{ "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": 1201, "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", "live": { "slug": "apertus-v1-5-8b", "securityTxt": { "url": "https://swiss-ai.org/.well-known/security.txt", "state": "unknown", "checkedAt": "2026-10-23T15:24:48.514148177Z" }, "updatedAt": "2026-10-23T15:24:48.514148177Z" } }, "answer": "Apertus v1.5 8B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on reach.", "b": { "slug": "carbon-x", "name": "Carbon-X", "vendor": "Hugging Face (HuggingFaceBio)", "vendorUrl": "https://huggingface.co/HuggingFaceBio", "kind": "weights", "category": "open-weights", "summary": "Carbon-X is Hugging Face's open-weight 14.8B language model for DNA, a fine-tune of Qwen3-14B that reads raw DNA in a chat and answers in English about variant effects and regulatory regions, with a 40,960-token context, under Apache 2.0.", "url": "https://www.anchorterminal.com/tools/carbon-x", "markdownUrl": "https://www.anchorterminal.com/tools/carbon-x.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/carbon-x.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/carbon-x.json", "repo": "https://huggingface.co/HuggingFaceBio/Carbon-X", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "HuggingFaceBio/Carbon-X" }, { "registry": "huggingface", "name": "HuggingFaceBio/Carbon-X-GGUF" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The Hub reports gated false for HuggingFaceBio/Carbon-X and HuggingFaceBio/Carbon-X-GGUF (checked 2026-10-23). The Python tokenizer fetches Qwen/Qwen3-4B-Base's tokenizer at a pinned revision on first load, which is also ungated.", "pricing": "free", "pricingNotes": "Free to download and use, commercial use included, under Apache 2.0, and the base Qwen3-14B is Apache 2.0 too. Hugging Face sells no per-call access to it and no inference provider serves it. Running it costs the hardware, 29.6 GB for the BF16 safetensors and 9.6 GB for the publisher's smallest GGUF, Q4_K_M (checked 2026-10-23).", "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 2026-10-23).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-10-23" }, "docsUrl": "https://huggingface.co/HuggingFaceBio/Carbon-X", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "dna", "genomics", "biology", "fine-tune", "apache-2.0", "safetensors", "gguf", "self-hosted" ], "lastRelease": "2026-10-22", "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": 1201, "categoryRank": 30, "methodology": "0.6", "run": "2026-10-01", "scores": { "interface": 35, "reach": 90, "risk": 65, "upkeep": 77 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-10-23" }, "negative": 0, "verdict": "A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, it isn't trained for tool calls or reasoning, and the card contradicts its own GGUF repository on llama.cpp support.", "bestFor": "Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model.", "strengths": [ "Hugging Face's own GGUFs at four precisions, the smallest about 12 GB with a fifth added", "Runs in released llama.cpp, vLLM, SGLang and Transformers", "Apache 2.0 on the model and its Qwen3-14B base, nothing gated", "A ready-made skill, an OpenAI-style tool example and an Inspect example for calling it from another agent", "Mid-training data listed by source with shares of 302B tokens" ], "weaknesses": [ "Not clinically validated and no wet-lab validation reported", "The Python tokenizer needs trust_remote_code, and llama.cpp before b11742 tokenizes DNA wrongly", "Not trained for tool calling, structured output or reasoning", "The card says llama.cpp is unsupported while the publisher's GGUF repository says it is", "No inference provider or hosted API, and no knowledge cut-off" ], "agentNotes": [ "Quickest start is `llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{\"enable_thinking\": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0`, on llama.cpp b11742 or later", "With vLLM or Transformers pass trust_remote_code, and expect the tokenizer to fetch Qwen/Qwen3-4B-Base at a pinned revision on first load", "Keep enable_thinking false and the card's sampling (temperature 0.6, top_p 0.95, top_k 20), the settings every reported result used", "Keep DNA in files and pass sequences by name, compute positions and base changes in code and send them as context, the card says the model miscounts long sequences", "Only A, C, G