{ "data": { "a": { "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", "dns": "carbon-x.grade.anchorterminal.com", "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": "2026-10-28", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 32, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 35, "reach": 90, "risk": 65, "upkeep": 72 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-03" }, "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, isn't trained for tool calls or reasoning, and its Python tokenizer runs repository code.", "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", "tokenizer_config.json gives 131,072 tokens as model_max_length while the card and config give 40,960", "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": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 35, "reach": 90, "risk": 65, "upkeep": 72 }, "provenanceScore": 61, "checked": { "categories": { "interface": "2026-11-03", "reach": "2026-11-03", "risk": "2026-11-03", "upkeep": "2026-11-03" }, "last": "2026-11-03", "lastMoved": "2026-11-03", "oldest": "2026-11-03", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "# 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-providers", "hugging-face-inference-endpoints", "smolagents", "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": "2026-11-03", "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 five commits from 22 to 29 October 2026, the first a squashed upload; the merged pull requests show earlier commits on 21 and 22 October. Registration of huggingface.co wasn't checked." ], "score": 61 }, "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": "2026-11-10T16:08:41.928222854Z" }, "domain": { "domain": "huggingface.co", "checkedAt": "2026-11-08T14:53:11.106908704Z" }, "updatedAt": "2026-11-10T16:08:41.928222854Z" } }, "answer": "Carbon-X and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on agent interface.", "b": { "slug": "devstral-small-2", "name": "Devstral Small 2", "vendor": "Mistral AI", "vendorUrl": "https://mistral.ai", "kind": "weights", "category": "open-weights", "summary": "Devstral Small 2 is Mistral's open-weight coding agent model, 24B parameters with image input, tool calling and a 256K context. Apache 2.0. It runs in vLLM, SGLang, Transformers, llama.cpp and Ollama, where the 4-bit build is 15 GB.", "url": "https://www.anchorterminal.com/tools/devstral-small-2", "markdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.md", "slimMarkdownUrl": "https://www.anchorterminal.com/tools/devstral-small-2.min.md", "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json", "dns": "devstral-small-2.grade.anchorterminal.com", "repo": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512", "license": "Apache-2.0", "transports": [], "packages": [ { "registry": "huggingface", "name": "mistralai/Devstral-Small-2-24B-Instruct-2512" }, { "registry": "ollama", "name": "devstral-small-2" } ], "auth": "none", "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The repository is not gated (checked 2026-11-10).", "pricing": "free", "pricingNotes": "Free to download and use under Apache 2.0. Running it yourself takes 15 GB at Q4_K_M from Ollama's library, 26 GB in FP8 from Mistral, or 26 GB at Q8_0 from ggml-org. Mistral's API serves it too (checked 2026-11-10).", "priceSummary": "Free", "where": "local", "x402": { "level": "no", "evidence": "Weights you download and run yourself, so there is nothing to pay per call and no payment protocol (checked 2026-11-10).", "endpoints": [] }, "toolCount": null, "popularity": { "githubStars": null, "npmWeekly": null, "pypiWeekly": null, "asOf": "2026-11-10" }, "docsUrl": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512", "capabilities": [ "model.open-weights" ], "tags": [ "open-weights", "model", "dense", "coding", "agentic", "vision", "tool-calling", "long-context", "apache-2.0", "safetensors", "fp8", "gguf", "ollama", "self-hosted" ], "lastRelease": "2026-07-15", "graded": true, "anchor": { "graded": true, "score": 61, "grade": "C", "agentReady": false, "rank": 0, "ranked": false, "notRankedWhy": "An open-weight model, graded by the open-weight checklist and ranked among open-weight models, not against tools", "rankOf": 1518, "categoryRank": 50, "methodology": "0.7", "run": "2026-10-01", "scores": { "interface": 65, "reach": 90, "risk": 60, "upkeep": 46 }, "pending": [ "performance", "tasks" ], "assessment": { "confidence": "low", "date": "2026-11-10" }, "negative": 