{
  "data": {
    "a": {
      "slug": "agents-a1-4b",
      "name": "Agents-A1-4B",
      "vendor": "InternScience (Shanghai AI Laboratory)",
      "vendorUrl": "https://internscience.github.io/Agents-A1/",
      "kind": "weights",
      "category": "open-weights",
      "summary": "Agents-A1-4B is InternScience's open-weight dense vision-language model for long-horizon agent work (search, research, instruction following and tool calling), about 4.5B parameters in the Qwen3.5 architecture with a 262,144-token context, published in BF16, FP8 and GGUF under Apache 2.0.",
      "url": "https://www.anchorterminal.com/tools/agents-a1-4b",
      "markdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.md",
      "slimMarkdownUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.min.md",
      "jsonUrl": "https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json",
      "repo": "https://huggingface.co/InternScience/Agents-A1-4B",
      "license": "Apache-2.0",
      "transports": [],
      "packages": [
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-FP8"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q4_K_M-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-Q8_0-GGUF"
        },
        {
          "registry": "huggingface",
          "name": "InternScience/Agents-A1-4B-F16-GGUF"
        }
      ],
      "auth": "none",
      "authNotes": "The weights download from Hugging Face with no account, token or terms to accept. The main repository and all four of InternScience's build repositories for the 4B are not gated (checked 2026-10-21).",
      "pricing": "free",
      "pricingNotes": "Free to download and use, commercial use included, under the Apache 2.0 licence named in the card's metadata and in the GitHub repository's LICENSE. Running it yourself costs the hardware, about 9.1 GB for the BF16 files or 3.4 GB for InternScience's Q4_K_M GGUF with its vision projector (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/InternScience/Agents-A1-4B",
      "capabilities": [
        "model.open-weights"
      ],
      "tags": [
        "open-weights",
        "model",
        "dense",
        "small",
        "multimodal",
        "reasoning",
        "tool-calling",
        "agentic",
        "derivative",
        "apache-2.0",
        "safetensors",
        "gguf",
        "fp8",
        "self-hosted"
      ],
      "lastRelease": "2026-07-14",
      "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": 27,
        "methodology": "0.6",
        "run": "2026-10-01",
        "scores": {
          "interface": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "pending": [
          "performance",
          "tasks"
        ],
        "assessment": {
          "confidence": "low",
          "date": "2026-10-21"
        },
        "negative": 0,
        "verdict": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
        "bestFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "strengths": [
          "Fits in about 4 GB from InternScience's Q4_K_M GGUF, and its BF16 files are 9.1 GB",
          "Runs in released vLLM, SGLang, Transformers and llama.cpp as a Qwen3.5 model",
          "FP8 and three GGUF precisions from the publisher",
          "Apache 2.0 through the chain, with no gate",
          "Trained for tool calling, several calls a turn, and 262,144 tokens of context",
          "An evaluation framework for its agent benchmarks is published on GitHub"
        ],
        "weaknesses": [
          "The base isn't declared, and the technical report doesn't cover the 4B",
          "No safety results, agent guidance or security contact",
          "Without a system message the template inserts its own prompt naming the model Intern-A1 with a fixed date of 2026-07-14",
          "Tool calling and structured output aren't documented beyond a parser name",
          "InternScience answered one of three discussions, and the history was squashed"
        ],
        "agentNotes": [
          "Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector",
          "With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work",
          "Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14",
          "The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them",
          "Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention",
          "The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding",
          "Validate JSON output yourself, since structured output isn't documented"
        ],
        "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": 70,
          "reach": 90,
          "risk": 55,
          "upkeep": 43
        },
        "provenanceScore": 40,
        "checked": {
          "categories": {
            "interface": "2026-10-21",
            "reach": "2026-10-21",
            "risk": "2026-10-21",
            "upkeep": "2026-10-21"
          },
          "last": "2026-10-21",
          "oldest": "2026-10-21",
          "stale": false,
          "staleAfterDays": 90
        }
      },
      "connect": {
