{ "data": { "a": { "slug": "inkling-2-small", "name": "Inkling 2 Small", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai", "kind": "weights", "category": "open-weights", "summary": "Inkling 2 Small is Thinking Machines Lab's open-weight mixture-of-experts model, 594B total and 17B active parameters, taking text and images and returning text, with adjustable thinking effort, published in NVFP4 and MXFP8 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/inkling-2-small", "markdownUrl": "https://www.anchorterminal.com/tools/inkling-2-small.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling-2-small.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json", "dns": "inkling-2-small.grade.anchorterminal.com", "repo": "https://huggingface.co/thinkingmachines/Inkling-2-Small", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "thinkingmachines/Inkling-2-Small" }, { "registry": "huggingface", "name": "thinkingmachines/Inkling-2-Small-MXFP8" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-11-12). Thinking Machines' Model Acceptable Use Policy, linked from the card, says that downloading or using the weights binds you to it.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 with Thinking Machines' Model Acceptable Use Policy on top. Running it yourself costs the hardware, 393.5 GB of files for the NVFP4 checkpoint and 622.8 GB for MXFP8, which Thinking Machines puts at 500 GB and 800 GB of GPU memory. Thinking Machines' Tinker serves it as a beta serverless inference API at $0.30 per million input tokens, $0.03 cached and $1.00 output (read 2026-11-06).", "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 2026-11-12).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-12" }, "docsUrl": "https://thinkingmachines.ai/model-card/inkling-2-small/", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "vision", "reasoning", "tool-calling", "apache-2.0", "safetensors", "nvfp4", "self-hosted", "new" ], "lastRelease": "2026-11-12", "graded": true, "anchor": { "graded": true, "score": 58.8, "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": 1524, "categoryRank": 78, "methodology": "0.8", "run": "2026-10-01", "scores": { "interface": 85, "reach": 40, "risk": 65, "upkeep": 67 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-12" }, "negative": 0, "verdict": "Thinking Machines' newest open model, a 594B mixture-of-experts generalist with images in, several tool calls a turn and per-request thinking effort. The NVFP4 checkpoint needs about 472 GB, and no released runtime runs it yet, so today that means vLLM's or SGLang's own images, or Tinker and Together hosted. The use policy binds on download and can change.", "bestFor": "Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image.", "strengths": [ "Safetensors only, with a checksum on every LFS file in the publisher's own organisation", "Thinking effort set per request from none to xhigh, several tool calls a turn and a 1M-token context", "Two checkpoints from Thinking Machines, NVFP4 and MXFP8, under Apache 2.0 with no gate", "Thinking Machines' tml-renderers package renders prompts and parses tool calls for this model", "A training-content summary with sources, mix and an October 2026 collection cut-off", "Thinking Machines' own Tinker hosts it as beta serverless inference, and the card names seven more API providers" ], "weaknesses": [ "The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory", "No released version of vLLM, SGLang, Transformers, llama.cpp or Ollama runs it on 12 November 2026", "Apache 2.0 comes with an acceptable use policy that binds on download and can change at any time", "Safety results are Thinking Machines' own, with no harness to rerun them and no prompt-injection guidance", "Structured output isn't documented, and the card gives no dated knowledge cut-off", "No independent benchmark results were read, so Capability is pending and the total is capped at 61" ], "agentNotes": [ "No released runtime runs it yet. Serve the NVFP4 checkpoint from SGLang's dev-inkling-2-small image with the cookbook's recipe for your hardware, or from vLLM's vllm/vllm-openai:inkling-2-small image", "In vLLM pass `--reasoning-parser inkling --enable-auto-tool-choice --tool-call-parser inkling`. In SGLang the tool-call parser is `inkling2` and the reasoning parser `inkling`", "Transformers' main branch loads only thinkingmachines/Inkling-2-Small-MXFP8, the 622.8 GB checkpoint, and no release has the code yet", "Pass `reasoning_effort` as none, minimal, low, medium, high (the default), xhigh or max, or a number from 0 to 0.99", "Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens", "Tool call arguments must reach the template as an object, not a JSON string, or it raises an error", "Images should be 40 to 4096 px a side. The model takes no audio, and the template raises an error for an audio part", "Use guided decoding or