0, "verdict": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.", "bestFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac", "strengths": [ "Runs in vLLM, SGLang, Transformers, llama.cpp and Ollama", "15 GB at 4 bits from Ollama's library", "Apache 2.0, ungated, and on Mistral's API", "Tool calling documented, with test prompts and expected output", "A 256K context with image input" ], "weaknesses": [ "No limitations, safety results, languages or data on the card", "No reasoning mode", "Tool calls one at a time, by the card's own tests", "Released in December 2025, and newer coding models have passed it", "Mistral ships FP8 only. The 4-bit builds are other people's" ], "agentNotes": [ "`ollama run devstral-small-2` is the quickest start, at 15 GB", "On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`", "Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it", "Use a low temperature. The repository's default is 0.15", "Plan for one tool call per turn", "The same model is on our Mistral API listing if you'd rather not host it" ], "metrics": { "kind": "local", "measured": false }, "reviewCount": 0, "avgRating": 0, "history": [ { "basis": "public evidence", "confidence": "low", "grade": "C", "methodology": "0.7", "pending": [ "performance", "tasks" ], "run": "2026-10-01", "runLabel": "October 2026 research run", "score": 61 } ], "editorialScores": { "interface": 65, "reach": 90, "risk": 60, "upkeep": 46 }, "provenanceScore": 84, "checked": { "categories": { "interface": "2026-11-10", "reach": "2026-11-10", "risk": "2026-11-10", "upkeep": "2026-11-10" }, "last": "2026-11-10", "oldest": "2026-11-10", "stale": false, "staleAfterDays": 90 } }, "connect": { "install": "ollama run devstral-small-2\n# or: vllm serve mistralai/Devstral-Small-2-24B-Instruct-2512 --max-model-len 262144 --tool-call-parser mistral --enable-auto-tool-choice", "http": "# vLLM serves an OpenAI-compatible API at http://localhost:8000/v1, and Ollama at http://localhost:11434/v1.\n# Send the system prompt from CHAT_SYSTEM_PROMPT.txt and tools in the OpenAI function format" }, "letme": { "capability": "https://letme.dev/model.open-weights", "tool": "https://letme.dev/devstral-small-2" }, "sameCompany": [ "mistral-embeddings", "mistral-moderation", "mistral-voxtral-transcribe", "voxtral-mini-4b-realtime-2602", "mistral-ocr", "mistral-vibe-cli", "mistral-large-4" ], "sameModel": [ "mistral-api" ], "weights": { "trainedBy": "Mistral AI", "builtOn": "Mistral-Small-3.1-24B-Base-2503, per the metadata", "params": "24B", "context": 262144, "goodFor": "A coding agent on one 24 GB GPU or a 32 GB Mac: exploring a repository, editing files and calling tools, with screenshots and diagrams as input.", "runtimes": [ "llama.cpp", "Ollama", "vLLM", "SGLang", "Transformers" ], "builds": [ { "name": "Ollama devstral-small-2 (Q4_K_M)", "url": "https://ollama.com/library/devstral-small-2", "who": "ollama", "precision": "q4_k_m", "sizeGB": 15, "memoryGB": 24, "format": "ollama", "runsOn": [ "Ollama" ], "note": "From Ollama's own library, 15 GB as its tags page lists it" }, { "name": "Devstral-Small-2-24B-Instruct-2512 (FP8 safetensors)", "url": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512", "who": "publisher", "precision": "fp8", "sizeGB": 25.79, "memoryGB": 32, "format": "safetensors", "runsOn": [ "vLLM", "SGLang", "Transformers" ], "note": "Mistral's release precision. The repository holds it in two layouts" }, { "name": "ggml-org Devstral-Small-2-24B-Instruct-2512-GGUF (Q8_0)", "url": "https://huggingface.co/ggml-org/Devstral-Small-2-24B-Instruct-2512-GGUF", "who": "llama.cpp", "precision": "q8_0", "sizeGB": 25.94, "memoryGB": 32, "format": "gguf", "runsOn": [ "llama.cpp" ], "note": "From ggml-org, the llama.cpp project's organisation. 