        "install": "llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja -c 32768\n# or, with the card's vLLM command:\nvllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 \\\n  --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "http": "curl http://localhost:8000/v1/chat/completions \\\n  -H 'Content-Type: application/json' \\\n  -d '{\"model\": \"InternScience/Agents-A1-4B\", \"messages\": [{\"role\": \"user\", \"content\": \"Hello!\"}]}'"
      },
      "letme": {
        "capability": "https://letme.dev/model.open-weights",
        "tool": "https://letme.dev/agents-a1-4b"
      },
      "weights": {
        "trainedBy": "InternScience (Shanghai AI Laboratory)",
        "builtOn": "Not declared. Inferred from the files as Qwen/Qwen3.5-4B (Apache 2.0), a fine-tune. config.json is identical to Qwen3.5-4B's and the tensor names match except for the 15 multi-token prediction tensors, which aren't shipped (4,539,265,536 parameters against 4,659,865,088). The technical report says the 35B sibling is fine-tuned from Qwen3.5-35B-A3B",
        "params": "4.5B",
        "context": 262144,
        "goodFor": "Small dense vision-language model trained for long-horizon agent work, search, research, instruction following and tool calling, thinking on by default, runs on almost any machine.",
        "runtimes": [
          "llama.cpp",
          "vLLM",
          "SGLang",
          "Transformers"
        ],
        "builds": [
          {
            "name": "Agents-A1-4B-Q8_0",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q8_0-GGUF",
            "who": "publisher",
            "precision": "q8_0",
            "sizeGB": 5.2,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B-Q4_K_M",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-Q4_K_M-GGUF",
            "who": "publisher",
            "precision": "q4_k_m",
            "sizeGB": 3.4,
            "memoryGB": 8,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "2.7 GB of weights and the 0.7 GB vision projector"
          },
          {
            "name": "Agents-A1-4B",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B",
            "who": "publisher",
            "precision": "bf16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM (ROCm)",
              "SGLang (ROCm)",
              "Transformers"
            ]
          },
          {
            "name": "Agents-A1-4B-FP8",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-FP8",
            "who": "publisher",
            "precision": "fp8",
            "sizeGB": 7.8,
            "memoryGB": 12,
            "format": "safetensors",
            "runsOn": [
              "vLLM",
              "SGLang"
            ]
          },
          {
            "name": "Agents-A1-4B-F16",
            "url": "https://huggingface.co/InternScience/Agents-A1-4B-F16-GGUF",
            "who": "publisher",
            "precision": "f16",
            "sizeGB": 9.1,
            "memoryGB": 12,
            "format": "gguf",
            "runsOn": [
              "llama.cpp (Vulkan)",
              "llama.cpp (ROCm)"
            ],
            "note": "Includes the 0.7 GB vision projector"
          }
        ],
        "run": "vllm serve InternScience/Agents-A1-4B --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder",
        "runBuild": "Agents-A1-4B",
        "checked": "2026-10-21",
        "minMemoryGB": 8
      },
      "family": {
        "lab": "InternScience (Shanghai AI Laboratory)",
        "family": "Agents-A1",
        "url": "https://www.anchorterminal.com/open-weights/#internscience",
        "others": [
          {
            "name": "Agents-A1",
            "url": "https://huggingface.co/InternScience/Agents-A1",
            "params": "35B total, 3B active (MoE; 35,107,181,936 by the Hub's safetensors count)"
          }
        ],
        "olderCount": 6,
        "unrestrictedCopies": 5
      },
      "area": "models",
      "provenance": {
        "legalEntity": "Shanghai Artificial Intelligence Laboratory (the InternAgent team there, per the card's recommended system prompt and the template's default prompt); the GitHub LICENSE leaves the copyright line as the Apache template",
        "domain": "internscience.github.io",
        "domainRegistered": "",
        "endpointOnVendorDomain": null,
        "terms": "",
        "privacy": "",
        "statusPage": "",
        "changelog": "",
        "securityTxt": "none",
        "checked": "2026-10-21",
        "notes": [
          "The card links the GitHub repository InternScience/Agents-A1 and its homepage on internscience.github.io, which link back to the Hugging Face collection. The Hugging Face organisation InternScience holds 16 models.",
          "Nothing is hosted, so the licence stands in for terms of service and no privacy policy is scored.",
          "No changelog for the files. The card's News section dates the 4B release to 14 July 2026, and the Hugging Face history was squashed into one commit that day.",
          "No SECURITY.md in InternScience/Agents-A1 or InternScience/.github, and internscience.github.io and shlab.org.cn return 404 for security.txt.",
          "unchecked: GitHub issues on InternScience/Agents-A1 (the GitHub API wasn't available)."
