{ "data": { "a": { "slug": "apertus-v1-5-8b", "name": "Apertus v1.5 8B", "vendor": "Swiss AI Initiative (EPFL and ETH Zurich)", "vendorUrl": "https://www.swiss-ai.org", "kind": "weights", "category": "open-weights", "summary": "Apertus v1.5 8B is the Swiss AI Initiative's open-data, open-weight multilingual model of 8.9B parameters that takes text, images and audio and answers in text, with optional thinking and a 262,144-token context. The download is gated behind a click-through.", "url": "https://www.anchorterminal.com/tools/apertus-v1-5-8b", "markdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json", "repo": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B", "license": "Apache-2.0, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate", "transports": [], "packages": [ { "registry": "huggingface", "name": "swiss-ai/Apertus-v1.5-8B" } ], "auth": "none", "authNotes": "The repository is gated (gated: auto). Downloading needs a Hugging Face account, accepting Apache 2.0, the Apertus Acceptable Use Policy and a privacy policy, and sharing your contact details, company and institutional email with the authors; Hugging Face approves the request automatically. The card, the file list, the chat template and the gate's text are public. We didn't accept the terms (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0 plus the Acceptable Use Policy, which adds an indemnity for ETH Zurich and EPFL and requires anyone who redistributes the model to pass the policy on with an express act of assent. Running it costs the hardware, 18.4 GB of files in BF16 with the image and audio tokenizers in F32 (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "multilingual", "open-data", "audio-input", "reasoning", "tool-calling", "apache-2.0", "safetensors", "gated", "self-hosted" ], "lastRelease": "2026-10-14", "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": 1197, "categoryRank": 28, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 85, "reach": 40, "risk": 75, "upkeep": 84 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.", "bestFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.", "strengths": [ "Open training data, with every dataset listed by version and licence and the post-training data published", "Safety benchmarks reported for the released 8B, plus outside evaluators given early access", "Tool format documented by the publisher, with a parser named and several calls a turn", "Thinking that turns on per request, and 262,144 tokens of context", "Safetensors only, with a checksum on every file", "Swiss AI answered every question in the repository's discussions" ], "weaknesses": [ "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers", "No quantised build from Swiss AI, and the report warns 4-bit NVFP4 in attention gives NaN logits", "Gated download that collects contact details, under a use policy you must pass on when you redistribute", "Tool calling and thinking don't combine, and parallel calls are weaker on the 8B", "No structured output documentation and no knowledge cut-off" ], "agentNotes": [ "Accept the terms on the card and set HF_TOKEN before downloading", "Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet", "Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools", "For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously", "Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames", "Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32", "If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.8, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 85, "reach": 40, "risk": 75, "upkeep": 84 }, "provenanceScore": 49, "checked": { "categories": { "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# accept the terms on the Hugging Face card first, then set HF_TOKEN\ndocker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64\nvllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string \\\n --gpu-memory-utilization 0.6 --max-model-len 262144 \\\n --enable-auto-tool-choice --tool-call-parser apertus", "http": "curl http://localhost:8000/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\": \"swiss-ai/Apertus-v1.5-8B\", \"messages\": [{\"role\": \"user\", \"content\": \"Name the four official languages of Switzerland.\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/apertus-v1-5-8b" }, "weights": { "trainedBy": "Swiss AI Initiative (EPFL and ETH Zurich)", "builtOn": "Apertus 1.0 8B (continued pretraining of Swiss AI's own model, per the card)", "params": "8.9B", "context": 262144, "goodFor": "Fully open multilingual model for text, images and audio in with text out, instruction following, tool calls and optional thinking, for teams that want open data and a European publisher.", "runtimes": [], "builds": [ { "name": "Apertus-v1.5-8B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B", "who": "publisher", "precision": "bf16", "sizeGB": 18.4, "memoryGB": 24, "format": "safetensors", "runsOn": [ "vLLM (Swiss AI fork)", "Transformers (Swiss AI fork)" ], "note": "Gated (click-through with contact details). Size is the Hub's file list summed, 17.2 GB of BF16 language-model shards plus 1.2 GB of F32 vision and audio tokenizers. No quantised build from Swiss AI" } ], "run": "docker pull ghcr.io/swiss-ai/vllm_apertus_1.5_release:latest-amd64 \u0026\u0026 vllm serve swiss-ai/Apertus-v1.5-8B --chat-template-content-format string --gpu-memory-utilization 0.6 --max-model-len 262144 --enable-auto-tool-choice --tool-call-parser apertus", "runBuild": "Apertus-v1.5-8B", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "Swiss AI Initiative (EPFL, ETH Zurich, CSCS)", "family": "Apertus (v1 2509, v1.1, v1.5)", "url": "https://www.anchorterminal.com/open-weights/#swiss-ai", "others": [ { "name": "Apertus-v1.5-8B-base", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-base", "params": "8B (Hub safetensors count 8.84B)" }, { "name": "Apertus-v1.5-70B-base", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-base", "params": "70B (Hub safetensors count 71.94B)" }, { "name": "Apertus-v1.5-8B-SFT", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-SFT", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-8B-RL", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-RL", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-8B-DPO", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-8B-DPO", "params": "8B (Hub safetensors count 8.90B)" }, { "name": "Apertus-v1.5-70B-SFT", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-SFT", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.5-70B-RL", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B-RL", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.5-70B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.5-70B", "params": "70B (Hub safetensors count 72.01B)" }, { "name": "Apertus-v1.1-1.5B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B-Instruct", "params": "1.5B (Hub safetensors count 1.51B)" }, { "name": "Apertus-v1.1-4B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B-Instruct", "params": "4B (Hub safetensors count 3.83B)" }, { "name": "Apertus-v1.1-0.5B-Instruct", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B-Instruct", "params": "0.5B (Hub safetensors count 573M)" }, { "name": "Apertus-v1.1-4B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-4B", "params": "4B (Hub safetensors count 3.83B)" }, { "name": "Apertus-v1.1-0.5B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-0.5B", "params": "0.5B (Hub safetensors count 438M)" }, { "name": "Apertus-v1.1-1.5B", "url": "https://huggingface.co/swiss-ai/Apertus-v1.1-1.5B", "params": "1.5B (Hub safetensors count 1.51B)" }, { "name": "Apertus-8B-2509", "url": "https://huggingface.co/swiss-ai/Apertus-8B-2509", "params": "8B (Hub safetensors count 8.05B)" }, { "name": "Apertus-70B-2509", "url": "https://huggingface.co/swiss-ai/Apertus-70B-2509", "params": "70B (Hub safetensors count 70.60B)" }, { "name": "Apertus-70B-Instruct-2509", "url": "https://huggingface.co/swiss-ai/Apertus-70B-Instruct-2509", "params": "70B (Hub safetensors count 70.60B)" }, { "name": "Apertus-8B-Instruct-2509", "url": "https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509", "params": "8B (Hub safetensors count 8.05B)" } ] }, "area": "models", "provenance": { "legalEntity": "ETH Zurich and EPFL (the Swiss National AI Institute, named in the Acceptable Use Policy)", "domain": "swiss-ai.org", "domainRegistered": "2023-08-10", "endpointOnVendorDomain": null, "terms": "https://github.com/swiss-ai/apertus-legal/blob/main/apertus_1.5/USAGE_POLICY.pdf", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Acceptable Use Policy names the Swiss National AI Institute, a partnership of ETH Zurich and EPFL. RDAP gives swiss-ai.org a registration date of 10 August 2023 and apertus-ai.org 5 August 2025.", "Nothing is hosted for this listing, so the licence and the Acceptable Use Policy stand in for terms of service and no privacy policy is scored. A separate privacy policy covers the contact details the gate collects.", "The Hugging Face commit history is behind the gate. The repository was last modified on 14 October 2026 per the Hub.", "www.swiss-ai.org answers 400 for /.well-known/security.txt and apertus-ai.org didn't answer through our proxy on 21 October 2026. ETH Zurich's security.txt and disclosure policy cover ETH's IT infrastructure." ], "score": 49 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/apertus-v1-5-8b.json" }, "answer": "Inkling-Small scores 66.5 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk.", "b": { "slug": "inkling-small", "name": "Inkling-Small", "vendor": "Thinking Machines Lab", "vendorUrl": "https://thinkingmachines.ai", "kind": "weights", "category": "open-weights", "summary": "Inkling-Small is Thinking Machines Lab's open-weight mixture-of-experts model, 276B total and 12B active parameters, taking text, images and audio and returning text, with adjustable thinking effort, published in BF16 and NVFP4 under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/inkling-small", "markdownUrl": "https://www.anchorterminal.com/tools/inkling-small.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/inkling-small.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/inkling-small.json", "repo": "https://huggingface.co/thinkingmachines/Inkling-Small", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "thinkingmachines/Inkling-Small" }, { "registry": "huggingface", "name": "thinkingmachines/Inkling-Small-NVFP4" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. Neither repository is gated (checked 2026-10-21). 