{ "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": "Apertus v1.5 8B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on reach.", "b": { "slug": "thinkingcap-qwen3-8-27b", "name": "ThinkingCap (Qwen3.8 27B)", "vendor": "BottleCap AI", "vendorUrl": "https://bottlecapai.com", "kind": "weights", "category": "open-weights", "summary": "ThinkingCap-Qwen3.8-27B is BottleCap AI's fine-tune of Alibaba's Qwen3.8-27B that shortens its reasoning traces, a 27B dense vision-language model on Hugging Face under PolyForm Small Business 1.0.0, with a gated main repository.", "url": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b", "markdownUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.json", "repo": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "license": "PolyForm-Small-Business-1.0.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-FP8" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ" }, { "registry": "huggingface", "name": "bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw" } ], "auth": "none", "authNotes": "The BF16 and FP8 repositories are gated (gated auto). A Hugging Face account must accept the card's conditions and give a name, a company and an email before the files open, with automatic approval. The GGUF, NVFP4, NVFP4A4-AWQ, MLX and RCO GGUF repositories were not gated on 21 October 2026, though their cards carry the same request form in their metadata (checked 2026-10-21).", "pricing": "free", "pricingNotes": "Free for personal non-commercial use and for companies with fewer than 100 people and under 1,000,000 USD (2019, inflation-adjusted) revenue, under PolyForm Small Business 1.0.0 plus BottleCap's personal-use grant. Larger organisations need a separate commercial licence from BottleCap AI. Running it costs the hardware, 55.6 GB for BF16 and 15.5 GB for the smallest 4-bit GGUF (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/bottlecapai/ThinkingCap-Qwen3.8-27B", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "multimodal", "reasoning", "tool-calling", "derivative", "gated", "source-available", "safetensors", "gguf", "fp8", "nvfp4", "mlx", "self-hosted" ], "lastRelease": "2026-10-06", "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": 43, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 70, "reach": 75, "risk": 45, "upkeep": 53 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-21" }, "negative": 0, "verdict": "A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.", "bestFor": "Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.", "strengths": [ "Same architecture and template as Qwen3.8-27B, so released vLLM, SGLang, Transformers and llama.cpp run it", "BottleCap publishes FP8, NVFP4, MLX and GGUF builds, the IQ4_XS needing about 19 GB", "Per-request reasoning effort, several tool calls a turn and 262,144 tokens of context", "The card documents its own evaluation method with seeds, intervals and paired quantisation checks", "Safetensors only, in a verified organisation" ], "weaknesses": [ "PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue", "The BF16 and FP8 repositories are gated behind contact details", "No safety results, agent guidance, security contact or training-data description", "No hosted API or inference provider", "Every accuracy and token-saving figure comes from BottleCap's own harness" ], "agentNotes": [ "Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap", "For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB", "On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`", "Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error", "Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop", "The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 2.5, "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": 70, "reach": 75, "risk": 45, "upkeep": 53 }, "provenanceScore": 47, "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": "llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja\n# or the gated BF16 weights, after accepting the card's conditions:\nvllm serve bottlecapai/ThinkingCap-Qwen3.8-27B --reasoning-parser qwen3 \\\n --enable-auto-tool-choice --tool-call-parser qwen3_xml", "http": "curl http://localhost:8080/v1/chat/completions \\\n -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/thinkingcap-qwen3-8-27b" }, "weights": { "trainedBy": "BottleCap AI", "builtOn": "Qwen/Qwen3.8-27B (Apache 2.0), declared in the card's metadata (base_model, base_model_relation finetune) and in NOTICE as the base checkpoint. ThinkingCap's own weights are under PolyForm Small Business 1.0.0 with a personal-use grant, and NOTICE keeps Apache 2.0 for the upstream Qwen materials", "params": "27B", "context": 262144, "goodFor": "Qwen3.8-27B with shorter reasoning traces, for agent and reasoning work where thinking tokens cost time, with images and video in.