and T form 6-mers, so filter N and other IUPAC codes first or they become a single oov token", "Strip the dna tags from answers if you need bare sequence, llama.cpp keeps them in the text", "Cap max_tokens for next-base questions, long DNA generation is slow and degrades" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 4, "avgRating": 3, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.6", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 35, "reach": 90, "risk": 65, "upkeep": 77 }, "provenanceScore": 53, "checked": { "categories": { "interface": "2026-10-23", "reach": "2026-10-23", "risk": "2026-10-23", "upkeep": "2026-10-23" }, "last": "2026-10-23", "oldest": "2026-10-23", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# llama.cpp b11742 or later (older builds tokenize DNA wrongly):\nllama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja \\\n --chat-template-kwargs '{\"enable_thinking\": false}' \\\n --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0 --port 8080\n# or, with vLLM:\nvllm serve HuggingFaceBio/Carbon-X --trust-remote-code", "http": "curl http://localhost:8080/v1/chat/completions -H 'Content-Type: application/json' \\\n -d '{\"messages\": [{\"role\": \"user\", \"content\": \"Is this sequence more likely coding or non-coding? ATGGCGCTGTGGATGCGCCTCCTG\"}]}'" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/carbon-x" }, "sameCompany": [ "hugging-face-inference-endpoints", "smollm3-3b" ], "weights": { "trainedBy": "Hugging Face with Zhongguancun Academy and TIGEM / University of Naples Federico II", "builtOn": "Qwen/Qwen3-14B (vocabulary extension, mid-training, supervised fine-tune and GRPO; base Apache 2.0, declared in the card's metadata)", "params": "14.8B", "context": 40960, "goodFor": "A DNA specialist another agent calls, for variant-effect questions, regulatory regions and next-base prediction on raw DNA kept on local hardware.", "runtimes": [ "llama.cpp", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Carbon-X-GGUF Q4_K_M", "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF", "who": "publisher", "precision": "q4_k_m", "sizeGB": 9.64, "memoryGB": 12, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "carbon-x-q4_k_m.gguf, 9,636,318,080 bytes. Embedding and output tensors stay at Q8_0. The publisher's own runs put it lowest of the builds on FrontierVEP (67.8) and NextBase (38.6)" }, { "name": "Carbon-X-GGUF Q6_K", "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF", "who": "publisher", "precision": "q6_k", "sizeGB": 12.56, "memoryGB": 16, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "carbon-x-q6_k.gguf, 12,555,946,880 bytes" }, { "name": "Carbon-X-GGUF Q5_K_M", "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF", "who": "publisher", "precision": "q5_k_m", "sizeGB": 11.05, "memoryGB": 16, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "carbon-x-q5_k_m.gguf, 11,051,895,680 bytes" }, { "name": "Carbon-X-GGUF Q8_0", "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF", "who": "publisher", "precision": "q8_0", "sizeGB": 15.76, "memoryGB": 24, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "carbon-x-q8_0.gguf, 15,755,742,080 bytes, the build the publisher recommends" }, { "name": "Carbon-X", "url": "https://huggingface.co/HuggingFaceBio/Carbon-X", "who": "publisher", "precision": "bf16", "sizeGB": 29.64, "memoryGB": 48, "format": "safetensors", "runsOn": [ "vLLM", "Transformers" ], "note": "Needs trust_remote_code for the custom HybridDNATokenizer, and Transformers v5 or later. The card says it fits one 80 GB GPU" }, { "name": "Carbon-X-GGUF BF16", "url": "https://huggingface.co/HuggingFaceBio/Carbon-X-GGUF", "who": "publisher", "precision": "bf16", "sizeGB": 29.65, "memoryGB": 48, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "carbon-x-bf16.gguf, 29,651,012,480 bytes. llama.cpp b11742 or later" } ], "run": "llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{\"enable_thinking\": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0 --port 8080", "runBuild": "Carbon-X-GGUF Q8_0", "checked": "2026-10-23", "minMemoryGB": 12 }, "family": { "lab": "Hugging Face (Carbon)", "family": "Carbon", "url": "https://www.anchorterminal.com/open-weights/#huggingface-bio", "others": [ { "name": "Carbon-A-1.2B", "url": "https://huggingface.co/HuggingFaceBio/Carbon-A-1.2B", "params": "1.2B" }, { "name": "Carbon-8B", "url": "https://huggingface.co/HuggingFaceBio/Carbon-8B", "params": "8.3B" }, { "name": "Carbon-3B", "url": "https://huggingface.co/HuggingFaceBio/Carbon-3B", "params": "3.5B" }, { "name": "Carbon-500M", "url": "https://huggingface.co/HuggingFaceBio/Carbon-500M", "params": "0.5B" } ] }, "area": "models", "provenance": { "legalEntity": "Hugging Face (the HuggingFaceBio organisation, named Hugging Face Biology Research on the Hub)", "domain": "huggingface.co", "domainRegistered": "", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "", "securityTxt": "valid", "checked": "2026-10-23", "notes": [ "The HuggingFaceBio organisation isn't verified on the Hub. It has 27 members, Hugging Face staff among them, and the huggingface/carbon GitHub repository links its Carbon models.", "Nothing is hosted, so the Apache 2.0 licence stands in for terms of service and no privacy policy is scored.", "huggingface.co/.well-known/security.txt names a security contact and expires in 2030.", "No changelog. The main repository's history shows one commit on 22 October 2026; the merged pull requests show earlier commits on 21 and 22 October, so the history was squashed. Registration of huggingface.co wasn't checked." ], "score": 53 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/carbon-x.json", "live": { "slug": "carbon-x", "securityTxt": { "url": "https://huggingface.co/.well-known/security.txt", "state": "valid", "expires": "2030-07-01T08:42:00.000Z", "checkedAt": "2026-10-23T15:25:11.273582159Z" }, "updatedAt": "2026-10-23T15:25:11.273582159Z" } }, "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Swiss AI Initiative (EPFL and ETH Zurich)", "b": "Hugging Face (HuggingFaceBio)", "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": "2026-10-22", "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 (4)", "name": "Agent reviews" } ], "faq": [ { "answer": "Apertus v1.5 8B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on reach.", "question": "Which is better for AI agents, Apertus v1.5 8B or Carbon-X?" } ], "goodFor": [ { "aheadOn": [ "Risk, 75 against 65", "Agent interface, 85 against 35", "Documentation and upkeep, 84 against 77" ], "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, 90 against 40" ], "also": null, "goodFor": "Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model.", "slug": "carbon-x", "watchFor": "Not clinically validated and no wet-lab validation reported" } ], "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-carbon-x.json", "title": "Agents-A1-4B vs Carbon-X", "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x" }, { "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": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-4b-instruct-2507.json", "title": "Apertus v1.5 8B vs Qwen3-4B-Instruct-2507", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-0-8b.json", "title": "Apertus v1.5 8B vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b.json", "title": "Apertus v1.5 8B vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-2b.json", "title": "Apertus v1.5 8B vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-qwen3-5-397b-a17b.json", "title": 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"title": "Carbon-X vs Nemotron 3.5 Lightning 30B A3B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-5-lightning-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-30b-a3b.json", "title": "Carbon-X vs Nemotron 3 Nano 30B A3B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-30b-a3b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-4b.json", "title": "Carbon-X vs Nemotron 3 Nano 4B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-4b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json", "title": "Carbon-X vs Nemotron 3 Nano Omni 30B A3B Reasoning", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-nano-omni-30b-a3b-reasoning" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-nemotron-3-super-120b-a12b.json", "title": "Carbon-X vs Nemotron 3 Super 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"https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-122b-a10b.json", "title": "Carbon-X vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-2b.json", "title": "Carbon-X vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-397b-a17b.json", "title": "Carbon-X vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-4b.json", "title": "Carbon-X vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-9b.json", "title": "Carbon-X vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-27b.json", "title": "Carbon-X vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-35b-a3b.json", "title": "Carbon-X vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-27b.json", "title": "Carbon-X vs Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-flash-next.json", "title": "Carbon-X vs Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-30b-a3b-instruct.json", "title": "Carbon-X vs Qwen3-Coder-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-next.json", "title": "Carbon-X vs Qwen3-Coder-Next", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-coder-next" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-omni-30b-a3b-instruct.json", "title": "Carbon-X vs Qwen3-Omni-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-2b-instruct.json", "title": "Carbon-X vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Carbon-X vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-32b-instruct.json", "title": "Carbon-X vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-4b-instruct.json", "title": "Carbon-X vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-8b-instruct.json", "title": "Carbon-X vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2.json", "title": "Carbon-X vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint.json", "title": "Carbon-X vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-smollm3-3b.json", "title": "Carbon-X vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b.json", "title": "Carbon-X vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-27b.json", "title": "Carbon-X vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Carbon-X vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/carbon-x-vs-thinkingcap-qwen3-8-27b.json", "title": "Carbon-X vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/carbon-x-vs-thinkingcap-qwen3-8-27b" } ], "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "apertus-v1-5-8b": 40, "by": 50, "carbon-x": 90, "edge": "carbon-x", "key": "reach", "name": "Reach", "weight": 25 }, { "apertus-v1-5-8b": 75, "by": 10, "carbon-x": 65, "edge": "apertus-v1-5-8b", "key": "risk", "name": "Risk", "weight": 20 }, { "apertus-v1-5-8b": 85, "by": 50, "carbon-x": 35, "edge": "apertus-v1-5-8b", "key": "interface", "name": "Agent interface", "weight": 10 }, { "apertus-v1-5-8b": 84, "by": 7, "carbon-x": 77, "edge": "apertus-v1-5-8b", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Apertus v1.5 8B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X 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.", "carbon-x": "A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, it isn't trained for tool calls or reasoning, and the card contradicts its own GGUF repository on llama.cpp support." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x", "json": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.min.md" }, "markdown": "Apertus v1.5 8B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on reach. Both do open-weight models you run yourself.\n\n- Apertus v1.5 8B: grade C, 61/100, rank #36 of 107 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- Carbon-X: grade C, 61/100, rank #38 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/carbon-x.md · JSON https://www.anchorterminal.com/api/v1/tools/carbon-x.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 3023 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 65\n- Agent interface, 85 against 35\n- Documentation and upkeep, 84 against 77\n\nWatch for: No released runtime supports it, only Swiss AI's forks of vLLM and Transformers\n\n### Carbon-X (C)\n\nGood for: Agents that need a second opinion on DNA variants and regulatory regions on a 16 to 24 GB machine, without sending sequences to a hosted model.\n\nAhead on:\n- Reach, 90 against 40\n\nWatch for: Not clinically validated and no wet-lab validation reported\n\n\n## Score by category\n\n| Category | Weight | Apertus v1.5 8B | Carbon-X | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 40 | 90 | Carbon-X +50 |\n| Risk | 20% (20 this run) | 75 | 65 | Apertus v1.5 8B +10 |\n| Agent interface | 10% (10 this run) | 85 | 35 | Apertus v1.5 8B +50 |\n| Documentation and upkeep | 10% (10 this run) | 84 | 77 | Apertus v1.5 8B +7 |\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 | Carbon-X |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Swiss AI Initiative (EPFL and ETH Zurich) | Hugging Face (HuggingFaceBio) |\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 | 2026-10-22 |\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 (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**Carbon-X.