validate JSON yourself, since structured output isn't documented", "Without the hardware, call it on Tinker's OpenAI-compatible endpoint with a Tinker key, at $0.30 input and $1.00 output per million tokens as read on 6 November 2026", "Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.8", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 58.8 } ], "editorialScores": { "interface": 85, "reach": 40, "risk": 65, "upkeep": 67 }, "provenanceScore": 72, "checked": { "categories": { "interface": "2026-11-12", "reach": "2026-11-12", "risk": "2026-11-12", "upkeep": "2026-11-12" }, "last": "2026-11-12", "lastMoved": "2026-11-12", "oldest": "2026-11-12", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# per SGLang's cookbook, the NVFP4 checkpoint on 4 B300 GPUs. Not in an SGLang release yet,\n# so run it inside the lmsysorg/sglang:dev-inkling-2-small image:\nSGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096 sglang serve \\\n --model-path thinkingmachines/Inkling-2-Small --tp 4 --ep 4 \\\n --moe-a2a-backend megamoe --attention-backend fa4 --bf16-gemm-backend torch --dtype bfloat16 \\\n --page-size 128 --grammar-backend xgrammar --enable-multimodal \\\n --reasoning-parser inkling --tool-call-parser inkling2", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"thinkingmachines/Inkling-2-Small\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"low\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/inkling-2-small" }, "sameCompany": [ "inkling", "inkling-small" ], "sameModel": [ "tinker" ], "weights": { "trainedBy": "Thinking Machines Lab", "params": "594B", "active": "17B", "context": 1048576, "goodFor": "Mixture-of-experts generalist for coding, agents and tool use with images in, a 1M-token context and thinking effort set per request.", "runtimes": [], "builds": [ { "name": "Inkling-2-Small", "url": "https://huggingface.co/thinkingmachines/Inkling-2-Small", "who": "publisher", "precision": "nvfp4", "sizeGB": 393.5, "memoryGB": 512, "format": "safetensors", "note": "The main checkpoint, in the precision the model was trained in. Routed experts in NVFP4 for layers 0 to 39 and MXFP8 for layers 40 to 47, per SGLang's cookbook. Thinking Machines states at least 500 GB of GPU memory" }, { "name": "Inkling-2-Small-MXFP8", "url": "https://huggingface.co/thinkingmachines/Inkling-2-Small-MXFP8", "who": "publisher", "precision": "mxfp8", "sizeGB": 622.8, "memoryGB": 1024, "format": "safetensors", "note": "Every routed expert in MXFP8. Thinking Machines states at least 800 GB of GPU memory and recommends the NVFP4 checkpoint. It's the only checkpoint Transformers' main branch loads" } ], "run": "SGLANG_OPT_DEEPGEMM_MEGA_MOE_NUM_MAX_TOKENS_PER_RANK=4096 sglang serve --model-path thinkingmachines/Inkling-2-Small --tp 4 --ep 4 --moe-a2a-backend megamoe --attention-backend fa4 --bf16-gemm-backend torch --dtype bfloat16 --page-size 128 --grammar-backend xgrammar --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling2", "runBuild": "Inkling-2-Small", "checked": "2026-11-12", "minMemoryGB": 512 }, "family": { "lab": "Thinking Machines Lab", "family": "Inkling 2", "url": "https://www.anchorterminal.com/open-weights/#thinking-machines" }, "area": "models", "provenance": { "legalEntity": "Thinking Machines Lab, Inc. (named as provider on its model card page and in its training-content summary)", "domain": "thinkingmachines.ai", "domainRegistered": "", "domainNote": "Not looked up: RDAP isn't requested under the 1 November 2026 rules.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/thinkingmachines/Inkling-2-Small/commits/main", "securityTxt": "valid", "checked": "2026-11-12", "notes": [ "Thinking Machines' model card page links both repositories in the thinkingmachines organisation on Hugging Face, which isn't verified by Hugging Face.", "Nothing is hosted for this listing, so the Apache 2.0 licence and the Model Acceptable Use Policy stand in for terms of service and no privacy policy is scored.", "The changelog is the repository's commit history, 3 commits on 12 November 2026, the upload, a library tag from a merged pull request and a card update.", "thinkingmachines.ai/.well-known/security.txt gives a security contact, a PGP key and an expiry in 2029, read on 12 November 2026, and the model card page links a coordinated vulnerability disclosure policy.", "Thinking Machines' terms bar scraping the Services. The founder decided on 6 November 2026 that reading at a person's pace is fine, so six pages were read one at a time on 12 November: the acceptable use policy, the training data page and its summary for this model, the model card page, and the tml-renderers and thinking effort guides. thinkingmachines.ai's robots.txt has no rules, and tinker-docs.thinkingmachines.ai answers 404 for robots.txt." ], "score": 72 