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "ThinkingCap-Qwen3.8-27B-IQ4_XS", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-IQ4_XS.gguf", "who": "publisher", "precision": "iq4_xs", "sizeGB": 15.5, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Not gated. Image input needs the 0.93 GB mmproj file from the same repository" }, { "name": "ThinkingCap-Qwen3.8-27B-Q4_K_M", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q4_K_M.gguf", "who": "publisher", "precision": "q4_k_m", "sizeGB": 17.4, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B-RCO-4.2bpw", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF-RCO-4.2bpw", "who": "publisher", "precision": "mixed-4.2bpw", "sizeGB": 14.3, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ], "note": "Not gated. Mixed precision averaging 4.19 bits per weight, with the feed-forward weights at 3.8 to 3.9 bits, so it doesn't count as a 4-bit build for the memory line" }, { "name": "ThinkingCap-Qwen3.8-27B-NVFP4", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4", "who": "publisher", "precision": "nvfp4", "sizeGB": 20.6, "memoryGB": 32, "format": "safetensors", "note": "Not gated. Weight-only NVFP4 for vLLM on Hopper (Marlin) and Blackwell" }, { "name": "ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-NVFP4A4-AWQ", "who": "publisher", "precision": "nvfp4", "sizeGB": 23.4, "memoryGB": 32, "format": "safetensors", "note": "Not gated. NVFP4 weights and activations, Blackwell only" }, { "name": "ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-MLX-4bit-DWQ", "who": "publisher", "precision": "mlx-4bit", "sizeGB": 22.8, "memoryGB": 32, "format": "mlx", "runsOn": [ "mlx-vlm" ], "note": "Not gated. Mixed 4 and 8-bit DWQ with the vision tower and MTP head (22.0 GB without the MTP files). The card says to use mlx-vlm or oMLX" }, { "name": "ThinkingCap-Qwen3.8-27B-Q6_K", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q6_K.gguf", "who": "publisher", "precision": "q6_k", "sizeGB": 23.9, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B-FP8", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-FP8", "who": "publisher", "precision": "fp8", "sizeGB": 31.2, "memoryGB": 48, "format": "safetensors", "note": "Gated like the main repository. Block-wise FP8 for vLLM on Hopper and Blackwell, per the card" }, { "name": "ThinkingCap-Qwen3.8-27B-Q8_0", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-Q8_0.gguf", "who": "publisher", "precision": "q8_0", "sizeGB": 29, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] }, { "name": "ThinkingCap-Qwen3.8-27B", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B", "who": "publisher", "precision": "bf16", "sizeGB": 55.6, "memoryGB": 96, "format": "safetensors", "runsOn": [ "vLLM (ROCm)", "SGLang (ROCm)", "Transformers" ], "note": "Gated (auto approval after accepting the card's conditions)" }, { "name": "ThinkingCap-Qwen3.8-27B-f16", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF/blob/main/ThinkingCap-Qwen3.8-27B-f16.gguf", "who": "publisher", "precision": "f16", "sizeGB": 54.7, "memoryGB": 96, "format": "gguf", "runsOn": [ "llama.cpp (Vulkan)", "llama.cpp (ROCm)" ] } ], "run": "llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja", "runBuild": "ThinkingCap-Qwen3.8-27B-Q4_K_M", "checked": "2026-10-21", "minMemoryGB": 24 }, "family": { "lab": "BottleCap AI (bottlecapai)", "family": "ThinkingCap", "url": "https://www.anchorterminal.com/open-weights/#bottlecap", "others": [ { "name": "ThinkingCap-Qwen3.6-27B", "url": "https://huggingface.co/bottlecapai/ThinkingCap-Qwen3.6-27B", "params": "27B (27,356,728,560 by the Hub's safetensors count)" } ], "unrestrictedCopies": 5 }, "area": "models", "provenance": { "legalEntity": "BottleCap AI (named in the licence's copyright line)", "domain": "bottlecapai.com", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "https://bottlecapai.com/privacy-policy/", "statusPage": "", "changelog": "", "securityTxt": "none", "checked": "2026-10-21", "notes": [ "The Hugging Face organisation bottlecapai is verified (team plan, 15 members). BottleCap's blog post for this model links the repository.", "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.", "bottlecapai.com/.well-known/security.txt returns 404. A trust centre at trust.mycroft.io/bottlecap-ai lists SOC 2 and policies behind a request form, with no route for reporting a flaw in the model.", "unchecked: the domain's registration date, and the main repository's commit history, which is behind the gate." ], "score": 47 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.json" }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Swiss AI Initiative (EPFL and ETH Zurich)", "b": "BottleCap AI", "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": "PolyForm-Small-Business-1.0.