        ],
        "score": 40
      },
      "pageJsonUrl": "https://www.anchorterminal.com/tools/agents-a1-4b.json",
      "live": {
        "slug": "agents-a1-4b",
        "securityTxt": {
          "url": "https://internscience.github.io/.well-known/security.txt",
          "state": "none",
          "checkedAt": "2026-10-23T15:25:17.440173449Z"
        },
        "updatedAt": "2026-10-23T15:25:17.440173449Z"
      }
    },
    "answer": "Agents-A1-4B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on risk and documentation and upkeep.",
    "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": "InternScience (Shanghai AI Laboratory)",
        "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",
        "b": "Apache-2.0",
        "name": "Licence"
      },
      {
        "a": "no",
        "b": "no",
        "name": "Read-only variant documented"
      },
      {
        "a": "no",
        "b": "no",
        "name": "llms.txt"
      },
      {
        "a": "2026-07-14",
        "b": "2026-10-22",
        "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": "",
        "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": "3/5 (4)",
        "b": "3/5 (4)",
        "name": "Agent reviews"
      }
    ],
    "faq": [
      {
        "answer": "Agents-A1-4B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on risk and documentation and upkeep.",
        "question": "Which is better for AI agents, Agents-A1-4B or Carbon-X?"
      }
    ],
    "goodFor": [
      {
        "aheadOn": [
          "Agent interface, 70 against 35"
        ],
        "also": null,
        "goodFor": "Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence",
        "slug": "agents-a1-4b",
        "watchFor": "The base isn't declared, and the technical report doesn't cover the 4B"
      },
      {
        "aheadOn": [
          "Risk, 65 against 55",
          "Documentation and upkeep, 77 against 43"
        ],
        "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-apodex-1-1-mini.json",
        "title": "Agents-A1-4B vs Apodex-1.1-mini",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.json",
        "title": "Agents-A1-4B vs BigBang-v1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.json",
        "title": "Agents-A1-4B vs Cosmos-Reason2-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.json",
        "title": "Agents-A1-4B vs Darwin-36B-Opus",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.json",
        "title": "Agents-A1-4B vs DeepSeek-V3.2",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash.json",
        "title": "Agents-A1-4B vs DeepSeek V4.1 Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-1-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731.json",
        "title": "Agents-A1-4B vs DeepSeek-V4-Flash-0731",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v4-flash-0731"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it.json",
        "title": "Agents-A1-4B vs DiffusionGemma 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-diffusiongemma-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 12B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-12b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 26B A4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-26b-a4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 31B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-31b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 E2B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e2b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it.json",
        "title": "Agents-A1-4B vs Gemma 4 E4B IT",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gemma-4-e4b-it"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air.json",
        "title": "Agents-A1-4B vs GLM-4.5-Air",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-5-air"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash.json",
        "title": "Agents-A1-4B vs GLM-4.7-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-4-7-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3.json",
        "title": "Agents-A1-4B vs GLM-5.3",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash.json",
        "title": "Agents-A1-4B vs GLM-5.3-Flash",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-glm-5-3-flash"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b.json",
        "title": "Agents-A1-4B vs gpt-oss-120b",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-120b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b.json",
        "title": "Agents-A1-4B vs gpt-oss-20b",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-gpt-oss-20b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b.json",
        "title": "Agents-A1-4B vs Granite 4.2 3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-granite-4-2-3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3.json",
        "title": "Agents-A1-4B vs Hy3",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-hy3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling.json",
        "title": "Agents-A1-4B vs Inkling",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small.json",
        "title": "Agents-A1-4B vs Inkling-Small",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-inkling-small"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview.json",
        "title": "Agents-A1-4B vs InternVL3.5-GPT-OSS-20B-A4B-Preview",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-internvl3-5-gpt-oss-20b-a4b-preview"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3.json",