25.06 GB of Q8_0 weights plus the 0.88 GB F16 projector, from the Hub's model information on 10 November 2026" }, { "name": "Ollama devstral-small-2 (Q8_0)", "url": "https://ollama.com/library/devstral-small-2", "who": "ollama", "precision": "q8_0", "sizeGB": 26, "memoryGB": 32, "format": "ollama", "runsOn": [ "Ollama" ], "note": "From Ollama's own library, 26 GB as its tags page lists it" } ], "run": "ollama run devstral-small-2", "runBuild": "Ollama devstral-small-2 (Q4_K_M)", "checked": "2026-11-10", "minMemoryGB": 24 }, "family": { "lab": "Mistral AI", "family": "Devstral", "url": "https://www.anchorterminal.com/open-weights/#mistral", "olderCount": 49 }, "area": "models", "provenance": { "legalEntity": "Mistral AI (RCS Paris 952 418 325)", "domain": "mistral.ai", "domainRegistered": "2019-05-15", "endpointOnVendorDomain": null, "terms": "", "privacy": "", "statusPage": "", "changelog": "https://huggingface.co/mistralai/Devstral-Small-2-24B-Instruct-2512/commits/main", "securityTxt": "valid", "checked": "2026-11-10", "notes": [ "The files sit in Mistral's verified mistralai organisation. Legal entity, domain and security.txt are carried from our Mistral Large 4 listing.", "Nothing is hosted under this listing, so Apache 2.0 stands in for terms of service and no privacy policy is scored. The Mistral API that serves this model has its own listing.", "The changelog is the commit history, 25 commits from 9 December 2025 to 15 July 2026." ], "score": 84 }, "pageJsonUrl": "https://www.anchorterminal.com/tools/devstral-small-2.json" }, "edges": [ { "by": 30, "category": "interface", "leads": "devstral-small-2", "name": "Agent interface", "reasons": { "a": "Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the inherited template shows the format and no parser is named. Trained tool calling 0, nothing says it was trained to call tools. Structured output 0, not documented. Reasoning control 0, thinking can be switched off but was never trained, and the card asks you to keep it off. Context 5, 40,960 tokens from the base, though Hugging Face trained at 8,192 and under 16K. Most of what the card documents for agents is Carbon-X as a tool another model calls, which this checklist doesn't read.", "b": "Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65." }, "scores": { "a": 35, "b": 65 } }, { "by": 26, "category": "upkeep", "leads": "carbon-x", "name": "Documentation and upkeep", "reasons": { "a": "Model card 17, everything but a knowledge cut-off. Training data 15, the mid-training sources and shares are listed, the instruction and fine-tuning data aren't named, and there's no cut-off. Last change 20, 28 October 2026, when config.json, generation_config.json, tokenizer_config.json and tokenizer.py were fixed. Responsiveness 10, organisation members replied to 2 of the 4 questions so far. Version policy 5, a first release whose history was squashed without a note. File health 5, the card now sends llama.cpp users to the publisher's GGUF repository, but the tokenizer config's 131,072 still contradicts the card's 40,960. 17 + 15 + 20 + 10 + 5 + 5 = 72.", "b": "Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46." }, "scores": { "a": 72, "b": 46 } }, { "by": 5, "category": "risk", "leads": "carbon-x", "name": "Risk", "reasons": { "a": "File safety 15, safetensors and GGUF only, but the documented loading path runs the repository's tokenizer.py through trust_remote_code. Provenance 15, the files are in HuggingFaceBio, which Hugging Face's GitHub repository links, with a checksum on every file. Safety evaluation 10, Hugging Face reports its own dual-use check on sequence editing in the launch post, without numbers or a harness. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, Hugging Face's security.txt is a reporting route, and public advisories couldn't be checked.", "b": "File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60." }, "scores": { "a": 65, "b": 60 } } ], "facts": [ { "a": "Open-weight model", "b": "Open-weight model", "name": "Kind" }, { "a": "Hugging Face (HuggingFaceBio)", "b": "Mistral 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", "b": "Apache-2.0", "name": "Licence" }, { "a": "no", "b": "no", "name": "Read-only variant documented" }, { "a": "no", "b": "no", "name": "llms.txt" }, { "a": "2026-10-28", "b": "2026-07-15", "name": "Last release" }, { "a": "no document linked", "b": "no document linked", "name": "Terms last updated" }, { "a": "no document linked", "b": "no document linked", "name": "Privacy policy last updated" }, { "a": "3/5 (4)", "b": "none", "name": "Agent reviews" } ], "faq": [ { "answer": "Carbon-X and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on agent interface.", "question": "Which is better for AI agents, Carbon-X or Devstral Small 2?" } ], "goodFor": [ { "aheadOn": [ "Risk, 65 against 60", "Documentation and upkeep, 72 against 46" ], "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" }, { "aheadOn": [ "Agent interface, 65 against 35" ], "also": null, "goodFor": "Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac", "slug": "devstral-small-2", "watchFor": "No