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-14", "b": "2026-10-06", "name": "Last release" }, { "a": "", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "", "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": "2.5/5 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Apertus v1.5 8B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on reach.", "question": "Which is better for AI agents, Apertus v1.5 8B or ThinkingCap (Qwen3.8 27B)?" } ], "goodFor": [ { "aheadOn": [ "Risk, 75 against 45", "Agent interface, 85 against 70", "Documentation and upkeep, 84 against 53" ], "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, 75 against 40" ], "also": null, "goodFor": "Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.", "slug": "thinkingcap-qwen3-8-27b", "watchFor": "PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue" } ], "job": { "capability": "model.open-weights", "name": "Open-weight models you run yourself" }, "others": [ { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b.json", "title": "Agents-A1-4B vs Apertus v1.5 8B", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apertus-v1-5-8b" }, { "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Agents-A1-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini.json", "title": "Apertus v1.5 8B vs Apodex-1.1-mini", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-apodex-1-1-mini" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1.json", "title": "Apertus v1.5 8B vs BigBang-v1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-bigbang-v1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b.json", "title": "Apertus v1.5 8B vs Cosmos-Reason2-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-cosmos-reason2-2b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus.json", "title": "Apertus v1.5 8B vs Darwin-36B-Opus", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-darwin-36b-opus" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.json", "title": "Apertus v1.5 8B vs DeepSeek-V3.2", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.json", "title": "Apertus v1.5 8B vs DeepSeek V4.1 Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.json", "title": "Apertus v1.5 8B vs DeepSeek-V4-Flash-0731", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.json", "title": "Apertus v1.5 8B vs DiffusionGemma 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 12B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 26B A4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 31B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 E2B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.json", "title": "Apertus v1.5 8B vs Gemma 4 E4B IT", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.json", "title": "Apertus v1.5 8B vs GLM-4.5-Air", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.json", "title": "Apertus v1.5 8B vs GLM-4.7-Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.json", "title": "Apertus v1.5 8B vs GLM-5.3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.json", "title": "Apertus v1.5 8B vs GLM-5.3-Flash", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b.json", "title": "Apertus v1.5 8B vs gpt-oss-120b", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.json", "title": "Apertus v1.5 8B vs gpt-oss-20b", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.json", "title": "Apertus v1.5 8B vs Granite 4.2 3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.json", "title": "Apertus v1.5 8B vs Hy3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.json", "title": "Apertus v1.5 8B vs Inkling", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.json", "title": "Apertus v1.5 8B vs Inkling-Small", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json", "title": "Apertus v1.5 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.json", "title": "Apertus v1.5 8B vs Kimi K3", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1.json", "title": "Apertus v1.5 8B vs Kolibri-1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kolibri-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.json", "title": "Apertus v1.5 8B vs Laguna M.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.json", "title": "Apertus v1.5 8B vs Laguna S 2.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.json", "title": "Apertus v1.5 8B vs Laguna XS 2.1", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.json", "title": "Apertus v1.5 8B vs LFM2.5-2.6B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.json", "title": "Apertus v1.5 8B vs Ling-3.0-tiny", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-distill-qwen-9b.json", "title": "Apertus v1.5 8B vs MiMo-V2.6 Distill Qwen 9B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-distill-qwen-9b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.json", "title": "Apertus v1.5 8B vs MiniCPM-o 4.5", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm5-2b.json", "title": "Apertus v1.5 8B vs MiniCPM5-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm5-2b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.json", "title": "Apertus v1.5 8B vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.json", "title": "Apertus v1.5 8B vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.json", "title": "Apertus v1.5 8B vs