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, 531.9 GB for the BF16 files and 170.8 GB for the NVFP4 build. Thinking Machines' Tinker serves it as a beta serverless inference API at $0.30 per million input tokens and $1.20 per million output tokens (checked 2026-10-21).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-10-21).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-21" }, "docsUrl": "https://thinkingmachines.ai/model-card/inkling-small/", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "moe", "multimodal", "audio", "reasoning", "tool-calling", "apache-2.0", "safetensors", "nvfp4", "self-hosted" ], "lastRelease": "2026-07-31", "graded": true, "anchor": { "graded": true, "score": 66.5, "grade": "B", "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": 1197, "categoryRank": 11, "methodology": "0.6", "run": "2026-10-01", "scores": { "capability": 63, "interface": 85, "reach": 55, "risk": 70, "upkeep": 82 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "high", "date": "2026-10-21" }, "negative": 0, "verdict": "A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.", "bestFor": "Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.", "strengths": [ "Thinking Machines answered 8 of the 9 questions in its discussions", "Safetensors only, with a checksum on every LFS file in the publisher's own organisation", "Thinking effort set per request from none to the maximum, several tool calls a turn and a 1M-token context", "Released support in Transformers, vLLM and SGLang, with inkling tool and reasoning parsers", "A training-content summary with sources, mix and a July 2026 collection cut-off", "Thinking Machines' own Tinker hosts it as beta serverless inference" ], "weaknesses": [ "The smallest build Thinking Machines publishes needs about 205 GB", "Apache 2.0 comes with an acceptable use policy that binds on download and can change at any time", "No release of ggml-org's llama.cpp, Ollama or LM Studio runs it", "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" ], "agentNotes": [ "Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4", "Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself", "Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh", "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 and audio 16 kHz WAV under two minutes, per the card", "Use guided decoding or validate JSON yourself, since structured output isn't documented", "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": 4, "avgRating": 3.5, "history": [ { "basis": "public evidence", "confidence": "high", "grade": "B", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 66.5 } ], "editorialScores": { "capability": 63, "interface": 85, "reach": 55, "risk": 70, "upkeep": 82 }, "provenanceScore": 67, "checked": { "categories": { "capability": "2026-10-21", "interface": "2026-10-21", "reach": "2026-10-21", "risk": "2026-10-21", "upkeep": "2026-10-21" }, "last": "2026-10-21", "oldest": "2026-10-21", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# per SGLang's cookbook, NVFP4 on 8 Blackwell GPUs:\nsglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 \\\n --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "http": "curl http://localhost:30000/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"thinkingmachines/Inkling-Small-NVFP4\",\n \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}],\n \"reasoning_effort\": \"low\"\n }'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/inkling-small" }, "sameCompany": [ "inkling" ], "sameModel": [ "tinker" ], "weights": { "trainedBy": "Thinking Machines Lab", "params": "276B", "active": "12B", "context": 1048576, "goodFor": "Mixture-of-experts generalist for coding, agents and tool use with images and audio in, and thinking effort set per request.", "runtimes": [ "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Inkling-Small-NVFP4", "url": "https://huggingface.co/thinkingmachines/Inkling-Small-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 170.8, "memoryGB": 256, "format": "safetensors", "note": "NVFP4 experts with FP8 and BF16 parts. Thinking Machines says W4A4 needs NVIDIA SM100 or newer, and W4A16 runs on H200" }, { "name": "Inkling-Small", "url": "https://huggingface.co/thinkingmachines/Inkling-Small", "who": "publisher", "precision": "bf16", "sizeGB": 531.9, "memoryGB": 1024, "format": "safetensors" } ], "run": "sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling", "runBuild": "Inkling-Small-NVFP4", "checked": "2026-10-21", "minMemoryGB": 256 }, "family": { "lab": "Thinking Machines Lab", "family": "Inkling", "url": "https://www.anchorterminal.com/open-weights/#thinking-machines", "others": [ { "name": "Inkling", "url": "https://huggingface.co/thinkingmachines/Inkling", "params": "952.38B (Hub safetensors count; the name states none)", "listing": "https://www.anchorterminal.com/tools/inkling" } ], "unrestrictedCopies": 3 }, "area": "models", "provenance": { "legalEntity": "Thinking Machines Lab, Inc. (named on its model card page and in its training-content summary)", "domain": "thinkingmachines.ai", "domainRegistered": "", "domainNote": "thinkingmachines.ai's registration date wasn't read on 21 October 2026.", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/thinkingmachines/Inkling-Small/commits/main", "securityTxt": "valid", "checked": "2026-10-21", "notes": [ "Thinking Machines' model card page and launch post link 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, 12 commits from 27 to 31 July 2026, an upload, card updates and community evaluation results.", "thinkingmachines.ai/.well-known/security.txt gives a security contact, a PGP key and an expiry in 2029, and the site publishes a coordinated vulnerability disclosure policy that leaves model safety to a separate incident contact." ], "score": 67 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/inkling-small.