731-vs-step-3-7-flash.md)\n- [Devstral Small 2 vs Kolibri-1](https://www.anchorterminal.com/compare/devstral-small-2-vs-kolibri-1.md)\n- [Devstral Small 2 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash.md)\n- [DiffusionGemma 26B A4B IT vs Kolibri-1](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-kolibri-1.md)\n- [DiffusionGemma 26B A4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/diffusiongemma-26b-a4b-it-vs-step-3-7-flash.md)\n- [EXAONE 4.5 33B vs Kolibri-1](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-kolibri-1.md)\n- [EXAONE 4.5 33B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/exaone-4-5-33b-vs-step-3-7-flash.md)\n- [Gemma 4 12B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-kolibri-1.md)\n- [Gemma 4 12B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-step-3-7-flash.md)\n- [Gemma 4 26B A4B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-kolibri-1.md)\n- [Gemma 4 26B A4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-step-3-7-flash.md)\n- [Gemma 4 31B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-kolibri-1.md)\n- [Gemma 4 31B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-step-3-7-flash.md)\n- [Gemma 4 E2B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-kolibri-1.md)\n- [Gemma 4 E2B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-step-3-7-flash.md)\n- [Gemma 4 E4B IT vs Kolibri-1](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-kolibri-1.md)\n- [Gemma 4 E4B IT vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-step-3-7-flash.md)\n- [GLM-4.5-Air vs Kolibri-1](https://www.anchorterminal.com/compare/glm-4-5-air-vs-kolibri-1.md)\n- [GLM-4.5-Air vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-4-5-air-vs-step-3-7-flash.md)\n- [GLM-4.7-Flash vs Kolibri-1](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-kolibri-1.md)\n- [GLM-4.7-Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-step-3-7-flash.md)\n- [GLM-5.3-Flash vs Kolibri-1](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-kolibri-1.md)\n- [GLM-5.3-Flash vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-step-3-7-flash.md)\n- [GLM-5.3 vs Kolibri-1](https://www.anchorterminal.com/compare/glm-5-3-vs-kolibri-1.md)\n- [GLM-5.3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/glm-5-3-vs-step-3-7-flash.md)\n- [gpt-oss-120b vs Kolibri-1](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-kolibri-1.md)\n- [gpt-oss-120b vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-step-3-7-flash.md)\n- [gpt-oss-20b vs Kolibri-1](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-kolibri-1.md)\n- [gpt-oss-20b vs Step 3.7 Flash](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-step-3-7-flash.md)\n- [Granite 4.2 30B vs Kolibri-1](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-kolibri-1.md)\n- [Granite 4.2 30B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-30b-vs-step-3-7-flash.md)\n- [Granite 4.2 3B vs Kolibri-1](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-kolibri-1.md)\n- [Granite 4.2 3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-step-3-7-flash.md)\n- [Granite 4.2 8B vs Kolibri-1](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-kolibri-1.md)\n- [Granite 4.2 8B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/granite-4-2-8b-vs-step-3-7-flash.md)\n- [Hy-Memory-30B-A3B vs Kolibri-1](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-kolibri-1.md)\n- [Hy-Memory-30B-A3B vs Step 3.7 Flash](https://www.anchorterminal.com/compare/hy-memory-30b-a3b-vs-step-3-7-flash.md)\n- [Hy3 vs Kolibri-1](https://www.anchorterminal.com/compare/hy3-vs-kolibri-1.md)\n- [Hy3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/hy3-vs-step-3-7-flash.md)\n- [Inkling-Small vs Kolibri-1](https://www.anchorterminal.com/compare/inkling-small-vs-kolibri-1.md)\n- [Inkling-Small vs Step 3.7 Flash](https://www.anchorterminal.com/compare/inkling-small-vs-step-3-7-flash.md)\n- [Inkling vs Kolibri-1](https://www.anchorterminal.com/compare/inkling-vs-kolibri-1.md)\n- [Inkling vs Step 3.7 Flash](https://www.anchorterminal.com/compare/inkling-vs-step-3-7-flash.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Kolibri-1](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-kolibri-1.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs Step 3.7 Flash](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-step-3-7-flash.md)\n- [Kimi K3.1 Pro vs Kolibri-1](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-kolibri-1.md)\n- [Kimi K3.1 Pro vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kimi-k3-1-pro-vs-step-3-7-flash.md)\n- [Kimi K3 vs Kolibri-1](https://www.anchorterminal.com/compare/kimi-k3-vs-kolibri-1.md)\n- [Kimi K3 vs Step 3.7 Flash](https://www.anchorterminal.com/compare/kimi-k3-vs-step-3-7-flash.md)\n- [Kolibri-1 vs Kronos](https://www.anchorterminal.com/compare/kolibri-1-vs-kronos.md)\n- [Kolibri-1 vs Laguna M.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-m-1.md)\n- [Kolibri-1 vs Laguna S 2.