Muse Glimmer 30B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b.json", "title": "Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Super 120B A12B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b.json", "title": "Apertus v1.5 8B vs Nemotron 3 Ultra 550B A55B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.json", "title": "Apertus v1.5 8B vs occamy-1.0", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think.json", "title": "Apertus v1.5 8B vs Olmo 3 7B Think", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.json", "title": "Apertus v1.5 8B vs Ornith-1.5-9B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-30b-a3b-instruct-2507.json", "title": "Apertus v1.5 8B vs Qwen3-30B-A3B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-30b-a3b-instruct-2507" }, { "json": 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ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/occamy-1-0-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b.json", "title": "Olmo 3 7B Think vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/olmo-3-7b-think-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-397B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-397b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Ornith-1.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/ornith-1-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-30B-A3B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-30b-a3b-instruct-2507-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-4B-Instruct-2507 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-4b-instruct-2507-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-0.8B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-0-8b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-122B-A10B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-122b-a10b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-2B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-2b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-397B-A17B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-397b-a17b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.5-9B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-5-9b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.6-35B-A3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-6-35b-a3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Coder-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-coder-next-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-Omni-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-omni-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-2B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-2b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-30B-A3B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-30b-a3b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-32B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-32b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-4B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-4b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwen3-VL-8B-Instruct vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwen3-vl-8b-instruct-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b.json", "title": "Qwopus3.8-27B-Flash-V2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/qwopus3-8-27b-flash-v2-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b.json", "title": "SmolLM3-3B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/smollm3-3b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b.json", "title": "Spark-X2.6-4B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/spark-x2-6-4b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-27B vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-27b-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b.json", "title": "Swift 1.5 Qwen3.8-Flash-Next vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/swift-1-5-qwen3-8-flash-next-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apertus-v1-5-8b": 40, "by": 35, "edge": "thinkingcap-qwen3-8-27b", "key": "reach", "name": "Reach", "thinkingcap-qwen3-8-27b": 75, "weight": 25 }, { "apertus-v1-5-8b": 75, "by": 30, "edge": "apertus-v1-5-8b", "key": "risk", "name": "Risk", "thinkingcap-qwen3-8-27b": 45, "weight": 20 }, { "apertus-v1-5-8b": 85, "by": 15, "edge": "apertus-v1-5-8b", "key": "interface", "name": "Agent interface", "thinkingcap-qwen3-8-27b": 70, "weight": 10 }, { "apertus-v1-5-8b": 84, "by": 31, "edge": "apertus-v1-5-8b", "key": "upkeep", "name": "Documentation and upkeep", "thinkingcap-qwen3-8-27b": 53, "weight": 10 } ], "summary": "Apertus v1.5 8B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on reach. 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.", "thinkingcap-qwen3-8-27b": "A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b", "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b.min.md" }, "markdown": "Apertus v1.5 8B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on reach. 