limitations, safety results, languages or data on the card" } ], "history": { "carbon-x": [ { "causes": [ "evidence" ], "date": "2026-11-03", "delta": 0, "from": 61, "fromGrade": "C", "to": 61, "toGrade": "C" } ], "devstral-small-2": null }, "job": { "capability": "model.open-weights", "name": 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"title": "Devstral Small 2 vs MiniMax-M2.5", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-5" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-7.json", "title": "Devstral Small 2 vs MiniMax-M2.7", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m2-7" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m3-1-flash.json", "title": "Devstral Small 2 vs MiniMax-M3.1-Flash", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-minimax-m3-1-flash" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-mistral-large-4.json", "title": "Devstral Small 2 vs Mistral Large 4", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-mistral-large-4" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-muse-glimmer-30b.json", "title": "Devstral Small 2 vs Muse Glimmer 30B", "url": 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Think", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-olmo-3-7b-think" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-122b.json", "title": "Devstral Small 2 vs Ornith-1.5-122B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-122b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-35b-a3b.json", "title": "Devstral Small 2 vs Ornith-1.5-35B-A3B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b.json", "title": "Devstral Small 2 vs Ornith-1.5-397B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-397b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ornith-1-5-9b.json", "title": "Devstral Small 2 vs Ornith-1.5-9B", "url": 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"https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-4b-instruct-2507" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-0-8b.json", "title": "Devstral Small 2 vs Qwen3.5-0.8B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-0-8b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-122b-a10b.json", "title": "Devstral Small 2 vs Qwen3.5-122B-A10B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-122b-a10b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-2b.json", "title": "Devstral Small 2 vs Qwen3.5-2B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-2b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-397b-a17b.json", "title": "Devstral Small 2 vs Qwen3.5-397B-A17B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-397b-a17b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-4b.json", "title": "Devstral Small 2 vs Qwen3.5-4B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-4b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-9b.json", "title": "Devstral Small 2 vs Qwen3.5-9B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-5-9b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-6-27b.json", "title": "Devstral Small 2 vs Qwen3.6-27B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-6-27b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-6-35b-a3b.json", "title": "Devstral Small 2 vs Qwen3.6-35B-A3B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-6-35b-a3b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-8-27b.json", "title": "Devstral Small 2 vs Qwen3.8-27B", "url": 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"https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-omni-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-2b-instruct.json", "title": "Devstral Small 2 vs Qwen3-VL-2B-Instruct", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-2b-instruct" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-30b-a3b-instruct.json", "title": "Devstral Small 2 vs Qwen3-VL-30B-A3B-Instruct", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-30b-a3b-instruct" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-32b-instruct.json", "title": "Devstral Small 2 vs Qwen3-VL-32B-Instruct", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-32b-instruct" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-4b-instruct.json", "title": "Devstral Small 2 vs Qwen3-VL-4B-Instruct", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-4b-instruct" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct.json", "title": "Devstral Small 2 vs