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling-2-small.json", "live": { "slug": "inkling-2-small", "probe": { "target": "https://tinker.thinkingmachines.dev/services/tinker-prod/oai/api/v1", "method": "get", "lastAt": "2026-11-13T20:11:37.236512439Z", "lastOk": true, "lastStatus": 404, "lastMs": 439, "authRequired": false, "uptime24h": 100, "uptime30d": 100, "p50ms24h": 238, "p95ms24h": 467, "samples24h": 245, "samples30d": 1854, "days": [ { "date": "2026-11-06", "probes": 186, "ok": 186 }, { "date": "2026-11-07", "probes": 243, "ok": 243 }, { "date": "2026-11-08", "probes": 243, "ok": 243 }, { "date": "2026-11-09", "probes": 243, "ok": 243 }, { "date": "2026-11-10", "probes": 244, "ok": 244 }, { "date": "2026-11-11", "probes": 244, "ok": 244 }, { "date": "2026-11-12", "probes": 245, "ok": 245 }, { "date": "2026-11-13", "probes": 206, "ok": 206 } ], "since": "2026-11-06T00:00:00Z" }, "securityTxt": { "url": "https://thinkingmachines.ai/.well-known/security.txt", "state": "valid", "expires": "2029-07-13T07:00:00.000Z", "checkedAt": "2026-11-14T16:19:22.779006645Z" }, "llmsTxt": { "url": "https://tinker-docs.thinkingmachines.ai/llms.txt", "ok": true, "status": 200, "checkedAt": "2026-11-13T15:54:36.79283015Z" }, "domain": { "domain": "thinkingmachines.ai", "registered": "2024-07-09", "source": "https://rdap.identitydigital.services/rdap/domain/thinkingmachines.ai", "checkedAt": "2026-11-08T15:05:10.558189316Z" }, "pages": [ { "url": "https://huggingface.co/thinkingmachines/Inkling-2-Small/commits/main", "kind": "changelog", "status": 200, "checkedAt": "2026-11-14T19:07:35.812797752Z", "changedAt": "0001-01-01T00:00:00Z", "fingerprint": "39971da7a686" }, { "url": "https://tinker-docs.thinkingmachines.ai/changelog/", "kind": "changelog", "status": 200, "checkedAt": "2026-11-13T18:32:58.088087394Z", "changedAt": "2026-11-13T18:32:58.088087394Z", "fingerprint": "c56006412e95" }, { "url": "https://thinkingmachines.ai/legal/privacy/", "kind": "privacy", "status": 200, "checkedAt": "2026-11-13T18:32:53.884197991Z", "changedAt": "2026-11-06T13:43:40.660240815Z", "fingerprint": "3c33a4a74c61" }, { "url": "https://thinkingmachines.ai/legal/terms/", "kind": "terms", "status": 200, "checkedAt": "2026-11-13T18:32:56.034092745Z", "changedAt": "2026-11-06T13:43:42.918165055Z", "fingerprint": "08c6ceca5dbb" } ], "updatedAt": "2026-11-14T19:07:35.812797752Z" }, "streak": { "days": 9, "from": "2026-11-06", "sinceWatching": true, "best": 9 } }, "answer": "Kolibri-1 scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories.", "b": { "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": 1524, "categoryRank": 34, "methodology": "0.8", "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": "0.8", "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": "2026-11-15T11:25:48.553073532Z" } ], "pypiWeekly": 143, "securityTxt": { "url": "https://aleph-alpha.com/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-11-14T16:18:56.775383443Z" }, "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": "2026-11-14T19:04:29.701168252Z", "changedAt": "2026-11-14T19:04:29.701168252Z", "fingerprint": "7079f7ade933" } ], "updatedAt": "2026-11-15T11:25:48.553073532Z" } }, "edges": [ { "by": 25, "category": "reach", "leads": "kolibri-1", "name": "Reach", "reasons": { "a": "Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line. Runtimes 0, no released version of the six runs it; vLLM and SGLang serve it from their own images and Transformers has it on its main branch only. Builds 15, two precisions from the publisher (NVFP4 and MXFP8). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and Together is live on Hugging Face. 0 + 0 + 15 + 10 + 10 + 5 = 40.", "b": "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." }, "scores": { "a": 40, "b": 65 } }, { "by": 5, "category": "interface", "leads": "kolibri-1", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back for this model, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to xhigh, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens. 20 + 25 + 20 + 0 + 10 + 10 = 85.", "b": "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." }, "scores": { "a": 85, "b": 90 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Thinking Machines Lab", "b": "Aleph Alpha", "name": "Vendor" }, { "a": "no (local only)", "b": "no (local only)", "name": "Hosted endpoint" }, { "a": "", "b": "", "name": "Transports" }, { "a": "None", "b": "None", "name": "Auth" }, { "a": "Free", "b": "Free", "name": "Pricing" }, { "a": "no", "b": "no", "name": "x402" }, { "a": "Apache-2.0", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-11-12", "b": "2026-10-19", "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": "none", "b": "3.3/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Kolibri-1 scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories.", "question": "Which is better for AI agents, Inkling 2 Small or Kolibri-1?" } ], "goodFor": [ { "aheadOn": null, "also": null, "goodFor": "Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image.", "slug": "inkling-2-small", "watchFor": "The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory" }, { "aheadOn": [ "Reach, 65 against 40", "Agent interface, 90 against 85" ], "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" } ], "history": { "inkling-2-small": [ { "causes": [ "product" ], "date": "2026-11-12", "delta": 6.7, "from": 52.1, "fromGrade": "D", "to": 58.8, "toGrade": "C" } ], "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" } ] }, "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-2-small.json", "title": "Agents-A1-4B vs Inkling 2 Small", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-2-small" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.json", "title": "Agents-A1-4B vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-2-small.json", "title": 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"json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-inkling-small.json", "title": "Inkling 2 Small vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Inkling 2 Small vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kimi-k3.json", "title": "Inkling 2 Small vs Kimi K3", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kimi-k3-1-pro.json", "title": "Inkling 2 Small vs Kimi K3.1 Pro", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kimi-k3-1-pro" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kronos.json", "title": "Inkling 2 Small vs Kronos", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kronos" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-m-1.json", "title": "Inkling 2 Small vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-s-2-1.json", "title": "Inkling 2 Small vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-xs-2-1.json", "title": "Inkling 2 Small vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-lbs-27b.json", "title": "Inkling 2 Small vs LBS-27B", "url": 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"https://www.anchorterminal.com/compare/inkling-2-small-vs-lfm3-vl-2b.json", "title": "Inkling 2 Small vs LFM3-VL-2B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-lfm3-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ling-3-0-tiny.json", "title": "Inkling 2 Small vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ling-3-1-flash-1019.json", "title": "Inkling 2 Small vs Ling-3.1-flash-1019", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ling-3-1-flash-1019" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-longcat-2-5.json", "title": "Inkling 2 Small vs LongCat-2.5", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-longcat-2-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Inkling 2 Small vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mimo-v2-6-flash-rl.json", "title": "Inkling 2 Small vs MiMo-V2.6-Flash-RL", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mimo-v2-6-flash-rl" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm-o-4-5.json", "title": "Inkling 2 Small vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm-v-4-7-1b.json", "title": "Inkling 2 Small vs MiniCPM-V 4.7 1B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm-v-4-7-1b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-minicpm-v-4-7-35b-a3b.json", "title": "Inkling 2 Small vs MiniCPM-V 4.7 35B-A3B", "url": 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"https://www.anchorterminal.com/compare/inkling-2-small-vs-mistral-large-4.json", "title": "Inkling 2 Small vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-glimmer-30b.json", "title": "Inkling 2 Small vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nanbeige-4-2-3b.json", "title": "Inkling 2 Small vs Nanbeige4.2-3B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nanbeige-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nanbeige-4-3-33b.json", "title": "Inkling 2 Small vs Nanbeige4.3-33B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nanbeige-4-3-33b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-5-bolt-e4b-4gb.json", "title": "Inkling 2 Small vs NVIDIA Nemotron 3.5 Bolt E4B 4GB", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-5-bolt-e4b-4gb" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Inkling 2 Small vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-30b-a3b.json", "title": "Inkling 2 Small vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-4b.json", "title": "Inkling 2 Small vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Inkling 2 Small vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-super-120b-a12b.json", "title": "Inkling 2 Small vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-ultra-550b-a55b.json", "title": "Inkling 2 Small vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-north-mini-code-1-0.json", "title": "Inkling 2 Small vs North Mini Code 1.0", "url": 