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-s-2-1.md)\n- [Kolibri-1 vs Laguna XS 2.1](https://www.anchorterminal.com/compare/kolibri-1-vs-laguna-xs-2-1.md)\n- [Kolibri-1 vs LBS-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-lbs-27b.md)\n- [Kolibri-1 vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm2-5-2-6b.md)\n- [Kolibri-1 vs LFM3-1.7B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-1-7b.md)\n- [Kolibri-1 vs LFM3-Audio-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-audio-2b.md)\n- [Kolibri-1 vs LFM3-Omni-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-omni-2b.md)\n- [Kolibri-1 vs LFM3-VL-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-lfm3-vl-2b.md)\n- [Kolibri-1 vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-0-tiny.md)\n- [Kolibri-1 vs Ling-3.1-flash-1019](https://www.anchorterminal.com/compare/kolibri-1-vs-ling-3-1-flash-1019.md)\n- [Kolibri-1 vs LongCat-2.5](https://www.anchorterminal.com/compare/kolibri-1-vs-longcat-2-5.md)\n- [Kolibri-1 vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Kolibri-1 vs MiMo-V2.6-Flash-RL](https://www.anchorterminal.com/compare/kolibri-1-vs-mimo-v2-6-flash-rl.md)\n- [Kolibri-1 vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-o-4-5.md)\n- [Kolibri-1 vs MiniCPM-V 4.7 1B](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-1b.md)\n- [Kolibri-1 vs MiniCPM-V 4.7 35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm-v-4-7-35b-a3b.md)\n- [Kolibri-1 vs MiniCPM5-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-minicpm5-2b.md)\n- [Kolibri-1 vs MiniMax-M2.5](https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-5.md)\n- [Kolibri-1 vs MiniMax-M2.7](https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m2-7.md)\n- [Kolibri-1 vs MiniMax-M3.1-Flash](https://www.anchorterminal.com/compare/kolibri-1-vs-minimax-m3-1-flash.md)\n- [Kolibri-1 vs Mistral Large 4](https://www.anchorterminal.com/compare/kolibri-1-vs-mistral-large-4.md)\n- [Kolibri-1 vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/kolibri-1-vs-muse-glimmer-30b.md)\n- [Kolibri-1 vs Nanbeige4.2-3B](https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-2-3b.md)\n- [Kolibri-1 vs Nanbeige4.3-33B](https://www.anchorterminal.com/compare/kolibri-1-vs-nanbeige-4-3-33b.md)\n- [Kolibri-1 vs NVIDIA Nemotron 3.5 Bolt E4B 4GB](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-bolt-e4b-4gb.md)\n- [Kolibri-1 vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Kolibri-1 vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-30b-a3b.md)\n- [Kolibri-1 vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-4b.md)\n- [Kolibri-1 vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Kolibri-1 vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-super-120b-a12b.md)\n- [Kolibri-1 vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/kolibri-1-vs-nemotron-3-ultra-550b-a55b.md)\n- [Kolibri-1 vs North Mini Code 1.0](https://www.anchorterminal.com/compare/kolibri-1-vs-north-mini-code-1-0.md)\n- [Kolibri-1 vs occamy-1.0](https://www.anchorterminal.com/compare/kolibri-1-vs-occamy-1-0.md)\n- [Kolibri-1 vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/kolibri-1-vs-olmo-3-7b-think.md)\n- [Kolibri-1 vs Ornith-1.5-122B](https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-122b.md)\n- [Kolibri-1 vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-35b-a3b.md)\n- [Kolibri-1 vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-397b.md)\n- [Kolibri-1 vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/kolibri-1-vs-ornith-1-5-9b.md)\n- [Kolibri-1 vs OTel-2.0-LLM-31B-IT](https://www.anchorterminal.com/compare/kolibri-1-vs-otel-2-0-llm-31b-it.md)\n- [Kolibri-1 vs Qwen-AgentWorld-35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen-agentworld-35b-a3b.md)\n- [Kolibri-1 vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Kolibri-1 vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-4b-instruct-2507.md)\n- [Kolibri-1 vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-0-8b.md)\n- [Kolibri-1 vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-122b-a10b.md)\n- [Kolibri-1 vs Qwen3.5-2B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-2b.md)\n- [Kolibri-1 vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-397b-a17b.md)\n- [Kolibri-1 vs Qwen3.5-4B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-4b.md)\n- [Kolibri-1 vs Qwen3.5-9B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-5-9b.md)\n- [Kolibri-1 vs Qwen3.6-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-27b.md)\n- [Kolibri-1 vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-6-35b-a3b.md)\n- [Kolibri-1 vs Qwen3.8-27B](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-27b.md)\n- [Kolibri-1 vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-8-flash-next.md)\n- [Kolibri-1 vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Kolibri-1 vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-coder-next.md)\n- [Kolibri-1 vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Kolibri-1 vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/kolibri-1-vs-qwen3-vl-2b-instruct.md)\n- [Kolibri-1 vs 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