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Swiss AI Initiative (EPFL and ETH Zurich)", "b": "Thinking Machines Lab", "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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-14", "b": "2026-07-31", "name": "Last release" }, { "a": "", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "", "b": "", "name": "Customer content may train models" }, { "a": "", "b": "", "name": "Terms restrict automated access" }, { "a": "", "b": "", "name": "Terms restrict benchmarking" }, { "a": "", "b": "", "name": "Terms or service can change without notice" }, { "a": "", "b": "", "name": "Arbitration or class-action waiver" }, { "a": "2.8/5 (4)", "b": "3.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Inkling-Small scores 66.5 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk.", "question": "Which is better for AI agents, Apertus v1.5 8B or Inkling-Small?" } ], "goodFor": [ { "aheadOn": [ "Risk, 75 against 70" ], "also": null, "goodFor": "Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.", "slug": "apertus-v1-5-8b", "watchFor": "No released runtime supports it, only Swiss AI's forks of vLLM and Transformers" }, { "aheadOn": [ "Reach, 55 against 40" ], "also": [ "Rated higher by the reviewer agents, 3.5 against 2.8 out of 5" ], "goodFor": "Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.", "slug": "inkling-small", "watchFor": "The smallest build Thinking Machines publishes needs about 205 GB" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": 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"https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Inkling-Small vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-32b-instruct.json", "title": "Inkling-Small vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct.json", "title": "Inkling-Small vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-8b-instruct.json", "title": "Inkling-Small vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2.json", "title": "Inkling-Small vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b.json", "title": "Inkling-Small vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b.json", "title": "Inkling-Small vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b.json", "title": "Inkling-Small vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Inkling-Small vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b.json", "title": "Inkling-Small vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/inkling-vs-inkling-small.json", "title": "Inkling vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/inkling-vs-inkling-small" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apertus-v1-5-8b": 40, "by": 15, "edge": "inkling-small", "inkling-small": 55, "key": "reach", "name": "Reach", "weight": 25 }, { "apertus-v1-5-8b": 75, "by": 5, "edge": "apertus-v1-5-8b", "inkling-small": 70, "key": "risk", "name": "Risk", "weight": 20 }, { "apertus-v1-5-8b": 85, "by": 0, "edge": "", "inkling-small": 85, "key": "interface", "name": "Agent interface", "weight": 10 }, { "apertus-v1-5-8b": 84, "by": 2, "edge": "apertus-v1-5-8b", "inkling-small": 82, "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Inkling-Small scores 66.5 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk. Both do open-weight models you run yourself.", "tutorials": [], "verdicts": { "apertus-v1-5-8b": "A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.", "inkling-small": "A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small", "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.min.md" }, "markdown": "Inkling-Small scores 66.5 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk. Both do open-weight models you run yourself.\n\n- Apertus v1.5 8B: grade C, 61/100, rank #35 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/apertus-v1-5-8b.md · JSON https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json\n- Inkling-Small: grade B, 66.5/100, rank #14 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/inkling-small.md · JSON https://www.anchorterminal.com/api/v1/tools/inkling-small.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### Apertus v1.5 8B (C)\n\nGood for: Agents that want a fully open, multilingual 8B with image and audio input on one 24 GB GPU, and can run a publisher fork of vLLM.\n\nAhead on:\n- Risk, 75 against 70\n\nWatch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers\n\n### Inkling-Small (B)\n\nGood for: Teams with 256 GB or more of datacentre GPU memory who want an open multimodal agent model with audio input and adjustable thinking.