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- ThinkingCap (Qwen3.8 27B): grade C, 61/100, rank #50 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/thinkingcap-qwen3-8-27b.md · JSON https://www.anchorterminal.com/api/v1/tools/thinkingcap-qwen3-8-27b.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 45\n- Agent interface, 85 against 70\n- Documentation and upkeep, 84 against 53\n\nWatch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers\n\n### ThinkingCap (Qwen3.8 27B) (C)\n\nGood for: Small teams and individuals running Qwen3.8-27B for agent or reasoning work who want shorter thinking traces on one 24 GB GPU or Mac.\n\nAhead on:\n- Reach, 75 against 40\n\nWatch for: PolyForm Small Business licence, free commercially only below 100 people and 1,000,000 USD (2019) revenue\n\n\n## Score by category\n\n| Category | Weight | Apertus v1.5 8B | ThinkingCap (Qwen3.8 27B) | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 75 | ThinkingCap (Qwen3.8 27B) +35 |\n| Risk | 20% (20 this run) | 75 | 45 | Apertus v1.5 8B +30 |\n| Agent interface | 10% (10 this run) | 85 | 70 | Apertus v1.5 8B +15 |\n| Documentation and upkeep | 10% (10 this run) | 84 | 53 | Apertus v1.5 8B +31 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Apertus v1.5 8B | ThinkingCap (Qwen3.8 27B) |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | BottleCap AI |\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 | PolyForm-Small-Business-1.0.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-14 | 2026-10-06 |\n| Terms last updated | | no document linked |\n| Privacy policy last updated | 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) | 2.5/5 (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**ThinkingCap (Qwen3.8 27B).** A Qwen3.8-27B with shorter reasoning, for agent work where thinking tokens cost time, and BottleCap's 4-bit GGUF runs in about 19 GB. The main limits are the licence, which needs a paid licence above 100 people or 1,000,000 USD revenue, a gated main repository, and no safety results or training-data description.\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### ThinkingCap (Qwen3.8 27B)\n\n1. Check the licence first, an organisation with 100 or more people or 1,000,000 USD (2019) revenue needs a commercial licence from BottleCap\n2. For files without the gate use `llama-server -hf bottlecapai/ThinkingCap-Qwen3.8-27B-GGUF:Q4_K_M --jinja`, about 17.4 GB\n3. On vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_xml`, and on SGLang `--tool-call-parser qwen3_coder`\n4. Set `reasoning_effort` (xhigh, medium or low) in chat_template_kwargs, any other value makes the template raise an error\n5. Keep the base's sampling (temperature 1.0, top_p 0.95, top_k 20), the GGUF card says greedy decoding can loop\n6. The GGUFs carry an MTP head, and llama.cpp builds without MTP support for this architecture may refuse to load them with a missing blk.64 tensor\n\n## Questions\n\n### Which is better for AI agents, Apertus v1.5 8B or ThinkingCap (Qwen3.8 27B)?\n\nApertus v1.5 8B and ThinkingCap (Qwen3.8 27B) score within a point of each other on agent readiness, 61 (C) and 61 (C). ThinkingCap (Qwen3.8 27B) leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-thinkingcap-qwen3-8-27b.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apertus-v1-5-8b\", \"b\": \"thinkingcap-qwen3-8-27b\"}`. From a terminal: `anchor compare apertus-v1-5-8b thinkingcap-qwen3-8-27b`\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/thinkingcap-qwen3-8-27b.json\n\n## Other comparisons with Apertus v1.5 8B or ThinkingCap (Qwen3.8 27B)\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 ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/agents-a1-4b-vs-thinkingcap-qwen3-8-27b.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 DeepSeek-V3.2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v3-2.md)\n- [Apertus v1.5 8B vs DeepSeek V4.1 Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-1-flash.md)\n- [Apertus v1.5 8B vs DeepSeek-V4-Flash-0731](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-deepseek-v4-flash-0731.md)\n- [Apertus v1.5 8B vs DiffusionGemma 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-diffusiongemma-26b-a4b-it.md)\n- [Apertus v1.5 8B vs Gemma 4 12B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-12b-it.md)\n- [Apertus v1.5 8B vs Gemma 4 26B A4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-26b-a4b-it.md)\n- [Apertus v1.5 8B vs Gemma 4 31B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-31b-it.md)\n- [Apertus v1.5 8B vs Gemma 4 E2B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e2b-it.md)\n- [Apertus v1.5 8B vs Gemma 4 E4B IT](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gemma-4-e4b-it.md)\n- [Apertus v1.5 8B vs GLM-4.5-Air](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-5-air.md)\n- [Apertus v1.5 8B vs GLM-4.7-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-4-7-flash.md)\n- [Apertus