Qwen3-VL-8B-Instruct", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwen3-vl-8b-instruct" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwopus3-8-27b-flash-v2.json", "title": "Devstral Small 2 vs Qwopus3.8-27B-Flash-V2", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-qwopus3-8-27b-flash-v2" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-recursive-flint.json", "title": "Devstral Small 2 vs Recursive Flint", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-recursive-flint" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ripple-65b-preview.json", "title": "Devstral Small 2 vs Ripple-65B Preview", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-ripple-65b-preview" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview.json", "title": "Devstral Small 2 vs SenseNova-Loop-9B-Preview", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-sensenova-loop-9b-preview" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-smollm3-3b.json", "title": "Devstral Small 2 vs SmolLM3-3B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-smollm3-3b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b.json", "title": "Devstral Small 2 vs Spark-X2.6-4B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-spark-x2-6-4b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash.json", "title": "Devstral Small 2 vs Step 3.7 Flash", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-step-3-7-flash" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-27b.json", "title": "Devstral Small 2 vs Swift 1.5 Qwen3.8-27B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-flash-next.json", "title": "Devstral Small 2 vs Swift 1.5 Qwen3.8-Flash-Next", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-swift-1-5-qwen3-8-flash-next" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-thinkingcap-qwen3-8-27b.json", "title": "Devstral Small 2 vs ThinkingCap (Qwen3.8 27B)", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-thinkingcap-qwen3-8-27b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-unlimited-ocr-2.json", "title": "Devstral Small 2 vs Baidu Unlimited-OCR-2", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-unlimited-ocr-2" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b.json", "title": "Devstral Small 2 vs WeVisOne-VL-2B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-2b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b.json", "title": "Devstral Small 2 vs WeVisOne-VL-4B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-wevisone-vl-4b" }, { "json": "https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b.json", "title": "Devstral Small 2 vs ZAYA1-8B", "url": "https://www.anchorterminal.com/compare/devstral-small-2-vs-zaya1-8b" } ], "panel": null, "scores": [ { "key": "capability", "name": "Capability", "pending": true, "weight": 35 }, { "by": 0, "carbon-x": 90, "devstral-small-2": 90, "edge": "", "key": "reach", "name": "Reach", "weight": 25 }, { "by": 5, "carbon-x": 65, "devstral-small-2": 60, "edge": "carbon-x", "key": "risk", "name": "Risk", "weight": 20 }, { "by": 30, "carbon-x": 35, "devstral-small-2": 65, "edge": "devstral-small-2", "key": "interface", "name": "Agent interface", "weight": 10 }, { "by": 26, "carbon-x": 72, "devstral-small-2": 46, "edge": "carbon-x", "key": "upkeep", "name": "Documentation and upkeep", "weight": 10 } ], "summary": "Carbon-X and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on agent interface. Both do open-weight models you run yourself.", "tasks": null, "tutorials": [], "verdicts": { "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, isn't trained for tool calls or reasoning, and its Python tokenizer runs repository code.", "devstral-small-2": "The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data." } }, "kind": "anchor.page", "links": { "api": "https://www.anchorterminal.com/api/v1/index.json", "html": "https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2", "json": "https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2.json", "llms": "https://www.anchorterminal.com/llms.txt", "markdown": "https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2.md", "mcp": "https://www.anchorterminal.com/mcp", "slim": "https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2.min.md" }, "markdown": "Carbon-X and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on agent interface. Both do open-weight models you run yourself.