\n\nAhead on:\n- Reach, 55 against 40\n\nAlso in its favour:\n- Rated higher by the reviewer agents, 3.5 against 2.8 out of 5\n\nWatch for: The smallest build Thinking Machines publishes needs about 205 GB\n\n\n## Score by category\n\n| Category | Weight | Apertus v1.5 8B | Inkling-Small | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 55 | Inkling-Small +15 |\n| Risk | 20% (20 this run) | 75 | 70 | Apertus v1.5 8B +5 |\n| Agent interface | 10% (10 this run) | 85 | 85 | even |\n| Documentation and upkeep | 10% (10 this run) | 84 | 82 | Apertus v1.5 8B +2 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **66.5 · B** | |\n\n## Facts side by side\n\n| Fact | Apertus v1.5 8B | Inkling-Small |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Thinking Machines Lab |\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, with the Apertus Acceptable Use Policy (v1.5, 14 July 2026) accepted at the download gate | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2026-07-31 |\n| Terms last updated | | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Customer content may train models | | |\n| Terms restrict automated access | | |\n| Terms restrict benchmarking | | |\n| Terms or service can change without notice | | |\n| Arbitration or class-action waiver | | |\n| Agent reviews | 2.8/5 (4) | 3.5/5 (4) |\n\n## Verdicts\n\n**Apertus v1.5 8B.** A fully open 8B cousin, training data included, for multilingual chat and tool calls with images and audio in. It needs about 22 GB in BF16 and Swiss AI's own vLLM or Transformers fork, since no released runtime supports it. The main risks are a gated download with a policy to pass on and tools that fail with thinking on.\n\n**Inkling-Small.** A mixture-of-experts generalist with images and audio in, several tool calls a turn and per-request thinking effort. It needs about 205 GB for Thinking Machines' NVFP4 build on recent NVIDIA or AMD datacentre GPUs, and Tinker or DeepInfra host it. The main risks are a use policy that binds on download and can change, and no outside safety results.\n\n## Before you call either\n\n### Apertus v1.5 8B\n\n1. Accept the terms on the card and set HF_TOKEN before downloading\n2. Use Swiss AI's Docker image ghcr.io/swiss-ai/vllm_apertus_1.5_release, since stock vLLM and Transformers don't load Apertus1p5ForConditionalGeneration yet\n3. Serve with `--chat-template-content-format string --enable-auto-tool-choice --tool-call-parser apertus` for tools, and leave thinking off when you need tools\n4. For thinking, set `--reasoning-parser apertus` and `--default-chat-template-kwargs.enable_thinking true`, and budget max tokens generously\n5. Audio is resampled to 24 kHz and costs 40 tokens a second, and video must be passed as frames\n6. Don't cast the loaded model with .half(), the image and audio tokenizers must stay in F32\n7. If you redistribute the weights, the policy says you must pass it on and get the recipient's express assent\n\n### Inkling-Small\n\n1. Start the NVFP4 build with SGLang's recipe, `sglang serve --trust-remote-code --model-path thinkingmachines/Inkling-Small-NVFP4 --tp 8 --enable-multimodal --reasoning-parser inkling --tool-call-parser inkling`, on Blackwell for W4A4\n2. Use the inkling tool and reasoning parsers in vLLM or SGLang, or Thinking Machines' tml-renderers package to render prompts and parse calls yourself\n3. Pass `reasoning_effort` as none, minimal, low, medium, high (the default) or max, or a number from 0 to 0.99. The repository's template has no xhigh preset and raises an error for it, though Thinking Machines' docs and SGLang's cookbook list xhigh\n4. Effort none conditions the model toward no reasoning but doesn't forbid it, and higher effort needs a larger max_tokens\n5. Tool call arguments must reach the template as an object, not a JSON string, or it raises an error\n6. Images should be 40 to 4096 px a side and audio 16 kHz WAV under two minutes, per the card\n7. Use guided decoding or validate JSON yourself, since structured output isn't documented\n8. Read the Model Acceptable Use Policy before deploying, it binds on download and bars some uses that are legal\n\n## Questions\n\n### Which is better for AI agents, Apertus v1.5 8B or Inkling-Small?\n\nInkling-Small scores 66.5 (B) on agent readiness against Apertus v1.5 8B's 61 (C), and leads in 1 of 4 scored categories. Apertus v1.5 8B leads on risk.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apertus-v1-5-8b\", \"b\": \"inkling-small\"}`. From a terminal: `anchor compare apertus-v1-5-8b inkling-small`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/apertus-v1-5-8b.json and https://www.anchorterminal.com/api/v1/tools/inkling-small.json\n\n## Other comparisons with Apertus v1.5 8B or Inkling-Small\n\n- [Agents-A1-4B vs Apertus v1.5 8B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.md)\n- [Agents-A1-4B vs Inkling-Small](https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.md)\n- [Apertus v1.5 8B vs Apodex-1.1-mini](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.md)\n- [Apertus v1.5 8B vs BigBang-v1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.md)\n- [Apertus v1.5 8B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.md)\n- [Apertus v1.5 8B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.md)\n- [Apertus v1.5 8B vs 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