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"https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-397b.json", "title": "Inkling 2 Small vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-9b.json", "title": "Inkling 2 Small vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-otel-2-0-llm-31b-it.json", "title": "Inkling 2 Small vs OTel-2.0-LLM-31B-IT", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-otel-2-0-llm-31b-it" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen-agentworld-35b-a3b.json", "title": "Inkling 2 Small vs Qwen-AgentWorld-35B-A3B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen-agentworld-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Inkling 2 Small vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-30b-a3b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507.json", "title": "Inkling 2 Small vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-0-8b.json", "title": "Inkling 2 Small vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-122b-a10b.json", "title": "Inkling 2 Small vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/inkling-2-small-vs-qwen3-5-122b-a10b" }, { "json": 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"https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Kolibri-1 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-thinkingcap-qwen3-8-27b.json", "title": "Kolibri-1 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2.json", "title": "Kolibri-1 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b.json", "title": "Kolibri-1 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b.json", "title": "Kolibri-1 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/kolibri-1-vs-zaya1-8b.json", "title": "Kolibri-1 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/kolibri-1-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 25, "edge": "kolibri-1", "inkling-2-small": 40, "key": "reach", "kolibri-1": 65, "name": "Reach", "weight": 25 }, { "by": 0, "edge": "", "inkling-2-small": 65, "key": "risk", "kolibri-1": 65, "name": "Risk", "weight": 20 }, { "by": 5, "edge": "kolibri-1", "inkling-2-small": 85, "key": "interface", "kolibri-1": 90, "name": "Agent interface", "weight": 10 }, { "by": 3, "edge": "kolibri-1", "inkling-2-small": 67, "key": "upkeep", "kolibri-1": 70, "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Kolibri-1 scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "inkling-2-small": "Thinking Machines' newest open model, a 594B mixture-of-experts generalist with images in, several tool calls a turn and per-request thinking effort. The NVFP4 checkpoint needs about 472 GB, and no released runtime runs it yet, so today that means vLLM's or SGLang's own images, or Tinker and Together hosted. The use policy binds on download and can change.", "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." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kolibri-1", "json": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kolibri-1.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kolibri-1.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/inkling-2-small-vs-kolibri-1.min.md" }, "markdown": "Kolibri-1 scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.\n\n- Inkling 2 Small: grade C, 58.8/100, rank #102 of 161 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-2-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json\n- Kolibri-1: grade C, 61/100, rank #44 of 161 open-weight models. Markdown https://www.anchorterminal.com/tools/kolibri-1.md · JSON https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6472 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Inkling 2 Small (C)\n\nGood for: Teams with 512 GB or more of datacentre GPU memory who want Thinking Machines' newest open multimodal agent model and can run a runtime's pre-release image.\n\nWatch for: The smaller checkpoint needs about 472 GB, and Thinking Machines says 500 GB of GPU memory\n\n### Kolibri-1 (C)\n\nGood for: German and English agent work with tools on a machine with about 96 GB of accelerator memory and vLLM, where a permissive licence matters.\n\nAhead on:\n- Reach, 65 against 40\n- Agent interface, 90 against 85\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\n## Why the edge\n\n### Reach: Kolibri-1 +25\n\n- Inkling 2 Small (40): Memory 0 of 30, the smallest build is Thinking Machines' NVFP4 checkpoint at 393.5 GB, about 472 GB with a fifth added, above the 256 GB line. Runtimes 0, no released version of the six runs it; vLLM and SGLang serve it from their own images and Transformers has it on its main branch only. Builds 15, two precisions from the publisher (NVFP4 and MXFP8). Terms 10, Apache 2.0 with a binding acceptable use policy on top, read as a custom licence. Download 10, no gate. Hosted 5, Thinking Machines' own Tinker serves it as beta serverless inference, and Together is live on Hugging Face. 0 + 0 + 15 + 10 + 10 + 5 = 40.