        "title": "Agents-A1-4B vs Kimi K3",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kimi-k3"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1.json",
        "title": "Agents-A1-4B vs Kolibri-1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-kolibri-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1.json",
        "title": "Agents-A1-4B vs Laguna M.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-m-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1.json",
        "title": "Agents-A1-4B vs Laguna S 2.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-s-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1.json",
        "title": "Agents-A1-4B vs Laguna XS 2.1",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-laguna-xs-2-1"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b.json",
        "title": "Agents-A1-4B vs LFM2.5-2.6B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-lfm2-5-2-6b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny.json",
        "title": "Agents-A1-4B vs Ling-3.0-tiny",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ling-3-0-tiny"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-distill-qwen-9b.json",
        "title": "Agents-A1-4B vs MiMo-V2.6 Distill Qwen 9B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-mimo-v2-6-distill-qwen-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5.json",
        "title": "Agents-A1-4B vs MiniCPM-o 4.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm-o-4-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm5-2b.json",
        "title": "Agents-A1-4B vs MiniCPM5-2B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minicpm5-2b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5.json",
        "title": "Agents-A1-4B vs MiniMax-M2.5",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-5"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7.json",
        "title": "Agents-A1-4B vs MiniMax-M2.7",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-minimax-m2-7"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b.json",
        "title": "Agents-A1-4B vs Muse Glimmer 30B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-muse-glimmer-30b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b.json",
        "title": "Agents-A1-4B vs Nemotron 3.5 Lightning 30B A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-5-lightning-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano 30B A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-30b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano 4B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-4b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning.json",
        "title": "Agents-A1-4B vs Nemotron 3 Nano Omni 30B A3B Reasoning",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-nano-omni-30b-a3b-reasoning"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Super 120B A12B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-super-120b-a12b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b.json",
        "title": "Agents-A1-4B vs Nemotron 3 Ultra 550B A55B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-nemotron-3-ultra-550b-a55b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0.json",
        "title": "Agents-A1-4B vs occamy-1.0",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-occamy-1-0"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think.json",
        "title": "Agents-A1-4B vs Olmo 3 7B Think",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-olmo-3-7b-think"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-35B-A3B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-35b-a3b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-397B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-397b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b.json",
        "title": "Agents-A1-4B vs Ornith-1.5-9B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-ornith-1-5-9b"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-30b-a3b-instruct-2507.json",
        "title": "Agents-A1-4B vs Qwen3-30B-A3B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-30b-a3b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-4b-instruct-2507.json",
        "title": "Agents-A1-4B vs Qwen3-4B-Instruct-2507",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-4b-instruct-2507"
      },
      {
        "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-0-8b.json",
        "title": "Agents-A1-4B vs Qwen3.5-0.8B",
        "url": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-qwen3-5-0-8b"
      },
      {
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      {
        "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
      },
      {
        "agents-a1-4b": 90,
        "by": 0,
        "carbon-x": 90,
        "edge": "",
        "key": "reach",
        "name": "Reach",
        "weight": 25
      },
      {
        "agents-a1-4b": 55,
        "by": 10,
        "carbon-x": 65,
        "edge": "carbon-x",
        "key": "risk",
        "name": "Risk",
        "weight": 20
      },
      {
        "agents-a1-4b": 70,
        "by": 35,
        "carbon-x": 35,
        "edge": "agents-a1-4b",
        "key": "interface",
        "name": "Agent interface",
        "weight": 10
      },
      {
        "agents-a1-4b": 43,
        "by": 34,
        "carbon-x": 77,
        "edge": "carbon-x",
        "key": "upkeep",