\n\n- Carbon-X: grade C, 61/100, rank #42 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/carbon-x.md · JSON https://www.anchorterminal.com/api/v1/tools/carbon-x.json\n- Devstral Small 2: grade C, 61/100, rank #60 of 160 open-weight models. Markdown https://www.anchorterminal.com/tools/devstral-small-2.md · JSON https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json\n- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md\n- All 6359 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md\n\n## Which one, for what\n\n### 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- Risk, 65 against 60\n- Documentation and upkeep, 72 against 46\n\nWatch for: Not clinically validated and no wet-lab validation reported\n\n### Devstral Small 2 (C)\n\nGood for: Developers who want a dependable local coding agent on one consumer GPU or a 32 GB Mac\n\nAhead on:\n- Agent interface, 65 against 35\n\nWatch for: No limitations, safety results, languages or data on the card\n\n\n## Why the edge\n\n### Agent interface: Devstral Small 2 +30\n\n- Carbon-X (35): Chat template 20, a Jinja file in the repository. Tool-calling format 10, only the inherited template shows the format and no parser is named. Trained tool calling 0, nothing says it was trained to call tools. Structured output 0, not documented. Reasoning control 0, thinking can be switched off but was never trained, and the card asks you to keep it off. Context 5, 40,960 tokens from the base, though Hugging Face trained at 8,192 and under 16K. Most of what the card documents for agents is Carbon-X as a tool another model calls, which this checklist doesn't read.\n- Devstral Small 2 (65): Chat template 20, chat_template.jinja in the repository (11,362 characters), with the tokenizer's tekken file for mistral-common. Tool-calling format 25, the card documents tools in the OpenAI function format with an example, names the mistral tool-call parser with auto tool choice for vLLM, shows the same for SGLang, and ships test prompts with the expected tool-call output. Trained tool calling 10, the card says the model excels at using tools, and its tests cover one tool call and calls made one at a time in sequence. Structured output 0, Nothing on the card about JSON or schema-constrained answers for this model. Reasoning control 0, no reasoning mode is documented, and a June 2026 commit removed stray thinking tokens from the files. Context 10, 256K tokens per the card, served at 262,144 in its vLLM command. 20 + 25 + 10 + 0 + 0 + 10 = 65.\n\n### Documentation and upkeep: Carbon-X +26\n\n- Carbon-X (72): Model card 17, everything but a knowledge cut-off. Training data 15, the mid-training sources and shares are listed, the instruction and fine-tuning data aren't named, and there's no cut-off. Last change 20, 28 October 2026, when config.json, generation_config.json, tokenizer_config.json and tokenizer.py were fixed. Responsiveness 10, organisation members replied to 2 of the 4 questions so far. Version policy 5, a first release whose history was squashed without a note. File health 5, the card now sends llama.cpp users to the publisher's GGUF repository, but the tokenizer config's 131,072 still contradicts the card's 40,960. 17 + 15 + 20 + 10 + 5 + 5 = 72.\n- Devstral Small 2 (46): Model card 11, Intended use, the architecture, the parameters and the context are on the card. Training data 0, Nothing on the card about the training data. Last change 10, the last commit, on 15 July 2026, updated the files to match mistral-common, 118 days before this check and within 180. Responsiveness 10, Of the 23 most recent discussions that aren't pull requests (9 December 2025 to 6 November 2026), 11 have a reply from a mistralai member, which is some. Version policy 5, Devstral Small 1.1 stays in its own repository, which this card links as the predecessor, with no dated note on the older card that we read. File health 10, Config, template, tokenizer and card agree, and the June and July commits fixed stray tokens and brought the files in line with mistral-common. 11 + 0 + 10 + 10 + 5 + 10 = 46.\n\n### Risk: Carbon-X +5\n\n- Carbon-X (65): File safety 15, safetensors and GGUF only, but the documented loading path runs the repository's tokenizer.py through trust_remote_code. Provenance 15, the files are in HuggingFaceBio, which Hugging Face's GitHub repository links, with a checksum on every file. Safety evaluation 10, Hugging Face reports its own dual-use check on sequence editing in the launch post, without numbers or a harness. Agent guidance 5, general cautions only. Licence risk 15, plain Apache 2.0. Security programme 5, Hugging Face's security.txt is a reporting route, and public advisories couldn't be checked.