v1.5 8B vs GLM-5.3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3.md)\n- [Apertus v1.5 8B vs GLM-5.3-Flash](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-glm-5-3-flash.md)\n- [Apertus v1.5 8B vs gpt-oss-120b](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-120b.md)\n- [Apertus v1.5 8B vs gpt-oss-20b](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-gpt-oss-20b.md)\n- [Apertus v1.5 8B vs Granite 4.2 3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-granite-4-2-3b.md)\n- [Apertus v1.5 8B vs Hy3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-hy3.md)\n- [Apertus v1.5 8B vs Inkling](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling.md)\n- [Apertus v1.5 8B vs Inkling-Small](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-inkling-small.md)\n- [Apertus v1.5 8B vs InternVL3.5-GPT-OSS-20B-A4B-Preview](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-internvl3-5-gpt-oss-20b-a4b-preview.md)\n- [Apertus v1.5 8B vs Kimi K3](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-kimi-k3.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 Laguna M.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-m-1.md)\n- [Apertus v1.5 8B vs Laguna S 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-s-2-1.md)\n- [Apertus v1.5 8B vs Laguna XS 2.1](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-laguna-xs-2-1.md)\n- [Apertus v1.5 8B vs LFM2.5-2.6B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-lfm2-5-2-6b.md)\n- [Apertus v1.5 8B vs Ling-3.0-tiny](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ling-3-0-tiny.md)\n- [Apertus v1.5 8B vs MiMo-V2.6 Distill Qwen 9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-mimo-v2-6-distill-qwen-9b.md)\n- [Apertus v1.5 8B vs MiniCPM-o 4.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm-o-4-5.md)\n- [Apertus v1.5 8B vs MiniCPM5-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minicpm5-2b.md)\n- [Apertus v1.5 8B vs MiniMax-M2.5](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-5.md)\n- [Apertus v1.5 8B vs MiniMax-M2.7](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-minimax-m2-7.md)\n- [Apertus v1.5 8B vs Muse Glimmer 30B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-muse-glimmer-30b.md)\n- [Apertus v1.5 8B vs Nemotron 3.5 Lightning 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-5-lightning-30b-a3b.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano 30B A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-30b-a3b.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano 4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-4b.md)\n- [Apertus v1.5 8B vs Nemotron 3 Nano Omni 30B A3B Reasoning](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.md)\n- [Apertus v1.5 8B vs Nemotron 3 Super 120B A12B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-super-120b-a12b.md)\n- [Apertus v1.5 8B vs Nemotron 3 Ultra 550B A55B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-nemotron-3-ultra-550b-a55b.md)\n- [Apertus v1.5 8B vs occamy-1.0](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-occamy-1-0.md)\n- [Apertus v1.5 8B vs Olmo 3 7B Think](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-olmo-3-7b-think.md)\n- [Apertus v1.5 8B vs Ornith-1.5-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-35b-a3b.md)\n- [Apertus v1.5 8B vs Ornith-1.5-397B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-397b.md)\n- [Apertus v1.5 8B vs Ornith-1.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-ornith-1-5-9b.md)\n- [Apertus v1.5 8B vs Qwen3-30B-A3B-Instruct-2507](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-30b-a3b-instruct-2507.md)\n- [Apertus v1.5 8B vs Qwen3-4B-Instruct-2507](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-4b-instruct-2507.md)\n- [Apertus v1.5 8B vs Qwen3.5-0.8B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-0-8b.md)\n- [Apertus v1.5 8B vs Qwen3.5-122B-A10B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b.md)\n- [Apertus v1.5 8B vs Qwen3.5-2B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-2b.md)\n- [Apertus v1.5 8B vs Qwen3.5-397B-A17B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b.md)\n- [Apertus v1.5 8B vs Qwen3.5-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-4b.md)\n- [Apertus v1.5 8B vs Qwen3.5-9B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-9b.md)\n- [Apertus v1.5 8B vs Qwen3.6-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-27b.md)\n- [Apertus v1.5 8B vs Qwen3.6-35B-A3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-6-35b-a3b.md)\n- [Apertus v1.5 8B vs Qwen3.8-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-27b.md)\n- [Apertus v1.5 8B vs Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-8-flash-next.md)\n- [Apertus v1.5 8B vs Qwen3-Coder-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-30b-a3b-instruct.md)\n- [Apertus v1.5 8B vs Qwen3-Coder-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-coder-next.md)\n- [Apertus v1.5 8B vs