        "name": "Documentation and upkeep",
        "weight": 10
      }
    ],
    "summary": "Agents-A1-4B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on risk and documentation and upkeep. Both do open-weight models you run yourself.",
    "tutorials": [],
    "verdicts": {
      "agents-a1-4b": "A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.",
      "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/agents-a1-4b-vs-carbon-x",
    "json": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.json",
    "llms": "https://www.anchorterminal.com/llms.txt",
    "markdown": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.md",
    "mcp": "https://www.anchorterminal.com/mcp",
    "slim": "https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.min.md"
  },
  "markdown": "Agents-A1-4B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on risk and documentation and upkeep. Both do open-weight models you run yourself.\n\n- Agents-A1-4B: grade C, 61/100, rank #35 of 107 open-weight models. Markdown https://www.anchorterminal.com/tools/agents-a1-4b.md · JSON https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.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### Agents-A1-4B (C)\n\nGood for: Local agent loops (search, research, tool calls) on a laptop, a small GPU or a phone-class machine, under a plain licence\n\nAhead on:\n- Agent interface, 70 against 35\n\nWatch for: The base isn't declared, and the technical report doesn't cover the 4B\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 55\n- Documentation and upkeep, 77 against 43\n\nWatch for: Not clinically validated and no wet-lab validation reported\n\n\n## Score by category\n\n| Category | Weight | Agents-A1-4B | Carbon-X | 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) | 55 | 65 | Carbon-X +10 |\n| Agent interface | 10% (10 this run) | 70 | 35 | Agents-A1-4B +35 |\n| Documentation and upkeep | 10% (10 this run) | 43 | 77 | Carbon-X +34 |\n| Negative events | ≤15 | 0 | 0 | |\n| **Total** | | **61 · C** | **61 · C** | |\n\n## Facts side by side\n\n| Fact | Agents-A1-4B | Carbon-X |\n| --- | --- | --- |\n| Kind | Open-weight model | Open-weight model |\n| Vendor | InternScience (Shanghai AI Laboratory) | 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 | Apache-2.0 |\n| Read-only variant documented | no | no |\n| llms.txt | no | no |\n| Last release | 2026-07-14 | 2026-10-22 |\n| Terms last updated | no document linked | 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 | 3/5 (4) | 3/5 (4) |\n\n## Verdicts\n\n**Agents-A1-4B.** A small agent cousin for search, research and tool-calling loops, with vision and 262,144 tokens of context. It runs in about 4 GB from InternScience's Q4_K_M GGUF, in vLLM, SGLang, Transformers or llama.cpp. The main risks are an undeclared base, no safety results or agent guidance, and a template that adds its own dated system prompt when you send none.\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### Agents-A1-4B\n\n1. Quickest start is `llama-server -hf InternScience/Agents-A1-4B-Q4_K_M-GGUF --jinja`, about 3.4 GB with the vision projector\n2. With vLLM use `--reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder`, as on the card, and add `--language-model-only` for text-only work\n3. Always send your own system message, or the template inserts InternScience's default prompt with the date fixed at 2026-07-14\n4. The card recommends temperature 0.85, top_p 0.95, top_k 20, min_p 0 and presence_penalty 1.1, and there is no generation_config.json to set them\n5. Thinking is on by default, send `chat_template_kwargs` with `enable_thinking` false for short tasks, a switch the card doesn't mention\n6. The config declares a multi-token prediction layer that isn't shipped, so don't turn on MTP speculative decoding\n7. Validate JSON output yourself, since structured output isn't documented\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, Agents-A1-4B or Carbon-X?\n\nAgents-A1-4B and Carbon-X score within a point of each other on agent readiness, 61 (C) and 61 (C). Carbon-X leads on risk and documentation and upkeep.\n\n\n## For agents\n\n- This comparison as JSON: https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.json, and with the fewest tokens: https://www.anchorterminal.com/compare/agents-a1-4b-vs-carbon-x.min.md\n- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {\"a\": \"agents-a1-4b\", \"b\": \"carbon-x\"}`. From a terminal: `anchor compare agents-a1-4b carbon-x`\n- Each listing in full: https://www.anchorterminal.com/api/v1/tools/agents-a1-4b.json and https://www.anchorterminal.com/api/v1/tools/carbon-x.json\n\n## Other comparisons with Agents-A1-4B 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 Apodex-1.1-mini](https://www.anchorterminal.com/compare/agents-a1-4b-vs-apodex-1-1-mini.md)\n- [Agents-A1-4B vs BigBang-v1](https://www.anchorterminal.com/compare/agents-a1-4b-vs-bigbang-v1.md)\n- [Agents-A1-4B vs Cosmos-Reason2-2B](https://www.anchorterminal.com/compare/agents-a1-4b-vs-cosmos-reason2-2b.md)\n- [Agents-A1-4B vs Darwin-36B-Opus](https://www.anchorterminal.com/compare/agents-a1-4b-vs-darwin-36b-opus.md)\n- [Agents-A1-4B vs DeepSeek-V3.2](https://www.anchorterminal.com/compare/agents-a1-4b-vs-deepseek-v3-2.md)\n- [Agents-A1-4B vs DeepSeek V4.1 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    "title": "Agents-A1-4B vs Carbon-X for AI agents in 2026: scores and prices",
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