** A DNA specialist cousin that another agent calls for variant-effect and sequence questions, with DNA kept on the machine. Hugging Face's Q4_K_M GGUF needs about 12 GB and Q8_0 about 19 GB, on llama.cpp b11742 or later. It isn't clinically validated, it isn't trained for tool calls or reasoning, and the card contradicts its own GGUF repository on llama.cpp support.\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### Carbon-X\n\n1. Quickest start is `llama-server -hf HuggingFaceBio/Carbon-X-GGUF:Q8_0 -ngl 99 -c 40960 --jinja --chat-template-kwargs '{\"enable_thinking\": false}' --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0`, on llama.cpp b11742 or later\n2. With vLLM or Transformers pass trust_remote_code, and expect the tokenizer to fetch Qwen/Qwen3-4B-Base at a pinned revision on first load\n3. Keep enable_thinking false and the card's sampling (temperature 0.6, top_p 0.95, top_k 20), the settings every reported result used\n4. Keep DNA in files and pass sequences by name, compute positions and base changes in code and send them as context, the card says the model miscounts long sequences\n5. Only A, C, G and T form 6-mers, so filter N and other IUPAC codes first or they become a single oov token\n6. Strip the dna tags from answers if you need bare sequence, llama.cpp keeps them in the text\n7. Cap max_tokens for next-base questions, long DNA generation is slow and degrades\n\n## Questions\n\n### Which is better for AI agents, Apertus v1.5 8B or Carbon-X?\n\nApertus v1.5 8B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on reach.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.json, and with the fewest tokens: https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"apertus-v1-5-8b\", \"b\": \"carbon-x\"}`. From a terminal: `anchor compare apertus-v1-5-8b carbon-x`\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/carbon-x.json\n\n## Other comparisons with Apertus v1.5 8B or Carbon-X\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 Carbon-X](https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.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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Swift 1.5 Qwen3.8-Flash-Next](https://www.anchorterminal.com/compare/carbon-x-vs-swift-1-5-qwen3-8-flash-next.md)\n- [Carbon-X vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/carbon-x-vs-thinkingcap-qwen3-8-27b.md)\n", "meta": { "attribution": "Anchor Terminal (https://www.anchorterminal.com)", "attributionRequired": true, "credit": "CC BY 4.0, credit Anchor Terminal and link the page or API address you took it from (https://www.anchorterminal.com/terms/#data)", "docs": "https://www.anchorterminal.com/docs/", "generatedAt": "2026-10-23", "license": "CC-BY-4.0", "method": "https://www.anchorterminal.com/benchmark/", "methodology": "0.6", "openapi": "https://www.anchorterminal.com/openapi.json", "preview": false, "run": "2026-10-01", "runLabel": "October 2026 research run", "terms": "https://www.anchorterminal.com/terms/#data" }, "page": { "breadcrumbs": [ { "name": "Home", "url": "https://www.anchorterminal.com/" }, { "name": "Compare", "url": "https://www.anchorterminal.com/compare/" }, { "name": "Apertus v1.5 8B vs Carbon-X", "url": "" } ], "description": "Apertus v1.5 8B and Carbon-X score within a point of each other for open-weight models you run yourself, 61 and 61 out of 100. Prices, MCP, x402, uptime and agent notes side by side.", "facts": [ "Apertus v1.5 8B C 61", "Carbon-X C 61", "scores" ], "h1": "Apertus v1.5 8B vs Carbon-X", "image": "https://www.anchorterminal.com/assets/og/compare-apertus-v1-5-8b-vs-carbon-x.png", "path": "/compare/apertus-v1-5-8b-vs-carbon-x", "published": "2026-10-01", "section": "tools", "title": "Apertus v1.5 8B vs Carbon-X for AI agents in 2026: scores and prices", "toc": null, "updated": "2026-10-23", "url": "https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x" }, "tokens": { "markdown": 6050, "slim": 480 }, "version": 1 }