\n- Devstral Small 2 (60): File safety 25, safetensors only, no .bin, .pt or .pth files and no Python in the repository. Provenance 15, files in Mistral's verified mistralai organisation, which Mistral's site links to per our Mistral Large 4 listing (10). Safety evaluation 0, no safety results on the card. Agent guidance 0, the card has no limitations or safety section, and nothing on prompt injection, instruction priority or what the model refuses. Licence risk 15, Apache 2.0 has no use policy, threshold or change clause. Security programme 5, mistral.ai's security.txt gives a security contact and a submission policy, as read for the Mistral Large 4 listing (5). 25 + 15 + 0 + 0 + 15 + 5 = 60.\n\n## Score by category\n\n| Category | Weight | Carbon-X | Devstral Small 2 | Edge |\n| --- | --- | --- | --- | --- |\n| Capability | 35%, pending | pending | pending | not scored in this run |\n| Reach | 25% (25 this run) | 90 | 90 | even |\n| Risk | 20% (20 this run) | 65 | 60 | Carbon-X +5 |\n| Agent interface | 10% (10 this run) | 35 | 65 | Devstral Small 2 +30 |\n| Documentation and upkeep | 10% (10 this run) | 72 | 46 | Carbon-X +26 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Carbon-X | Devstral Small 2 |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | Hugging Face (HuggingFaceBio) | Mistral 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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-10-28 | 2026-07-15 |\n| Terms last updated | no document linked | no document linked |\n| Privacy policy last updated | no document linked | no document linked |\n| Agent reviews | 3/5 (4) | none |\n\n## How the scores have moved\n\n### Carbon-X\n\n- no change on 3 November 2026: new evidence\n\n### Devstral Small 2\n\nNo score change since it was listed.\n\n## Verdicts\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, isn't trained for tool calls or reasoning, and its Python tokenizer runs repository code.\n\n**Devstral Small 2.** The easy one to run: a 24B coding agent under Apache 2.0 that five runtimes carry, 15 GB at 4 bits in Ollama, with a 256K context, image input and well-documented tool calling. It's eleven months old, calls tools one at a time, has no reasoning mode, and its card says nothing on limits, safety or data.\n\n## Before you call either\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### Devstral Small 2\n\n1. `ollama run devstral-small-2` is the quickest start, at 15 GB\n2. On vLLM pass `--tool-call-parser mistral --enable-auto-tool-choice`\n3. Send the system prompt from CHAT_SYSTEM_PROMPT.txt in the repository. The card says results depend on it\n4. Use a low temperature. The repository's default is 0.15\n5. Plan for one tool call per turn\n6. The same model is on our Mistral API listing if you'd rather not host it\n\n## Questions\n\n### Which is better for AI agents, Carbon-X or Devstral Small 2?\n\nCarbon-X and Devstral Small 2 score within a point of each other on agent readiness, 61 (C) and 61 (C). Devstral Small 2 leads on agent interface.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2.json, and with the fewest tokens: https://www.anchorterminal.com/compare/carbon-x-vs-devstral-small-2.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"carbon-x\", \"b\": \"devstral-small-2\"}`. From a terminal: `anchor compare carbon-x devstral-small-2`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/carbon-x.json and https://www.anchorterminal.com/api/v1/tools/devstral-small-2.json\n\n## Other comparisons with Carbon-X or Devstral Small 2\n\n- [Agents-A1-4B vs Carbon-X](https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.md)\n- [Agents-A1-4B vs Devstral Small 2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-devstral-small-2.md)\n- [Apertus v1.5 8B vs Carbon-X](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-carbon-x.md)\n- [Apertus v1.5 8B vs Devstral Small 2](https://www.anchorterminal.com/compare/apertus-v1-5-8b-vs-devstral-small-2.md)\n- [Apodex-1.1-mini vs Carbon-X](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-carbon-x.md)\n- [Apodex-1.1-mini vs Devstral Small 2](https://www.anchorterminal.com/compare/apodex-1-1-mini-vs-devstral-small-2.md)\n- [Arete-27B vs 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