\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\n### Agent interface: Kolibri-1 +5\n\n- Inkling 2 Small (85): Chat template 20, a Jinja file in the repository. Tool-calling format 25, Thinking Machines' tml-renderers guide shows how tools are declared and how calls come back for this model, and its package ships the parser. Trained tool calling 20, several calls in one turn. Structured output 0, not documented. Reasoning control 10, thinking effort is set per request from none to xhigh, and the reasoning comes back in reasoning_content. Context 10, up to 1M tokens. 20 + 25 + 20 + 0 + 10 + 10 = 85.\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\n## Score by category\n\n| Category | Weight | Inkling 2 Small | Kolibri-1 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 65 | Kolibri-1 +25 |\n| Risk | 20% (20 this run) | 65 | 65 | even |\n| Agent interface | 10% (10 this run) | 85 | 90 | Kolibri-1 +5 |\n| Documentation and upkeep | 10% (10 this run) | 67 | 70 | Kolibri-1 +3 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **58.8 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Inkling 2 Small | Kolibri-1 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Thinking Machines Lab | Aleph Alpha |\n| Hosted endpoint | no (local only) | no (local only) |\n| Transports | | |\n| Auth | None | None |\n| Pricing | Free | Free |\n| x402 | no | no |\n| Licence | Apache-2.0 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-11-12 | 2026-10-19 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | none | 3.3/5 (4) |\n\n## How the scores have moved\n\n### Inkling 2 Small\n\n- +6.7 on 12 November 2026 (D to C): product\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## Verdicts\n\n**Inkling 2 Small.** Thinking Machines' newest open model, a 594B mixture-of-experts generalist with images in, several tool calls a turn and per-request thinking effort. The NVFP4 checkpoint needs about 472 GB, and no released runtime runs it yet, so today that means vLLM's or SGLang's own images, or Tinker and Together hosted. The use policy binds on download and can change.\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## Before you call either\n\n### Inkling 2 Small\n\n1. No released runtime runs it yet. Serve the NVFP4 checkpoint from SGLang's dev-inkling-2-small image with the cookbook's recipe for your hardware, or from vLLM's vllm/vllm-openai:inkling-2-small image\n2. In vLLM pass `--reasoning-parser inkling --enable-auto-tool-choice --tool-call-parser inkling`. In SGLang the tool-call parser is `inkling2` and the reasoning parser `inkling`\n3. Transformers' main branch loads only thinkingmachines/Inkling-2-Small-MXFP8, the 622.8 GB checkpoint, and no release has the code yet\n4. Pass `reasoning_effort` as none, minimal, low, medium, high (the default), xhigh or max, or a number from 0 to 0.99\n5. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens\n6. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n7. Images should be 40 to 4096 px a side. The model takes no audio, and the template raises an error for an audio part\n8. Use guided decoding or validate JSON yourself, since structured output isn't documented\n9. Without the hardware, call it on Tinker's OpenAI-compatible endpoint with a Tinker key, at $0.30 input and $1.00 output per million tokens as read on 6 November 2026\n10. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\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## Questions\n\n### Which is better for AI agents, Inkling 2 Small or Kolibri-1?\n\nKolibri-1 scores 61 (C) on agent readiness against Inkling 2 Small's 58.8 (C), and leads in 3 of 4 scored categories.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/inkling-2-small-vs-kolibri-1.json, and with the fewest tokens: https://www.anchorterminal.com/compare/inkling-2-small-vs-kolibri-1.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"inkling-2-small\", \"b\": \"kolibri-1\"}`. From a terminal: `anchor compare inkling-2-small kolibri-1`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/inkling-2-small.json and https://www.anchorterminal.com/api/v1/tools/kolibri-1.json\n\n## Other comparisons with Inkling 2 Small or Kolibri-1\n\n- [Agents-A1-4B vs Inkling 2 Small](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-2-small.md)\n- [Agents-A1-4B vs Kolibri-1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.md)\n- [Apertus v1.5 8B vs Inkling 2 Small](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-2-small.md)\n- [Apertus v1.5 8B vs Kolibri-1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.md)\n- [Apodex-1.1-mini vs Inkling 2 Small](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-inkling-2-small.md)\n- [Apodex-1.1-mini vs Kolibri-1](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-kolibri-1.md)\n- [Arete-27B vs Inkling 2 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