Qwen3-Omni-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-omni-30b-a3b-instruct.md)\n- [Apertus v1.5 8B vs Qwen3-VL-2B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-2b-instruct.md)\n- [Apertus v1.5 8B vs Qwen3-VL-30B-A3B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-30b-a3b-instruct.md)\n- [Apertus v1.5 8B vs Qwen3-VL-32B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-32b-instruct.md)\n- [Apertus v1.5 8B vs Qwen3-VL-4B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-4b-instruct.md)\n- [Apertus v1.5 8B vs Qwen3-VL-8B-Instruct](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-vl-8b-instruct.md)\n- [Apertus v1.5 8B vs Qwopus3.8-27B-Flash-V2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwopus3-8-27b-flash-v2.md)\n- [Apertus v1.5 8B vs SmolLM3-3B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-smollm3-3b.md)\n- [Apertus v1.5 8B vs Spark-X2.6-4B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-spark-x2-6-4b.md)\n- [Apertus v1.5 8B vs Swift 1.5 Qwen3.8-27B](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-27b.md)\n- [Apertus v1.5 8B vs Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Apodex-1.1-mini vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-thinkingcap-qwen3-8-27b.md)\n- [BigBang-v1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/bigbang-v1-vs-thinkingcap-qwen3-8-27b.md)\n- 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26B A4B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-26b-a4b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 31B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 E2B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-e2b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [Gemma 4 E4B IT vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gemma-4-e4b-it-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-4.5-Air vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-4-5-air-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-4.7-Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-4-7-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-5.3-Flash vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-flash-vs-thinkingcap-qwen3-8-27b.md)\n- [GLM-5.3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/glm-5-3-vs-thinkingcap-qwen3-8-27b.md)\n- [gpt-oss-120b vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gpt-oss-120b-vs-thinkingcap-qwen3-8-27b.md)\n- [gpt-oss-20b vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/gpt-oss-20b-vs-thinkingcap-qwen3-8-27b.md)\n- [Granite 4.2 3B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/granite-4-2-3b-vs-thinkingcap-qwen3-8-27b.md)\n- [Hy3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/hy3-vs-thinkingcap-qwen3-8-27b.md)\n- [Inkling-Small vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/inkling-small-vs-thinkingcap-qwen3-8-27b.md)\n- [Inkling vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/inkling-vs-thinkingcap-qwen3-8-27b.md)\n- [InternVL3.5-GPT-OSS-20B-A4B-Preview vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/internvl3-5-gpt-oss-20b-a4b-preview-vs-thinkingcap-qwen3-8-27b.md)\n- [Kimi K3 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/kimi-k3-vs-thinkingcap-qwen3-8-27b.md)\n- [Kolibri-1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/kolibri-1-vs-thinkingcap-qwen3-8-27b.md)\n- [Laguna M.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-m-1-vs-thinkingcap-qwen3-8-27b.md)\n- [Laguna S 2.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-s-2-1-vs-thinkingcap-qwen3-8-27b.md)\n- [Laguna XS 2.1 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/laguna-xs-2-1-vs-thinkingcap-qwen3-8-27b.md)\n- [LFM2.5-2.6B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/lfm2-5-2-6b-vs-thinkingcap-qwen3-8-27b.md)\n- [Ling-3.0-tiny vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/ling-3-0-tiny-vs-thinkingcap-qwen3-8-27b.md)\n- [MiMo-V2.6 Distill Qwen 9B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/mimo-v2-6-distill-qwen-9b-vs-thinkingcap-qwen3-8-27b.md)\n- [MiniCPM-o 4.5 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minicpm-o-4-5-vs-thinkingcap-qwen3-8-27b.md)\n- [MiniCPM5-2B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minicpm5-2b-vs-thinkingcap-qwen3-8-27b.md)\n- [MiniMax-M2.5 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minimax-m2-5-vs-thinkingcap-qwen3-8-27b.md)\n- [MiniMax-M2.7 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b.md)\n- [Muse Glimmer 30B vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/muse-glimmer-30b-vs-thinkingcap-qwen3-8-27b.md)\n- [Nemotron 3.5 Lightning 30B A3B vs ThinkingCap (Qwen3.8 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"name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Apertus v1.5 8B vs ThinkingCap (Qwen3.8 27B)", "url": "" } ], "description": "Apertus v1.5 8B and ThinkingCap score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. 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